McKinsey
Technology Trends
Outlook 2022
ReportAugust 2022

Introduction
by Michael Chui, Roger Roberts, and Lareina Yee
Technology continues to be a primary catalyst for change in theworld. Technology advances give businesses, governments, andsocial-sector institutions more possibilities to lift their productivity,
invent or reinvent offerings, and contribute to humanity’s wellbeing. And while it remains difficult to predict how technology
trends will play out, executives can plan ahead better by tracking
the development of new technologies, anticipating how
companies might use them, and understanding the factors that
affect innovation and adoption.

This research overview, an accompanying online interactive, anda set of 14 in-depth trend profiles lay out these considerations inmore detail so that executives can better comprehend how theindividual trends are developing and interacting, and what thesedevelopments might mean for their organizations. Looking acrossthe trends, we also arrived at some general observations thatleaders might find instructive.

To that end, we have worked with the internal and external
experts on the McKinsey Technology Council to identify andinterpret 14 of the most significant technology trends unfolding
today (see sidebar “About the McKinsey Technology Council”).
This study builds on the trend research we shared last year,
adding fresh data and deeper analysis to provide a more granularassessment of trends in two thematic groups: Silicon Age, which
encompasses digital and IT technologies, and Engineering
Tomorrow, which encompasses physical technologies indomains such as energy and mobility.

First, several trends that are based on proven and maturetechnologies—namely applied AI, advanced connectivity, futureof bioengineering, and cloud and edge computing—score more
highly on quantitative measures of innovation, interest, andinvestment than trends based on technologies that are still in theearly stages of development. These trends also tend to have
viable applications in more industries, which places them closer
to a state of mainstream adoption than other trends (Exhibit 1).

Our analysis examines such tangible, quantitative factors as
investment, research activity, and news coverage to gauge themomentum of each trend. We also conducted dozens ofinterviews and performed hundreds of hours of research to learn
which industries are apt to benefit most as they absorb thesetechnologies. And, recognizing that trends can speed up, slow
down, or change course, we examined the uncertainties andquestions surrounding each trend.

New dynamics

The next noteworthy group consists of trends that are closelyaligned with sustainability priorities. These trends—which we callfuture of clean energy, future of sustainable consumption, andfuture of mobility—display increasing levels of innovation,interest, and investment. Indeed, of all the 14 trends we studied,the clean-energy and mobility trends attracted the mostinvestment. They have also emerged as significant acrossmultiple industries.

Outside the first two major categories, newer and less-provendigital tools power a set of emerging trends: industrializing
machine learning, immersive-reality technologies, trustarchitectures and digital identity, next-generation softwaredevelopment, and quantum technologies. These trends could
have major benefits for businesses. For example, industrializing
machine learning (ML) can shorten the production time frame for
ML applications by 90 percent. But much work must be done todevelop, refine, and commercialize the technologies that
underpin these trends. As a result, it’s unclear how long it willtake for these trends to be adopted in multiple sectors, let alone
to realize the potential seen by proponents.

About the McKinsey Technology Council
Technology is changing everything in our work and home lives.
We launched the McKinsey Technology Council to help
understand what is coming and how it will affect us all—taking a“look around the corner” toward the futures that technology
change can unlock as well as the tough questions it raises.
We will look at a spectrum of technologies, from computing tobiology, and their applications across all sectors, from mining toentertainment. We look at the science, how it translates into
engineering, and when it will accelerate to impact—at scale, andaround the world. We are doing this with a diverse group of morethan 50 senior leaders in McKinsey, who come from a wide rangeof science and technology backgrounds, and orchestratingexchanges between them and dozens of leading scientists,
entrepreneurs, founders, and researchers who are leading
pathbreaking work in their fields.
—Lareina Yee, senior partner, McKinsey; chair, McKinsey
Technology Council

McKinsey & Company

2Introduction(continued)
Exhibit 1

Applied AI recorded the highest
innovation score of all 14 trends,
while clean energy drew the mostinterest and investment.

Innovation, interest, investment, and adoption, by technology trend, 2021

Innovation,1 score (0 = lower; 1 = higher)

Color = adoption rate score(0 = none; 5 = mainstream)

1.0

0

1

2

3

4

5

Applied AI

0.9

Size = investment, $ billion
Industrializing
machine learning

0.8

Advanced connectivity

0.7
Future ofbioengineering

Trust architectures and digital identity

0.6

250

150

75

20

Immersive-reality
technologies

0.5

Cloud andedge computing

Quantumtechnologies

0.4
0.3

Future of mobility
Future of clean energy

0.2

Future of sustainable consumption

0.1

Future of space technologies

Web3

Next-generation software development

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Interest,2 score (0 = lower; 1 = higher)

0.9

1.0

Note: Innovation and interest scores for the 14
trends are relative to one another. All 14 trends
exhibit high levels of innovation and interest
compared with other topics and are also
attracting significant investments ($2 billion
minimum in 2021).
1The innovation score combines the 0–1 scores forpatent and research, which are relative to thetrends studied. The patent score is based on ameasure of patent filings, and the research score isbased on a measure of research publications.
2The interest score combines the 0–1 scores fornews and searches, which are relative to the trends
studied. The news score is based on a measure ofnews publications, and the searches score is basedon a measure of search engine queries.

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3Introduction (continued)
Despite the uncertainty of these newer trends, it’s apparent thatindustries are broadly exposed to changes resulting fromtechnological innovation and the diffusion of new technologyenabled business practices. When we looked at how frequently
news reports mentioned companies in 20 sectors alongsidedifferent trends, we found that most of these sectors display ameaningful association with five or more trends. The mature andsustainability-focused trends described above exhibit a close
connection with multiple industries. Several nascent trends also
show a moderately close connection with many industries.
Overall, we see that press coverage connects the automotive,
manufacturing, telecommunications, and technology sectors
closely to at least one trend and moderately to several others(Exhibit 2). We plan to explore the effects that technology trends
are having, and could have, on sectors in more depth over the
coming months. (For more about how we performed this work,
please see the sidebar “Research methodology.”)

Looking ahead
We expect changes like these will accelerate and intensify in theyears to come, much as they have during the roughly three
decades since the start of the internet revolution. The changes
will not only alter the competitive landscape but also exert evermore powerful effects on society: reshaping markets, boostingproductivity, spurring economic growth, and enhancing lives
and livelihoods.

When we look at these trends, what impresses us more thananything else is the massive effect that technology will have on
every sector. The next few decades promise to be a time inwhich technologies progress ever-more quickly from science toengineering to impact—at scale, and around the world. We also
expect to see the multiplying effect of “combinatorial innovation,”as different technologies come together in creative ways. For
example, this is happening now as organizations combine
different technologies to create the metaverse and the manylayers that make it up.
Informed by our colleagues and the members of the McKinsey
Technology Council, we intend to publish more about the effects
of technology trends on particular industries, about the efforts ofcompanies to tap into technology trends, and about the ways thatbusiness leaders can manage the implications of technology
trends for their strategies, operations, and talent. We invite you tojoin us in understanding how technology trends evolve andinfluence the world, and we invite you to share your feedback
with us at: techforexecs@mckinsey.com.

About the authors
Michael Chui is a McKinsey Global Institute partner in
McKinsey’s Bay Area office, where Roger Roberts is a partnerand Lareina Yee is a senior partner.
The authors wish to thank the following McKinsey colleagues fortheir contributions to this research: Soumya Banerjee, Arjita
Bhan, Andreas Breiter, Tom Brennan, Ryan Brukardt, Kevin
Buehler, Zina Cole, Jacomo Corbo, Chris Daehnick, Ian De
Bode, Hugo del Campo, Rayan Elsharkawi, Delfino Garcia,
Justin Greis, Liz Grennan, Rob Hamill, Tinashe Handina, Martin
Harrysson, David Harvey, Kersten Heineke, Matt Higginson,
Nicolas Hohn, Alharith Hussein, Mena Issler, Jesse Klempner,
Benedikt Kloss, Charlie Lewis, Martin Linder, Niko Mohr, Timo
Möller, Chandrasekhar Panda, Mark Patel, Robin Riedel, Tanya
Sheptock, Sven Smit, Shivam Srivastava, Bhargs Srivathsan,
Brooke Stokes, Kimberly Te, Kasia Tokarska, Selena WangThomas, Ameera Waterford, Allen Weinberg, Linde Wester,
Olivia White, Perez Yeptho, and Matija Zesko.
They also wish to thank the external members of the McKinsey
Technology Council.

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4Introduction (continued)
Exhibit 2

Most industry sectors display a meaningful association with five or more technology trends.
Minimal relevance

Relevance of trend to industry1
Silicon Age

Advancedconnectivity

High relevanceEngineering Tomorrow

Applied AI

Cloud andedge
computing

Immersivereality
technologies

Industrializing ML

Nextgeneration
softwaredevelopment

Quantumtechnologies

Trustarchitecturesand digital
identity

Web3​Future of bioengineering

Future ofclean energy​Future ofmobility​Future ofspace
technologies​Future ofsustainableconsumption

Aerospace and defense
Agriculture
Automotive and assembly
Aviation, travel, and logistics
Chemicals
Construction and building materials
Consumer packaged goods
Education
Electric power, natural gas, and utilities

Financial services
Healthcare systems and services
Information technology and electronics
Media and entertainment
Metals and mining
Oil and gas
Pharmaceuticals and medical products
Public and social sectors
Real estate
RetailTelecommunications
1Relevance estimated qualitatively by industry experts based on trend’s potential to affect an industry; degree of relevance is scaled at both trend and industry levels.

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5Introduction (continued)
Research methodology
To assess the development of each technology trend, our teamcollected data on five tangible measures of activity: searchengine queries, news publications, patents, research
publications, and investment. For each measure, we used adefined set of data sources to find occurrences of keywords
associated with each of the 14 trends, screened thoseoccurrences for valid mentions of activity, and indexed theresulting numbers of mentions on a 0–1 scoring scale that is
relative to the trends studied. The innovation score combines thepatents and research scores; the interest score combines thenews and search scores. (While we recognize that an interest
score can be inflated by deliberate efforts to stimulate news andsearch activity, we believe that each score fairly reflects theextent of discussion and debate about a given trend.) Investment
measures the flows of funding from the capital markets into
companies linked with the trend. Data sources for the scores
include the following:— Patents. Data on patent filings are sourced from Google
Patents.

— Research. Data on research publications are sourced fromthe Lens (www.lens.org).
— News. Data on news publications are sourced from Factiva.
— Searches. Data on search engine queries are sourced from
Google Trends.

The associations shown on the industry heat map were derived
by reviewing the news reports on each trend for references tospecific companies within each of the 20 industries.
In addition, we updated the selection and definition of trends fromlast year’s study to reflect the evolution of technology trends:— Trends added since last year’s study: industrializing machinelearning, Web3, immersive-reality technologies, future ofmobility, and future of space technologies— Trends not carried over from last year’s study: next-levelprocess automation and virtualization (now considered an
implication of several trends) and next-generation materials(partially represented by other trends)— Trends with adjusted definitions: next-generation softwaredevelopment (partly covered under future of programming in2021), trust architectures and digital identity (partly coveredby trust architecture in 2021), future of sustainableconsumption (disaggregated from future of clean
technologies), and future of clean energy (disaggregated fromfuture of clean technologies)— Trends with minor changes to definition: applied AI, advanced
connectivity (previously called future of connectivity), cloudand edge computing (previously called distributed
infrastructure), quantum technologies (previously called nextgeneration computing), and future of bioengineering(previously called Bio Revolution)— Investment. Data on private-market and public-market capitalraises are sourced from PitchBook.

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6

Trends at a glance

Silicon Age

Engineering Tomorrow

Advanced connectivity

Next-generation software development

Future of bioengineering

Applied AI

Quantum technologies

Future of clean energy

Cloud and edge computing

Trust architectures and digital identity

Future of mobility

Immersive-reality technologies

Web3

Future of space technologies

Industrializing machine learning

Future of sustainable consumption

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7

Trend summaries:
Silicon Age

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8

McKinsey
Technology Trends
Outlook 2022
Advanced connectivity
August 2022

What is the trend about, and what are the mostnoteworthy technologies?

Advanced connectivity

5G/6G cellular, wireless low-power networks, low-Earth-orbit satellites, and other technologies support a host of digital solutions that can help
networks increase geographic coverage, reduce latency, reduce energy consumption, increase data throughput, and increase spectrum
efficiency. This has led to higher-quality network access for consumers and unlocked new use cases for industrial players
Nonterrestrial
wireless

Terrestrial
wireless

Fixed

Connectivity
technology

Optical fiber

LPWA1 networks

Wi-Fi 6

5G/6G cellular

LEO2 satellite
constellations

Description

Most reliable highthroughput, lowlatency connectivity
provided throughphysical wire

Wireless low-power
networks with widearea coverage
supporting a highnumber of connected
devices (eg, NB-IoT,3
LTE-M, LoRa, Sigfox)

Next-generation Wi-Fi(also called “industrial
Wi-Fi”) with higher
throughput, more
controllable quality ofservice, and almost“cellular like” level of
security

Next-generation
cellular technologies
with higher-spectrum
efficiency, providing
high-bandwidth, lowlatency services for
consumers andindustrial verticals

Wide-area coveragewith significantly
reduced latency vsexisting satellite
offerings, due tolower-orbit
constellations

1Low-power wide-area.

2Low-Earth orbit.
3Narrow-bandwidth Internet of Things.

Source: McKinsey analysis

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10Advanced connectivity

Why should leaders pay attention?
As advanced connectivity becomes broadly available, industries will find innovative use cases
Monetization opportunities for technology andtelecom companies

New capabilities for industrial
companies and consumers

Connectivity as anenabler
Enhanced connectivity is anenabler for new and upcoming
technologies

Uplift in B2B
revenues fortelecomcompanies

Exponentiallygrowing need for
network capacity

Rapid increasein number ofconnected
devices

Impact fromindustrial use
cases

Closing the digital
divide andenhancing existing
connections

10–20%

20–25%~51.9 billion$2 trillion~200 million

Overall revenue
increase fromdeveloping 5G-enabled
premium connectivity
and B2B use cases

Annual rate of global
data creation, which
necessitates access tohigher-bandwidth
networks

Total number ofconnected devices
expected in 2025, up
from 43 billion in 2020

Estimated global GDP
impact, driven by
operational enhancements
resulting from advanced
connectivity in just 4 majorindustries

Number of individuals
accessing the internet
for the first time in 2021,
due to new connectivity
technologies•

IoT applications will
increase the need for 5G,
Wi-Fi 6, and LPWAN•

Mobile AR/VR1 and cloud
gaming are examples ofconsumer technologies
dependent on low-latency,
high-throughput wirelessnetworks•

Edge and cloudcomputing technologies,
coupled with advanced
connectivity, will unlock full
benefits of next-gen
computing for consumers
and industrial verticals

1Augmented reality/virtual reality.

Source: DataSphere Forecast, 2020–2025, IDC; “Connected world: An evolution in connectivity beyond the 5G revolution,” McKinsey, Feb 2020; Simon Kemp,“Digital 2022: Global Overview Report,” Datareportal, Jan 26, 2022; Worldwide Global DataSphere IoT Device and Data Forecast, 2021–2025, IDC

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11Advanced connectivity

Why are advanced-connectivity technologies interesting,compared with what already exists?
We compared the current generation of advanced connectivity technologies with their predecessors

Summary

Previous generation

Optical fiber Rapid growth has connected

Next/current generation

Widely used copper had lower
millions of people to high-speed throughput and higher cost for aninternet
operator

Modern optic fiber brought an exponential
increase in throughput, much lower latency, andlower maintenance costs for telecom companies

LPWAN

Standards designed from theground up aim to optimize for
IoT devices

Relatively costly standard cellularconnectivity and low-range tech suchas Wi-Fi/Bluetooth drove most IoTapplications

Purpose-built LPWAN connectivity standards
enable more devices, higher energy efficiency,
extended coverage, and lower connectivity cost

Wi-Fi 6Significantly higher industry
readiness has been enabled by
improvements in security andconnection quality

Wi-Fi 5 brought a marked
Wi-Fi 6 improves upon previous standards in
improvement in indoor wirelessspeed, range, and security, making it moreconnectivity and a major improvement suitable for industrial applicationsin speed over Wi-Fi 4

5G/6G
cellularwireless

Advanced cellular technology
4G cellular technology with moderate 5G/6G offers much higher throughput, device
standards are replacing 4G
speed provided true mobile broadband density, spectrum efficiency, quality of service,
networks, bringing new features access for the first timeand security guarantees with very low latency
and access to new spectrum
for improved user experiences

LEO satellite These satellite constellations
ensure that the most remoteconstellalocations on earth have hightions
quality connectivity

Source: McKinsey analysis

Satellite connectivity was for militaryand industrial applications, with limited
consumer usage for internet orcommunication

LEO satellites aspire to reduce the cost of
hardware and increase accessibility to satelliteinternet connectivity by providing enhanced
proximity to users
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12Advanced connectivity

What disruptions could the trend enable?
Advanced connectivity will catalyze the adoption of technologies to create disruptions in many industries$1.2T–$2.0T
Estimated
global GDP
impact from
disruption in just
4 majorindustries

Bridging thedigital divide

Automotive
and assembly

Connectivity could enable preventive maintenance, improve navigation,
prevent collisions, enable various levels of vehicle autonomy and carpooling
services, and provide personalized infotainment offerings

Healthcare
systems andservices

Low-latency networks and high density of connected devices and sensors make itpossible to monitor patients at home in real time, which could be a major boonin the treatment of chronic diseases

Aerospace anddefenseFrom critical communications through nonterrestrial networks (NTN) to connected
field assets, connectivity expansions such as 5G networks can vastly boost
capabilities and performance for aerospace and defense users

RetailConnectivity allows retailers to manage inventory, improve warehouse
operations, coordinate supply chains, eliminate checkout activities, and add
augmented reality for better product information

With broader 5G, optic fiber, and satellite internet coverage, the digital divide for the next billion internet
users is being bridged

Current users will also see their experiences improve significantly as network speeds and latency improve,enabling use cases previously considered unfeasible
Source: “Connected world: An evolution in connectivity beyond the 5G revolution,” McKinsey, Feb 2020

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13Advanced connectivity

What disruptions could the trend enable? (continued)
Industry affected

Impact from technology trend

Telecommunications

 Introduce new B2C and B2B service offerings, such as improved cellular services for retail customers
and private 5G solutions for enterprise customers

Aviation, travel, andlogistics

 Track and trace products and provide data to help customers optimize supply chains using LPWA
wireless technology

Construction andbuilding materials

 Building Information Modelling (BIM), onsite 3-D printing, and AR applications will all require high-speed,
low-latency, expansive connectivity networks.

Information technology  Demand for smart sensors and IoT1-enabled devices will grow as connectivity improves and cost drops
and electronics
Media andentertainment

 Enable high speed, value-creating entertainment experiences within limited disruptions as new devices(eg, AR/VR devices) enter the market

Metals and mining Expand coverage to enable “smart mining” and digitization/automation practices that will enhance
productivity and safety

Electric power, naturalgas, and utilities

 Implement a smart utility grid with smart meters, sensors, and other cloud devices

Oil and gas

 Leverage advanced connectivity technologies to permit and optimize real-time monitoring of drilling andproduction activities, as well as digital tools and analytics to offshore operators

1Internet of Things.

Source: McKinsey analysis

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14Advanced connectivity

What should leaders consider
when engaging with the trend?
Advanced connectivity will be a huge catalyst for change as
the value chain and ecosystem continue to mature

Benefits
 Enabler: Connectivity is a key enabler of revolutionary
capabilities of digital transformations, driving efficiency
through automation and enabling technologies reliant on
high-quality connectivity such as cloud computing and IoT
 Experience: Average consumers’ experiences are enhancedwith ubiquitous connectivity and significantly higher quality ofservice, enabling individuals to work remotely, accessbandwidth-heavy services, stream higher-quality content, etc
 Global aspirations: Advanced connectivity technologies are
aspiring to have a global footprint, as countries from theglobal south and north stand to benefit significantly in the
future, even if the rate of adoption is uneven

Source: McKinsey analysis

Risks and
uncertainties
 Ecosystem maturity: The ecosystems for evolving
connectivity modalities such as LPWA and LEO are maturing,but so far, few players provide solutions and services inmarkets
 Business viability: Commoditization of connectivity has
meant that only a few telecom companies have been able tomonetize 5G well enough to get a good ROI; the trajectory ofcapital expenditures and maintenance costs will also be
closely watched Availability: Some technologies, such as high-band 5G and
LEO, may be limited by the large capital investments required
to build out networks with competitive coverage andperformance for mainstream use cases

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15Advanced connectivity

What industries could be most affected by the trend?
Connectivity technologies are relatively mature with several examples of industries successfully using them to create
impact in their operations and services
Cellular wireless, optical fiber, and LPWAN technologies are leading catalysts of change in these industries; applications
include ubiquitous connectivity for consumers, industrial automation, and IoT applications such as smart meters
Industry affected

Implications from technology trend

Telecomunications

Telecom companies are using advanced connectivity to introduce new B2C and B2B service offerings, such as
improved cellular services for retail customers and private 5G solutions for enterprise customers

Automotive
and assembly

Innovative automotive players of the future will introduce self-driving, connected vehicles packed with featuresthat depend on high-quality network access even in remote locations
Private 5G, industrial Wi-Fi, and LPWA networks support Industry 4.0 solutions that lift productivity, lower energy
consumption, and reduce costs in factories

Aviation, travel,
and logistics

LPWA wireless technology lets logistics providers track and trace products and provide data to help customers
optimize supply chains, improving overall operational efficiency

Healthcare

Connectivity will be a major boon in the treatment of chronic diseases, as AI-powered diagnostics can be
conducted using data from patients while they are monitored at home using connected medical devices; this will
improve patient access to healthcare while improving the overall digitization of healthcare services

Source: McKinsey analysis

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16Advanced connectivity

Who has successfully created impact with advanced connectivity?
Leading players across industries have already leveraged advanced connectivity to optimize their operations

Automotive
and assembly

Volkswagen has implemented 5G private networks in their factory in Dresden; VW replaced wired
connections between machinery, and now updates finished cars with over-the-air updates and connects
unmanned vehicles with edge-cloud servers
Michelin utilized LPWAN to enable real-time inventory management in 2019; using Sigfox standards,
Michelin was able to gain up to a 10% reduction of the on-sea inventory and a 40% increase in estimatedtime of arrival (ETA) accuracy while reducing inventory ruptures caused by exceptional events like critical
weather conditions

Bosch equipped their first factory with a 5G private network in 2020; the network enables a range ofadvanced use cases such as autonomous transport systems at scale, an automation platform connecting
hundreds of end points, and robots cooperating with human factory workers by adjusting movements in realtime

Telecommunications

Verizon deployed 5G private networks in NFL stadiums to enhance spectators’ experience; these
networks allow fans to access real-time stats and data in AR and to access a feed of up to 7 camera angles
simultaneously via the 5G multiview offering

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17Advanced connectivity

What are some
topics of debate
related to thetrend?
Despite relative maturity,
advanced connectivity
technologies still spark a certain
amount of debate regarding
their implementation andperceived vs realized benefits

1 5G Transition

Can 5G completely replace 4G LTE? What percentage of new
networks will have high-band 5G?


2 Extraterrestrial
networking

Private 5G networks are a proven technology, with many players alreadyreaping their benefits
Other technologies, such as IoT and automated guided vehicles, perform muchbetter when using high-quality networks enabled by private 5G
However, shifting from 4G LTE to private 5G may not be cost-effective for all
players; this would depend on a player's technological aspirations and planned
use cases

How will satellite constellations shift the balance of bandwidthfrom terrestrial connectivity to space-to-Earth connectivity?


A few players are already piloting internet services; there are signs that
consumer devices with LEO connectivity are on the horizon
However, due to high capital expenditures and user costs, the business modeland pricing will be a challenge for scaling up networks, nor can LEO
connectivity fully serve as a substitute for terrestrial networks for all use
cases that rely on cost-efficiency, energy consumption, or overall performance

3 Choosing LPWA Will certain LPWA protocols emerge as the standards forstandards

particular industries? Or will 5G outcompete LPWA networks
for IoT applications?


Depending on availability of traditional LTE networks, a player might choose
between licensed or unlicensed cellular LPWA standards; this choice may
also be critical when dealing with stationary and mobile devices
LPWA standards vary in terms of bandwidth, cost, power consumption, range
and other features; depending on the final use case for the player, somestandards might be more appropriate than others
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Source: Expert interviews; McKinsey analysis

18Advanced connectivity

Additional resources

Knowledge center

McKinsey Center for Advanced Connectivity
Related reading
Connected world: An evolution in connectivity beyond the 5G revolution

Interview: Laying the foundation to accelerate the enterprise IoT journey
Unlocking the value of 5G in the B2C marketplace
Reliably connecting the workforce of the future (which is now)

Breaking through the hype: The real-world benefits of 5G connectivity
How tapping connectivity in oil and gas can fuel higher performance
Agriculture’s connected future: How technology can yield new growth

How our latest work helps leaders get ready for the 5G revolution
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19

McKinsey
Technology Trends
Outlook 2022
Applied AI
August 2022

Applied AI

What is the trend
about?

Applied AI uses intelligent application to solve classification,
prediction, and control problems to automate, add, or augment
real-world business use cases. As AI technologies rapidly push
new frontiers of innovation, business adoption continues to growacross use cases

Selected AI technologies1
Foundational methods of AI

Selected use cases2
Applications of AI at work

Machine learning (ML)

Risk management Computer vision

Service operations optimization

 Natural-language processing (NLP) Deep reinforcement learning

Product and/or service
development

 Knowledge graphs
1Technologies are nonexhaustive and examples that are at the frontier of innovation and used across industries.
2Use cases are nonexhaustive and industry agnostic examples that are leading in business adoption.

Source: McKinsey analysis

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21

Applied AI

Why should leaders pay
attention?
AI adoption has continually increased, enabled by its
financial investment and development for easier access1

Globalexpansion of AI

AI adoption by organizations, 2020–21, %56

All geographies

50
64
61

Developed Asia–Pacific57

Developing markets2

45
51
47

Europe65

India

Rapidly growing 30×
innovation
Relative number of patents filed
in 2021 vs. 2015 (compound
annual growth rate of 76.9%)

47
41
55
51

North America
2020

Share of respondents to a 2021
global survey who said theirorganizations were adopting AI(up 50% from 2020)

Easier and more 94.4%
affordable AI
Improvement in training speed for
implementation AI models since 2018

57

Latin America

2021

56%

0

10% 20 30% 40 50% 60 70%

1For details about easing ML development and integration, see “Industrializing machine learning,” McKinsey

Investment
growth andintensified
efforts$93.5 billion
Private investment in AI-relatedcompanies in 2021, accompanied
by higher concentration of efforts(doubling vs. 2020)

Technology Trends Outlook 2022, McKinsey, August 2022.
2Including China, Middle East, and North Africa.
Source: Daniel Zhang et al., The AI Index 2022 annual report, Stanford University, Mar 2022; “The state of AI in 2021,”
McKinsey, Dec 8, 2021

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22

Applied AI

Why should leaders pay
attention? (continued)
The potential value at stake from AI is$10 to $15 trillion ...

… and leaders adopting AI exhibit stronger
financial performance

Global annual potential, forecast

AI maturity and financial performance

Value at stake, $ trillion

CAGR, %

Service
operations$0.4–0.6

Product
development$0.3

Strategy
and corporate
finance$0.2

Financeand IT$0.2

OthersAnalytics leaders 1

HR
$0.1–
0.2

Risk
$0.5–0.9

2.5×

12.6

Supply chain
management andmanufacturing$3.6–5.6

Marketing andsales$3.3–6.0

Other
operations$0.3–0.4

Source:GlobalOct
Institute,
Source:McKinsey
S&P Global,
2020;2018
McKinsey Analytics Quotient data set

2.1×
5.3

5.1

2.5

5-yearrevenue2

5-year total return toshareholders (TRS)3

1AI leaders are defined as the top quintile of companies that have that taken the McKinsey Analytics Quotient(AQ) assessment.
2Includes revenue through fiscal year 2019; during this time, the 5-year revenue CAGR of the S&P 500 index
was 4.1%.
3Includes TSR through FY 2019; during this time, the 5-year TSR CAGR of the S&P 500 index was 11.7%.

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Applied AI

Why should leaders pay attention? (continued)
Increase by ≤5%

Increase by 6–10%

Increase by >10%

Decrease by <10%

Decrease by ≥20%

Decrease by 10–19%

Revenue increase from AI
adoption, by function

Cost decrease from AI
adoption, by function% of respondents1% of respondents1

Service operations

34

16

15

Manufacturing

38

15 10 63

Human resources

30

Marketing and sales

38

18

15
25

Risk

26

25

Supply chain management27

15

Product and/or service development

30

Strategy and corporate finance

33

Average across all activities

33

65

21

87

37

87

63

20

26

40

86

11 74

21

27

83

15

13

51

27

12 54

32

24

23

13 64

25

12

Across business
functions, AI has alreadymade notable financial
impact

70

35

17

20

41

78

15

27

36

78

22

2 67

10

67

18

24
28

23
30

28

69

Share of respondents whoreport at least 5% of EBIT1
being attributable to AI

67%
Average share of respondents
reporting a revenue increase
via AI adoption

79%

68
33

27%

79

Average share of respondents
reporting a cost decrease via AI
adoption

1Earnings before interest and taxes.

Source: “The state of AI in 2021,” McKinsey, Dec 8, 2021 (for FY 2020)

McKinsey & Company

24Applied AI

What are the most noteworthy technologies?
AI involves machines exhibiting intelligence,1 encompassing various interconnected fields of technology2

Description
Artificial intelligence1

Machine learning

2

3

ComputervisionNaturallanguage
processing

4

Deep
Reinforcement
learning

Example use
case1

ML: Subfield of AI that uses statistical methods to learn
from data

Schedule
optimization

2

Computer vision: Subfield of ML using visual data,such as images, videos, and 3-D signals, extracting
complex information and gaining rich interpretations

Facial recognition as
biometrics

3

NLP: Subfield of ML that involves processing,
generating, and understanding language-based data,such as written text and spoken word

Speech recognition ina virtual voiceassistant

4

Deep reinforcement learning: Combination of deeplearning and reinforcement learning, in which an agent
makes decisions within an uncertain environment using
complex algorithms inspired by brain neural networks

Planning robotic-arm
motion for the
manufacturing line

5

Knowledge graphs: Collection of data points structured
into a network to show complex relationships among
themselves

Social-network
analysis5Knowledgegraphs1AI is nonprogrammatic intelligence exhibited by machines, in which they perform cognitive functions often associated with human minds. Cognitive functions

include all aspects of perceiving, reasoning, learning, and problem solving.
2Technologies are not exhaustive and are examples that are at the frontiers of innovation and cut across industries.
Source: Expert input; McKinsey analysis

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What industries and functions are leadingin the adoption of AI applications?

Applied AI

Industry

AI adoption by industry and function, 2021
% of respondents
Human
resources

Manufacturing

Marketingand sales

Productor service
development

Risk

Service
operations

All industries

9

12

20

23

13

25

9

13

Business, legal,
and professional

11

26

20

15

4

18

6

17

Consumergoods/retail

14

8

28

15

13

26

8

13

Financialservices

2

18

22

17

1

15

4

18

Healthcare
systems

10

4

24

20

32

40

13

8

Pharma andmedical products

9

11

14

29

13

17

12

9

High tech/telecom

12

11

28

45

16

34

10

16

Source: AI index report, McKinsey in collaboration with Stanford University Human-Centered Artificial Intelligence (HAI), Mar 2022

Strategy andcorporate Supply chain
financemanagement

Technology-centricindustries are leading
adoption by businesses
Product and service
development, service
operations, andmarketing and sales are
the business functions
leading adoption of AI

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Applied AI

What industries are most affected by the trend?
A diverse set of stakeholders across all industries are experiencing the impact from applied AI, which can include disruption in value chains, better financial
outcomes, and improved operations

Industry affected1

Example impact from the trend

Information technology
and electronics

Pervasive use across the tech industry and constituent sectors, such as software, hardware, and electronic devices (eg, use of generative AI models tocreate 3-D visuals for software simulations)

Telecommunications

Programming AI models to identify recurring customer concerns and deliver solutions before complaints arise

Pharmaceuticals andmedical products

Exploring relationships across different medical treatments and their combined outcomes for the discovery of new drugs

Aerospace and defense

Aiding the design process (eg, through visual simulations of aircraft performance under different conditions) as well as for security and risk mitigation
processes

Healthcare systems
and services

Enhancing healthcare services through functions like automated pathology recognition and diagnosis decision support

Financial services

Supporting risk management in financial services, eg, detecting credit card fraud to reduce incidents of loss

Retail and consumer
packaged goods

Boosting sales by using ML to analyze huge sets of purchasing data, discern patterns, and give shoppers customized recommendations

Education

Improving personalized learning based on students’ progress

Aviation, travel,
and logistics

Leveraging multimodal fusion, enabled by AI, to combine inputs from various sensors that can help operate autonomous vehicles 2

1Not exhaustive and focused on industries where AI has widespread applications with mature adoption.
2For more, see “Future of mobility,” McKinsey Technology Trends Outlook 2022, McKinsey, Aug 2022.

Source: Expert input; McKinsey analysis

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Applied AI

What industries are most affected by the trend? (continued)
A diverse set of stakeholders across all industries are experiencing implications from applied AI, which can include disruption in value chains, better financial
outcomes, and improved operations

Industry affected1

Example impact from the trend

Agriculture

Enabling process optimization through capabilities like productivity forecasting and driverless tractor applications

Automotive andassembly

Automation of quality testing and manufacturing/assembly processes

Chemicals

Optimizing chemical development and production cycles by recognizing molecules, generating chemical compound formulas, and analyzing chemical
mixtures

Construction andbuilding materials

Using autonomous machinery and robots, computer-vision enhanced safety procedures, and 3-D design optimization software

Electric power, natural
gas and utilities

Optimizing energy production and scheduling, detecting equipment defects early to minimize downtime, and analyzing consumer energy use data to inform
personalized recommendations

Metals and mining

Increasing worksite process efficiencies and aiding the development of digital twins that can generate visualizations and models of remote sites

Oil and gas

Exploration of site through computer vision to assess the value of holdings and use AI/ML to customize drilling plans for geologically-complex areas andforecast demand

Public and social
sectors

Leveraging AI/ML to expedite delivery of key services (eg, use of NLP for tax FAQ handling); additionally, AI/ML can be used as a tool to help in audit
mechanisms to ensure the proper use of resources (eg, predictive tools to help focus tax auditing)

Real estate

Providing personalized customer property recommendations, performing market analyses to help developers manage risk and price volatility, as well as
optimizing ROI

1Nonexhaustive and focused on industries where AI has widespread applications with mature adoption.

Source: Expert input; McKinsey analysis

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Applied AI

What are some use cases for applied AI?
Innovation led

Use case1

Gaining business adoption

Relevant
industries3

Description

Benefits4

Technology2

Function

Generate 3-DmodelsComputer vision; ML

Product
development

Technology;
manufacturing;consumer goods;
retailApply generative techniques that synthesize 3-D
visuals based on singular or multimodal instructions.
Examples: Models for animation, furniture models,and apparel re-creations

Decrease cost withimproved efficiency
through quickly
generated 3-D models

Prioritize
dynamicallychanging tasks

ML; deep reinforcement learning

Service
operations

Any

Optimize changing workflow through multitask
learning to prioritize most relevant tasks.
Examples: Schedule-planning and project
management tools

Decrease cost withimproved productivity

Fuse multimodal sensors

Deep reinforcement
learning; ML; computer
vision. Optional: NLP

Product
development

Transportation;
retail; healthcare

Utilize various sensor inputs to perform tasks.
Examples: Sales checkout for retail; vehicle sensing
for autonomous driving

Decrease cost byautomating systems
requiring sensor input

Recommend
products topurchase

ML

Product
development

Technology; retailfinance; healthcare

Predict and suggest potential products relevant to acustomer’s interests based on prior customer data(individuals or groups).
Examples: Online suggestions of products topurchase; movie recommendations

Improve revenue
through increased
sales via personalizedrecommendations

Detect fraud

ML

Risk
management

Any

Detect fraudulent behaviors to reduce incidents ofloss. Examples: Detection of fraudulent credit card
purchases and account log-in

Reduce losses
through stronger
detection of risky
behaviors

Optional: NLP

Optional: Computer
vision; NLP

Optional: Knowledge
graphs; NLP;
computer vision

Optional: Knowledge
graphs; NLP

1List of use cases is nonexhaustive and highlights those that are at the frontier of innovation and/or rapidly gaining adoption across organizations.
2Technologies typically used to implement the use case. Optional technologies can be applied but depend on the specific task for the use case.

3Relevant industries are nonexhaustive and highlight industries with visible adoption of the use case.
4Nonexhaustive benefits, focusing on major benefits to businesses.

Source: Expert input; McKinsey analysis

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Applied AI

What should a leader consider
when engaging with the trend?

Benefits

Risks and uncertainties

 Cost savings: Up to 90% of survey respondents
cited cost decreases in 2020
 Overall revenue increase: Up to 75% of survey
respondents cited revenue increases in 20201
 New use cases: New use cases will unlock new
business capabilities and opportunities acrossautomation and acceleration
 Increased access to AI and ease of
implementation: New technologies and practices,such as ML operations and software automation,
should make AI more readily available

 High up-front investment in talent and resources:
This creates a high barrier to entry related todeveloping AI and ML workflows for production1
 Cybersecurity and privacy concerns: Data risks
and vulnerabilities are occurring across the technicalAI workflow; 55% of survey respondents cite
cybersecurity as a leading risk in their business in
2021 and are actively taking steps to mitigate it Increasing regulation and compliance: Newlegislation will affect the development of AI’s direction
 AI ethics: Issues include responsibility, equity,
fairness, and explainability

1For more about development of ML systems and tools, see “Industrializing machine learning,” McKinsey

Technology Trends Outlook 2022, McKinsey, Aug 2022.
Source: “The state of AI in 2021,” McKinsey, Dec 8, 2021; Stanford HAI; McKinsey analysis

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Applied AI

What are some
topics of debate
related to thetrend?

1 Trustworthiness What does it mean to apply trustworthy and responsible AI?

2 Explainability

3 Applications

prioritization

4 Other risks

Potential risks and concerns increase as AI use cases expandAccording to the EU Commission High-Level Expert Group on AI, responsible and trustworthy AI can be
defined by abiding laws, incorporating ethics, and implementing technical and social robustness tomitigate potential harm

The commission has developed 7 requirements for AI responsibility and trust: human agency andoversight; societal and environmental well-being; technical robustness and safety; privacy and data
governance; transparency; accountability; and diversity, nondiscrimination, and fairness

When is AI explainability needed?
AI explainability looks at how well we can understand an AI model. Interest in this field is rising as models
are growing increasingly complex and high-risk use cases (eg, disease diagnosis) are being explored

According to Stanford University Human-Centered Artificial Intelligence (HAI), there are three types of AI:
engineers’ explainability (technically explains how the AI model works), causal explainability (explains
why a model input leads to its output), and trust-inducing explainability (information that people need totrust and deploy a model)

Depending on the situation, organizations may use one type of explainability, a combination of types, or
all three types (eg, disease risk evaluation looks at all three types)

How might companies better determine which AI
application provide the most benefit?
Across industries and organizations, each applications of AI will impact different stakeholders in a uniqueway; understanding how AI impacts each stakeholder, the organization, and the ecosystem will be
particularly important for leaders as they decide which AI applications to leverage

Understanding what impact on a use case an AI application will have will be more essential inprioritization decisions for leaders as they build the capabilities to deploy and monitor AI at scale

What are other areas of risk that are relevant?


According to Stanford HAI, leading areas of risk for organizations include cybersecurity, regulatorycompliance, explainability, individual privacy, organizational reputation, and equity and fairnessWhile customers, shareholders, and regulators are calling for increased scrutiny on these topics,
subjective topics (eg, privacy, equity, and fairness) are not high strategic priorities within organizations,as they lack resources and capabilities to fully understand and address these concerns

Source: European Commission High-Level Expert Group on Artificial Intelligence; expert interviews; Stanford HAI; McKinsey analysis

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Applied AI

Additional resources

Knowledge center

QuantumBlack, AI by McKinsey

Related reading
The state of AI in 2021
The AI Index Report: Measuring trends in artificial intelligence
It’s time for businesses to chart a course for reinforcement learning

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McKinsey
Technology Trends
Outlook 2022
Cloud and edge computing
August 2022

Cloud and edge computing

What is the trend about, and what are the mostnoteworthy technologies?

Networks of the future consist of traditional cloud data centers and a variety of computational resources located at networkedge nodes closer to end users to reap the benefits of traditional cloud computing while gaining advantages such as betterdata latency and increased data autonomy
Tomorrow’s networks will consist of devices at many locations computing simultaneously
Edge networks closer to the user

Hybrid cloudDevice edge

Remote edge

Branch edge

Enterprise edge

Telecom/MEC1
edge

Cloud

Computelocation

Smartphone
CameraWearable tech

Connected vehicle
Resource extraction site
Remote filming locations

BranchRetail outlets
Restaurants

Factories
Hospitals
Airports

Network aggregation
pointsNetwork access points

Regional data centers
Co-location data centers
Hyperscale data centers

Use
cases

Remote patientmonitoring

Remote asset
management

Building energy
management

Smart construction andmanufacturing

Smart city infrastructure

Streaming mediadelivery

Real-time fleet tracking

Remote content
rendering

Real-time personalpromotions

Passenger analytics at
airports

Immersive-content
experiences

Proactive equipment
maintenanceWorker safety
monitoring

Air quality monitoring
Media/content delivery

Real-time multiplayer
gaming
Local content exchange

1Multi-access edge computing.

Source: McKinsey analysis

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Cloud and edge computing

Why should leaders pay attention?
Cloud has already effected change across industries and will remain an important tech disruption

Cloud computing is ahuge opportunity for allorganizations

Cloud is no longer public
or private but isincreasingly hybrid

The market for public
cloud continues to growrapidly

Security and access in thecloud remain a top
concern for users$1 trillion~90%~$300 billion~75%

Opportunity in run-rate EBITDA1
across Fortune 500 companies in
2030 through cloud cost optimization
levers and value-oriented businessuse cases

Share of cloud users who have amulti-cloud strategy, with over 80%
having a hybrid mix of private andpublic cloudWorldwide public cloud services
market in 2020, growing at a CAGR
of ~25%, driven by growth in IaaS,
PaaS, and SaaS2

Share of enterprises where cloudsecurity issues are a top concern,with the top challenges being
infrastructure configuration, access,and insecure APIs

1Earnings before interest, taxes, depreciation, and amortization.
2Infrastructure as a service, platform as a service, and software as a service.

Source: “Cloud’s trillion-dollar prize is up for grabs,” McKinsey, Feb 2021; Flexera 2022 state of the cloud report, Flexera, Mar 2022; Worldwide Semiannual
Public Cloud Services Tracker, IDC, May 2021; 2022 cloud security report, Check Point

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Cloud and edge computing

Why should leaders pay attention? (continued)
Edge computing might soon become an operational necessity for many organizations

Data regulation istaking center stage
around the world

Enterprise edge
computing spend
is growing rapidly>60~$250 billion

<20%

26%

Number of countries
reporting data protection
localization requirements in
2021; requirements can befulfilled by adoption of edgestorage and computing

Projected worldwide
spending on edge
computing in 2025, growing
at a CAGR of ~10%

Share of data generated byenterprises that is ultimately
used, due to challenges withlatency and costs of moving
data across environments

Forecast share of servers
shipped in 2024 that will be
deployed at the edge—up
from 20% in 2019

Source: IDC; Omdia

Data volume
Distributed computing
and velocity
is getting moreare growing at an
popular, unlocking realunprecedented pacetime insights

Edge computing
provides flexibility
for organizations
to achieve greater data
sovereignty, greater
autonomy, better
security, and better
latency while unlocking
a variety of use casesthat rely on real-timedata processing

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Cloud and edge computing

What distinguishes edge computing from traditional cloud?
Computational resources

From multi-cloud-based
centralized computation …
High latency

Low latency

Public cloudPrivate cloud

Hyperscaledata centers

Co-locateddata centers… to hybrid computation withedge and cloudEnd users

MEC
Edge location

Private cloudPublic cloudEnd users
Connected
vehicleEdge location
Source: McKinsey analysis

Fully centralizedcore withcomputation andstorage done in
the cloud, leading
to high latency andnetwork congestion

Selected corefunctions moved
toward the edge,
where computing
infrastructure is
deployed to run
latency-intensive
apps

Edge computing will leverage
many types of networking
technology to connect end users
to a decentralized core ofcomputing infrastructure
located closer to the end user
Reduced distance to end users
will shrink data transmission
delays and costs, as well as
provide faster access to asmaller, more relevant set ofdata, which helps companies
comply with data residency
laws
Traditional public cloud willcontinue to play a critical role in
the networks of the future by
performing non-time-sensitive
computing use cases at better
economies of scale at a
distance from the end user
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Cloud and edge computing

What disruptions could the trend enable?
Disruptions from edge computing will have impact on almost all industries and functions
The impact can be described in terms of 2 broad categories:

Network service improvements

New services unlocked by improved quality of experience

Improvements in the performance of the
network and in the quality of experience
for users

B2B services that usually do not benefit theend user directly

Content/CDN1 caching

B2C services that generally benefit theend user directly

Active-device
location tracking

Real-time
personal
promotions

Cloud gaming

Remote desktop
applications

Drones/smart
robotsConnected cars

Cognitive
assistance

Augmented andassisted reality

Mobile backhaul
optimization

Media delivery
optimization
1Content delivery network.

Source: McKinsey analysis

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Cloud and edge computing

What should a leader consider
when engaging with the trend?

Benefits

Risks and uncertainties

Data latency: Edge will enable use cases that had
been challenging to implement effectively, due to datalatency (eg, cloud gaming, smart factories, autonomousvehicles)Business model: Telecom companies and IT service
providers need to figure out partnership, services, andinfrastructure management approaches to unlock costefficiency and avoid major cost increases resulting fromgreater technical complexity

Data residency compliance: Edge will ensurecompliance with local data residency laws necessary toexperience the benefits of both cloud and edge
Data autonomy: Edge will ensure much more granular
control over individual and enterprise data by limiting
reliance on public cloud
Data security: Edge provides a security advantage
over public cloud infrastructure, which is often
susceptible to breaches enabled by the infrastructuresharing model and misconfigurations
Source: McKinsey analysis

Technical challenges: Cloud and edge involve
managing resources over networks that requireinteroperability among a wide variety of devices andsensors to deliver value
Scaling hurdles: The growing number of edge nodes
and devices will be challenging, since edge doesn’t
benefit from the same economies of scale as traditional
cloud computing

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Cloud and edge computing

What industries are most affected by the trend?
Edge computing is quickly approaching maturity; several players have successfully used it to create impact in their operations and services
Synergetic technologies (5G, MEC, SD-WAN,1 and other advancements in networking) are driving adoption for edge to create major impact across manyindustries

Industry affected

Implications of technology trend

Telecommunications

Increase in revenue streams from technologies such as MEC, given the telecom company role as the primary owner of the
networking infrastructure required for distributed computing

Automotive andassembly

Increase in overall efficiency of transportation routes through schedule management, route optimization, etc; reduced relianceof connected/autonomous vehicles on large, distant data centers for access to compute

Electric power, naturalgas, and utilities

Increase in employee safety and efficiency at work sites through real-time tracking and optimization; improvements inequipment efficiency through condition monitoring, real-time data processing, and predictive maintenance

Manufacturing

Improvements in networking and data latency, increasing effectiveness of other Industry 4.0 technologies, leading to betteroverall productivity

Financial services

Sensors and monitors in vehicles, helping insurance players reduce collision and theft

RetailImprovements in advanced analytics use cases (eg, personalization, staff allocation, theft detection)

Healthcare systems
and services

Improvements in most digital use cases (eg, remote diagnostics, active drug tracking, fitness trackers)

1Software-defined wide-area network.

Source: McKinsey analysis

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Cloud and edge computing

What industries are most affected by the trend? (continued)
Edge computing is quickly approaching maturity; several players have successfully used it to create impact in their operations and services
Synergetic technologies (5G, MEC, SD-WAN,1 and other advancements in networking) are driving adoption for edge to create major impact across manyindustries

Industry affected

Implications of technology trend

Aerospace anddefenseBetter networking and data latency, which make automated manufacturing technologies more effective, leading to higher
overall productivity for aerospace players, while flowing data to cloud platforms for efficient analytics

Aviation, travel, andlogistics

More effective demand forecasting, schedule management, and route optimization; well-orchestrated data decentralization canalso provide resilience against data loss

Information technology and electronics

Increase in products and services the industry can offer, spanning cloud and edge environments

Media andentertainment

Maximizing streaming performance and delivery of large volumes of digital content with minimal delays and downtime;enabling flexible server capacity to meet unpredictable consumer demand while maintaining high quality of service

Pharmaceuticals andmedical products

Accelerated drug discovery by enabling better use and storage of AI/ML models; continuous monitoring of equipment that
improves quality, safety, and yield of drugs and formulations.

1Software-defined wide-area network.

Source: McKinsey analysis

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Who has successfully created impact with cloud and edge
computing?
Industry
Telecommunications

Cloud and edge computing

Case example
AT&T has created a new service line providing customers with multi-access edge
computing by partnering with system integrators to connect customers’ enterprisedata centers with LTE and 5G infrastructure

Tesla’s vehicles are powered by homegrown full self-driving (FSD) processors
Automotive
and assembly that act as edge nodes to run machine learning algorithms trained in the cloud tounlock self-driving capabilities

RetailWalmart is planning to use edge computing not only to improve its own Internetof things (IoT), real-time analytics, and customer experiences but also to leverage
its nationwide coverage of supercenters to provide edge computing services tocustomers near these locations

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42

Cloud and edge computing

What are some topics
of debate related tothe trend?

of edge
1 Impact
computing

2 Outlook
Cloud and edge computing willundoubtedly create tremendous
change, but experts are still debating
several key questions

3 Security
vulnerabilities
4 Energy
consumption
5 Meeting demandWill edge truly be more disruptive than cloud?


Edge is extremely flexible and supports a wide array of devices while lying in a business and regulatory sweetspotHowever, traditional cloud enables economies of scale that would be impossible for edge computing networks that
require a high level of interoperability and commonality of standards currently absent in networking

Will hyperscale cloud providers win the edge race?
Public cloud providers have already created services and partnership ecosystems to provide seamless edge andcloud connectivity to their customers



Telecom companies with 5G-enabled MEC can choose to either contend or partner with hyperscalersOEMs and networking and edge service providers will be important as edge networks scale up and customers
require custom solutions

Will the increase in number of storage and processing units lead to security
vulnerabilities?


Keeping sensitive data at edge locations away from centralized servers helps restrict access and minimize risks inthe event of a major attack

However, increasing the number of edge locations increases the attack vectors for malicious actors; if proper
precautions aren’t taken, security vulnerabilities may arise

How will cloud and edge evolve in line with the sustainable IT paradigm?
Data centers are increasingly relying on green IT measures such as sustainably sourced energy and energyefficient cooling systems

Edge computing further reduces overall energy requirements, as less data is transmitted across the network andmore is processed and stored locally

However, as networks expand, the amount of critical infrastructure and number of devices, data centers, and related
energy requirements will continue to increase

How will cloud and edge resources cope with growing demand?


As sensor costs drop and performance increases, will new technological advances in the space be able to meet thegrowing demand for data movement and AI-enabled analytics which rely heavily on the cloud?



Increase in network capacity and performance may increase demand for cloud-based workloads and reduce theneed for specialized edge services

Source: Expert interviews; McKinsey analysis

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Cloud and edge computing

Additional resources

Knowledge center

Cloud Insights

Related reading
New demand, new markets: What edge computing means for hardware companies
Cloud foundations: Ten commandments for faster—and more profitable—cloud migrations
The cloud transformation engine

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McKinsey
Technology Trends
Outlook 2022
Immersive-reality technologies
August 2022

Immersive-reality technologies

What is this trend about?
The immersive-reality space has 4 key components
Technology

Blending technology into
the world …

to see the world differently …

Spatial computing

Mixed reality (MR)

Augmented reality (AR)Virtual reality (VR)Software enables interaction

Graphics linked to reality

Information not linked to reality

100% computer graphics

Description

Interprets physical space andintroduces virtual 3-D objects,
allowing users to interact withenvironments that feature
virtual elements

Modifies the real world through adevice, augmenting or diminishing
the user’s view of the world

Interacts directly with andoverlays onto the dynamic
external reality (eg, AR glasses
with live translation); runs
interactively in 3-D and real time

Replaces the real world (eg, via
headsets) by placing the user inan entirely digital experiencethat uses external cameras/
sensors to render movements in
virtual worlds

Experience

N/A

Merging of reality and MR:
User’s sense of being immersed
is gonePartly immersive: User holds a
sense of presence in the realworld with digital overlay

Fully immersive: User’s visualsensation is controlled by thesystem inside the virtual world… or see a different world

Immersive-reality technologies will have a significant role to play in the metaverse
Source: McKinsey analysis

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What is this trend about? (continued)

Immersive-reality technologies

Most mature immersive-reality solutions fall under a few key themesNot exhaustive

Learning andassessment

Product design anddevelopment

Enhanced situationalawareness

B2C use cases (eg, gaming,
fitness, retail)Learning and training: Hands-on skills andprocedures training—especially useful for
simulating unusual or dangerous edgecases that are difficult to simulate safely inreal life, thus building muscle memory

Product design: Creation of digital twins toenable virtual walk-throughs of a physicalenvironment (eg, construction site) or aphysical product (eg, new space satellite),enabling more efficient product prototyping andtest simulations

Overlay of data visualization enables
more productive assessment of situations

Live events: Gaming, virtual workouts, andother virtual events mimicking real-life
experiences such as concerts, conferences,
sporting events, and fashion shows

Assessment: Use of the same infrastructure(eg, 3-D models, procedure rules) to stress-test
the workforce’s knowledge, skill, and capability
in safety and efficiency and target further
training needs

Development: Simulation of process design,
such as a software engineer “grabbing” blocks
of code overlaid virtually onto factoryequipment to redesign the process flow, thenpushing the equipment back into production

Manufacturing example: Lead engineer
conducts factory operations and maintenanceremotely; VR tech enables workers to conduct
virtual walk-throughs, with visualized data andpop-up decision options for areas requiring
maintenance or repair

Source: McKinsey analysis

Retail example: Store manager observes
store while wearing AR glasses that display
sales data overlaid on sections and products

Virtual showroom: Shopping by virtually
walking through stores, trying on new
products, etc

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Immersive-reality technologies

Why should leaders pay attention?
Overall trends~$1.2 trillion
market size by
2035
Global immersivereality market size
is expected to
grow at a CAGR of~24% until 2035,
facilitated by several
factors, including
increased use ofsmartphones andconnected devices
and rising adoptionof 5G networks

Increasing functionality across industries

Increasing
innovation
2× growth inimmersive-reality
patents from2018 to 2021

Growing
venture capital
investments~$3.9 billion ofventure capital
investments madeinto VR/AR startups in 2021, thesecond-best year
historically (after~$4.4 billion in 2018)as venture capitalinterest recovers
from COVID-19
pandemic
2.2× growth inaverage ticket size
from 2020 to 2021;
1.3× growth from2018

Growing B2B
adoption~66% CAGR inenterprise adoptionof AR through 2026
Need for morecollaboration
platforms (eg,Virbela, ARuVR)
triggered by
COVID-19 pandemic
to enable remote
work

Product andserviceenablement

Development
and training
scalability

Rapid prototyping(eg, driven by earlystage amendments
and powerfulvisualization)shortens time tomarket and reduces
costs drastically

Scalability of
training expands
across all sectors,
particularly for nondesk workers (eg,situational/
emergency training
without risking
users), while
ensuring
standardization inquality of training

New services
unlocked byengaging
consumers in
new ways

Source: “Demand for augmented reality in enterprise and consumer markets to create US $175 billion AR market by 2026,” ABI Research, June 10, 2021; Chris Metinko, “VR/AR investments increase just as
metaverse talk heats up—but that may not be the only reason,” Crunchbase News, Jan 21, 2022; “Extended reality market size, share, trends, by type,” Emergen Research, Feb 2021; McKinsey analysis

Process
improvement
Faster and more
efficient processes
possible via earlywarning-detection
mechanisms, risk
management,
improved quality
assurance, reduced
assembly/
construction efforts,
and reduced
guesswork inmanual labor

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What are the mostnoteworthy technologies?

AR Augmented reality (AR) is a partly immersive experience inwhich users interact directly with a 3-D overlay onto theexternal reality in real time Examples of AR technology devices include AR projections
from phone devices, AR windshield on cars, AR glasses
 Capabilities needed to advance this technology include
common use higher resolution displays (eg, 8K), more precise
eye sensing and tracking technology to reduce lags and errors
in display overlay, etc

VR Virtual reality is a fully immersive digital experience in which
computer-graphics-rendered virtual worlds replace thereal world
 Examples of VR technology devices include headsets for a
fully immersive VR experience Capabilities needed to accelerate this technology include
specialized lower-latency hardware, improved sensors thatallow for full-body virtual tracking, etc

Source: McKinsey analysis

Immersive-reality technologies

Significant advancements are stillrequired for AR/VR and are 8–10
years out
While some capabilities are technicallypossible today in isolation, device
makers need to consider features suchas battery life, weight, and ergonomics,
which adds challenges (eg, 8K displays
exist but are too heavy and expensive forcommon use)
AR requires technology that issignificantly superior to that of VR
Unlocking scalability will require reducing
prices by >50%

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Immersive-reality technologies

What are the most noteworthy technologies? (continued)
A diverse set of sensors and input will be needed, expanding the peripherals market 10−20× from today
Type

Overview

On-body sensors

On-body sensors are tools to track and identify users and the objects around them toaccurately reflect their limb movements and the physical objects around them inthe virtual world (eg, devices that are handheld or concealed in wearables)

Off-body sensors

Off-body sensors allow for more precise recreation of elements of the physical worldin virtual spaces with consumer applications like Nintendo Wii or enterprise applications
such as spatial-mapping hardware

Haptics

Haptic devices (eg, haptic gloves or vests) convey the sense of touch to the user withvibrations to augment virtual experiences

Holography andvolumetric video

Holograms and volumetric video diffract light across multiple wave fronts to display highquality, 3-D representations that can be seen without using a headset (eg, Microsoft
Mesh or Google Project Starline)

Electromyography(EMG)

EMG is a neuro technology that detects and records electrical activity from muscles
to control movement and manipulate objects in virtual spaces and is being used inwearables to augment AR/VR headset devices

Microelectromechanicalsystem (MEMS)

MEMS uses midair ultrasonic waves to allow users to physically feel tactile
experiences without any wearables
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Immersive-reality technologies

What disruptions could the trend enable?
Maturity level

Near term

Medium termLong term/end state

0–3 years

3–10 years

10+ yearsAR

AR exists mostly as a proof
of concept with few
enterprise use cases;
experiences occur within
narrowly definedenvironments (eg,warehouses) and overlay low-fi
visuals over the real world

Consumer AR is introduced as a
low-fi experience while
enterprise AR improves, withaugmented visuals interacting
more fluidly with external inputs
and usability expanding out of
preprogrammed spaces and use
cases

Consumer AR shrinks and use
cases proliferate, with aseamless digital layer acting as anoverlay to the real world; as users
navigate fluidly throughout theirday, external sensors interpret,
interact with, and enhance thephysical environment

VR

Medium-fidelity VR
experiences offer limited
virtual worlds andexperiences; avatars aremanipulated using external
peripherals that limit
immersion

High-fidelity and comfortable VRexperiences are available atscale; avatars are manipulated via
body movements captured by
sensors

Virtual worlds in VR are almost
indistinguishable from real life,and haptics have improved to givea realistic sense of feel across thebody

Source: McKinsey analysis

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Immersive-reality technologies

High expected impact
Insignificant impact

2.7 billion~80%

The world’s
deskless workers, representingof the global workforce, areconcentrated in 8 industries and present huge potential for scaling immersive-reality technology

What industries
could be mostaffected by thetrend?
Overall, industries with a higher
proportion of non-desk workers
are leading in adoption

226M

497M

858M

148M

Agriculture

Retail427M

Manufacturing

265M

122M

Education
Healthcare

Restaurants/
hospitality

Construction/real estate

189M

Transportationand logistics~75% of deskless workers
spend most of their time at work
using some form of tech, with>60% reporting lack ofsatisfaction or feeling the needfor improvement in the tech
they use

Source: “The rise of the deskless workforce,”
Emergence, 2018; McKinsey analysis

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Immersive-reality technologies

What industries could be most affected by the trend? (continued)
Use cases are emerging both horizontally and vertically across industries
Not exhaustive

Automotive and assembly;
aerospace and defense

RetailHealthcare systems
and services

Industry

Education

Exampleuse cases

Learning and development
Remote collaboration
Field-worker assistanceConferences and events

Digital twins/operations
Factory design
Product design
Training
Remote assistance
Safety3-D catalog
Virtual store/digital showrooms
Interactive try-on
Store layout and design
Warehouse optimization

Surgical assistance (AR)Telemedicine (mental health,pain management, etc)
Imaging/pathology
Training
R&D/simulations

Significance

63% of companies that are~100% of design of physical~33% of customers who

metaverse adopters haveundertaken learning anddevelopment for employees in
the metaverse

products/spaces (eg, factories,
warehouses) could be simulated ina synthetic environment

are active on the metaverse
have purchased real-world
items there

Increasing efficacy of
immersive-reality solutions
in treating mental disorders

Source: Nilufar Baghaei et al., “Extended reality for anxiety and depression therapy amidst mental disorders: A systematic review,” ResearchGate, Mar 2022;“Value creation in the metaverse: The real business of the virtual world,” McKinsey, June 2022

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Immersive-reality technologies

What industries could be most affected by the trend? (continued)
Immersive reality could change the way energy and materials industries operate
Not exhaustive

Construction andbuilding materials

Real estateElectric power, naturalgas, and utilities

Aviation, travel,
and logistics

Media andentertainment

Creating immersive,
virtual environments,
giving architects a
better sense of aspace before it
physically exists

Designing interior
spaces along withfloor and furnitureplanning, andproviding virtual
tours of properties toenhance customer
experience

Using AR to view
overlaid visualizationof underground assets
and complex
components for
improved operationalsafety (eg, advising
field technician onwhat actions to take)Diagnosing flow
constraints inwarehouses andmanaging vehicle
fleetsParticipating invirtual events
mimicking real-life
experiences such asconcerts,
conferences,
sporting events, andfashion shows

Source: McKinsey analysis

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Who has successfully created impact withimmersive-reality technologies?

Immersive-reality technologies

Many industries have started to experiment with AR applications
Information
technology andelectronics

Fujitsu uses AR in the sales process to allow customers to see all productcharacteristics

Aerospace anddefenseBoeing leverages AR to improve manufacturing process efficiency and has achieved
a 90% quality increase and 30% speed increase on its pilot projects

Aviation, travel,
and logistics

Japan Airlines is experimenting with Microsoft HoloLens AR as a technical training
tool for its maintenance technicians

Automotive andassembly

Porsche has shortened operational time spent on addressing issues by 40%
through the use of AR headsets to simulate virtual models of problem vehicles

Source: Company websites

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Immersive-reality technologies

What should leaders consider
when engaging with the trend?
Not exhaustive

Benefits

Risks and uncertainties

More efficient product prototyping and test simulations throughthe creation of digital twins to enable virtual walk-throughs of physicalenvironments or new physical products

Pace of hardware improvements to enable miniaturization/weight
reduction, ruggedness; sensor advances for increased precision,
nausea mitigation, etc

Process improvement through early-warning-detection mechanisms,
risk management, improved quality assurance, on-the-job visual
guidance, and more

Cost reductions required to make many more applicationscommercially viable and scalable

Introduction of new products and services by engaging consumers
in new ways and enhancing customer experiences
Increased collaboration by facilitating more engaging virtual-teaminteractions, without the need of being physically present
Scalability of trainings by allowing users to develop hands-on skills,
especially when simulating unusual situations, all while ensuringconsistency in the quality of training provided
Cost savings as a result of effective product development, improved
processes, and scalable, quality-assured trainings

Source: McKinsey analysis

Uncertainty on whether consumer applications will target niche
customer segments or focus broadly on mass markets
End-user devices could take multiple forms, from independent
platforms to peripheral accessories for smartphones or a mix of both
Exposure to complex security vulnerabilities must be mitigated, as
typical AR/VR applications need access to many technologies (eg,smartphones, body sensors, glasses) and may be linked with socialmedia accounts and external applications
Concern about user’s ability to control what data are collected
and how data are processed or shared with third parties (eg, to what
extent will users be surveilled?)
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Immersive-reality technologies

What are some
topics of debate
related to thetrend?

of1 Waysworking

2 Scalability

Will immersive reality shift the new wave of (remote) work?
Many business are reconsidering their remote vs in-person work operating models as
COVID-19 measures are relaxed. As immersive-reality tech boosts collaboration andfacilitates remote operations, will remote work be here to stay?

Will initial ideas continue to stall at proof of concept—orbegin to break through to scale? What will be the triggers forbreakout success?
Significant tech advancements still required for AR/VR are approximately 8–10 years out.
Although some of the required individual capabilities are technically possible today, device
makers still need to produce these features (eg, battery life, weight, ergonomics) inconjunction with each other to improve sensory precision, mitigate security and privacy
concerns, and broaden consumer applications, among other factors.

Not exhaustive

3 Enterprisearchitecture
integration

4 Falseinformation

How will consumer-oriented pioneering platforms integratewith enterprise tech architectures?
Adopting immersive-reality solutions puts a strain on tech architecture. Enterprises will haveto evolve their capabilities to integrate with these new technologies while mitigating privacy
and security concerns; the investments required to do so are unclear.

To what extent can immersive reality facilitate the spread offalse information?
Deepfake technology and mixed reality facilitate misrepresentation (eg, facial-swap features),which could have social implications such as cultural appropriation or the spread of “fakenews” for targeted political influence or any other malicious intent.

5 Virtualcrimes
Source: McKinsey analysis

How can virtual crimes be mitigated and regulated?
Ethical questions are emerging around the potential psychological effects of immersivereality technologies, raising questions around how to deal with different forms of harm,such as virtual violence, bullying, and trespassing.

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Immersive-reality technologies

Additional resources

Related reading

Augmented and virtual reality: The promise and peril of immersive technologies
Product development gets a makeover—with virtual reality
Meet the metaverse: Creating real value in a virtual world
Value creation in the metaverse

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58

McKinsey
Technology Trends
Outlook 2022
Industrializing machine learning
August 2022

Industrializing machine learning

What is the trend about?

Machine learning (ML) workflows are the processes that bring AI and ML into production
for real-world business use
Solutions industrializing ML provide the software and hardware technologies to scale
ML workflows and ease the development and deployment of ML for organizations1

ML workflow

Input

1. Datamanagement

2. Modeldevelopment

Data collection
and processing

Modeltraining

Outputs

3. Modeldeployment
Model
deployment into
production2

Processes

4. Live-model
operations
Model
maintenanceinto production

Future progression
ML production fororganizations is delivered
reliably and at scale, featuring: deployment scaled acrossnetworks
 modular structure with highreuse
 robust monitoring and testing automation of common
processes
Live data

Trainingdata

Trainedmodel

Model inproduction

Model sustained
over time low maintenance cost, lower
risk, higher ROI

1To differentiate applied AI and industrializing ML, this tech trend refers to the systems that put AI (including its subfields such as ML) into production for real-lifebusiness use. Applied AI refers to the real-world business use cases after the technical infrastructure is implemented.
2Once performance standards are met.
Source: McKinsey analysis

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Industrializing machine learning

Why should leaders pay attention?
Solutions for industrializing ML address the technical challenges that prevent organizations from unlocking the full potentialof AI and ML

AI is becoming essential for success …
AI has massive
potential
acrossindustries
AI is disrupting
traditional
businessmodels$10T–$15T
global impact
potential

5… but challenges remain72%
of organizations surveyed have notsuccessfully adopted and scaled1
Challenges include:of 10 largest global
companies did notexist 25 years ago

Difficult transition from pilots to products
Model failure in production

AI leaders havestrongerfinancialperformance

2.5×
greater 5-year total
shareholder returns

Stalling team productivity
Limitations in protection against potential
risks from unknown variables

1McKinsey survey with >1,000 company executives who launched transformations. Organizations get stalled in the pilot phase or during scaling, or they have limited impact despite scale.

Source: “Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018; S&P Capital IQ (Oct 2020); McKinsey analysis

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Industrializing machine learning

Why should leaders pay attention? (continued)
Value levers

Industrializing
ML has potential
for impact on allindustries byreducing hurdles
to develop ML in a
reliable manner1

Maintainperformance
Accelerate time
to value
Reducerisk
Increaseproductivity

Impact potential within 1 year2~60% less value erosion 12 months from model
deployment because of live-model operations~8–10× less time from proof of concept to
production system because of standardization fromdata management to model deployment

100% of production models integrated into
enterprise risk governance and fully auditablebecause of interoperable systems~30–40% resource reduction for ML operations
with improved automation

1Impact also associated with the applied-AI tech trend.
2Based on observations from ML operations deployment in 5 large-scale analytics transformations supported by McKinsey.

Source: Expert input; McKinsey analysis

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Why are the technologies interesting, compared withwhat already exists?

Industrializing machine learningThis emerging tech stack is moving toward simplicity, scalability, and interoperability across the full ML workflow life cycle

Overall ML
workflowDatamanagement
Modeldevelopment

Model
deployment

Live-model
operations

From

To

Outdated tools operated in an inefficient manner
Fragmented technology landscape creating inconsistent
standards and limiting collaboration between teams

Tooling optimizing ML workflows
Technology enabling learning to be shared and collaboration
across the business, including technical and nontechnical employees

Massive manual effort for one-off use with nocontrols over quality and drift, which can damage overall
performance

Automated data management for high-quality data
Data reuse across hundreds of solutions with robust controls

Individualistic, artisan experimentation
Highly manual work that needs refactoring before use

Structured and collaborative development
Solutions assembled from prebuilt components and tooling withhigh degree of automation

No control over what is running in production
Manual and error-prone deployment with poor testingand validation

Controlled production release decisions
Model management providing full transparency on production
solutions
Automated, efficient CI/CD1 for test and validation of all releases

Performance degrading, often undetected, eroding
model value
Unstable solutions down for weeks at a time

End-to-end ML system monitoring with instant alerts enabling rapid
issue resolution

1Continuous integration (CI) and continuous delivery (CD).

Source: Expert input; McKinsey analysis

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Industrializing machine learning

What are the most noteworthy technologies?
Software solutions across the ML workflow

ML workflow
1. Datamanagement
ML subprocesses

Examples oftechnology
solutions

2. Modeldevelopment

3. Modeldeployment

Data discovery and creation

Feature engineering• collection
Data transformation

Model development andoptimization

Model registry andmanagementlabelingvalidation



4. Live-model
operations

Model testing and validation

Monitoring (eg, infrastructure,
data, model, key performanceindicators)

model selection

Model deployment

Model maintenancetraining and tuning

continuous integration

Data versioning

Experimentation and testingcontinuous deployment

Model explainability

Data platforms for data
discovery and extraction

Model libraries

Packaged testing anddeployment platforms

Monitoring of live-model, data
pipelines, and related issues

Version and result tracking

Continuous retraining withperiodic refreshing of data sets

Synthetic data
Automated data labeling
Reproducible data pipelines

Centralized repository forexperimentation
Experiment tracking and modelvisualization tools

continuous improvement

Developer environments(notebooks and IDEs1)

Plug-and-play end-to-end platforms
1Integrated development environments.

Source: Expert input; McKinsey analysis

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64Industrializing machine learning

What are the most noteworthy technologies? (continued)
Hardware solutions for software interconnection and workload optimization

Integrated hardware

Heterogeneous computing

Solutions connecting physical hardware chips andsoftware frameworks

Solutions optimizing computational workloads by allocating different hardware chips
based on specific task1

Vertically integrated Horizontally
hardware systems
integrated hardware

Graphical processing
units (GPUs)

Tensor processing Neuromorphic
units (TPUs)
processing units (NPUs):

Specialized hardware–
software solutions thatare tailored to specific
ML tasks; service-based
examples include
access management
service to GPUs anddata flow as a service

Hardware useful for linearalgebra-based
computations

Early-stage hardware chip
based on brain neural-networkarchitectures with potential
impact for low energy
consumption

Hardware offering adiverse and broad set ofsolutions (eg, simplifying
use of distributed
compute)Specialized hardware
useful for deeplearning computations
AI-specific GPUs are being and able to handle
tailored with faster training complex linear algebra(eg, “tensor” or matrix
speeds, faster transfer
multiplications)
speeds, and stronger
computing power

1With the end approaching for Moore’s law and Dennard’s law, where computational growth grows exponentially, new solutions for optimizing computing arebeing explored. The solutions here are different computer chips that can be leveraged for different tasks.
Source: Expert input; McKinsey analysis

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65Industrializing machine learning

What should leaders consider
when engaging with the trend?

Benefits

Risks and uncertainties

Accelerated AI adoption due to reduced technical barriers andrequirements to implement AI

Upfront investment and resources for setup, whereorganizations need ML-savvy talent and processes to build
capabilities and accelerate the learning curve across theorganization

Improved productivity for technical employees across ML life
cycle
Easier collaboration between technical and nontechnicalexperts on ML model development
Scalability and interoperability leveraging bigger, richer
reused data sets
Reduced cost through faster development and deployment,
standardized processes, improved technical performance
Improved security and privacy along with reduced risk due
to greater standardization and process automation,transparency, and robustness
Source: Expert input; McKinsey analysis

Dependency on 3rd-party vendors leading development of ML
technologies for initial onboarding and continuous support
Fast-developing market, where processes and accountability
for maintaining ML solutions have been poorly defined
Increasing regulation and compliance, where legislation canaffect ML’s development (eg, data governance policies affecting
data management solutions)
Increasing need for responsible and trustworthy ML
systems to address concerns about ethics, privacy, equity andfairness, explainability, accountability, security, and governance
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Industrializing machine learning

Who has successfully created impact by industrializing ML?
Solutions for industrializing ML have generated value for organization as part of large-scale analytics transformations

Context

Technology
used1

Global pharma company

Asian digital bank

North American retailer

Integrating personalized recommendersystem for >50 country and drug combinations

Industrializing and scaling AI use cases tobecome a truly data-driven bank

Applying solutions industrializing ML to address
scaling pain points

Sales uplift of 10–20%

Enabling >150 use cases annually

Scaled up end-to-end analytics transformationfrom only 5 models in production

Software for model development

Software at all stages

Software for model deployment

Software for model deployment

Hardware at all stages

Software for live-model operations

Software for live-model operations

Impactachieved

Time to market

Codecompatibility
check
Tech setup time

4

1

18

3

month

Lead time to
production

monthsmonths

Deployment
processmonths22

Asset reuse

weeks

hours

<10%

>50%

Deployment
lead time

3

15

days

minutes

0%100%

Deployment
time

Enterpriserisk coverage

Manual anderror prone

Automated
and reliable

2

1

weeks

day

>8

15

hours

minutes

1Diverse solutions used according to the technology type and subprocess.

Source: Expert input; McKinsey analysis

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67Industrializing machine learning

What industries could be most affected by the trend?
A diverse set of stakeholders across a range of industries are experiencing implications from the industrialization of ML; impact from thistrend is most expected in industries where accelerating production of ML application yields a competitive advantage

Industry affected

Examples of impact from tech trend

Information technology
and electronics

Designing hardware and software so that devices become more integrated and connected with the natural world (eg, AI models
to interpret voice commands, sensors)

Telecommunications

Deployment to aid business functions from marketing and sales (eg, upselling or cross-selling engines) to customer service (eg,call center volume forecasting and predictions) and network optimizations

Pharmaceuticals andmedical products

Supporting the development of new drugs, (eg, through exploring relationships between molecules and chemical compounds)
and enabling support functions (eg, manufacturing, supply chain optimization) for various medical treatments

Aerospace and defense

Augmenting design and manufacturing processes through optimizations from AI/ML models (eg, AI models to aid in the 3-D
simulations for aircraft design, supply chain optimization for manufacturing, security risk management)

Automotive andassembly

Using AI/ML to enhance design and manufacturing processes such as predictive maintenance, automated quality testing, anddemand forecasting and to provide customer service features such as navigation

Financial services

Supporting key services in the financial sector including risk management and assisting in many other processes—eg, bydetecting credit card fraud

Media andentertainment

Provision of high levels of personalization in media and entertainment experiences (eg, tailored recommendations)

McKinsey & Company

68Industrializing machine learning

What are some
topics of debate
related to thetrend?

1 Impact of ML

How can solutions that industrialize ML change organizations,
their operating models, and their engineering roles?

industrialization
on organizations The technologies are part of a wider effort in scaling ML operations toward a modular,
automated, monitored life cycle approach to AI/ML—potential impact:
and technical reduce resourcing needs and production times
talent ‘democratize’ (ie, use nonspecialized) data scientists working horizontally on mostvalue-add tasks, assisted by standardized tooling
reduce technical barriers and enable closer collaboration with nontechnical SMEs,1
offering greater visibility and expanding potential use cases

should organizations select which solutions that
2 Selection criteria How
industrialize ML are most relevant to their needs and strategy?

for solutionthat industrialize ML

3 Accountability
for AI and ML

As the solutions that industrialize ML grow, how can roles ofaccountability be defined to ensure trustworthy andresponsible AI/ML?


1Small and medium-size enterprises.

Industry-specific use cases influence ML workflows, varying drastically across risk
levels, required data governance, SME relevance, and model complexity
Potential long-term dependency on 3rd-party vendors means organizations may have longterm partnerships rather than solution providers, often making trade-offs between bestof-breed vs end-to-end/cloud-native ML platforms and open-source vs supported
enterprise software

Processes and accountability for maintaining ML solutions are currently poorly defined,with lack of clarity on roles of responsibility across the ML workflow
As with applied AI, organizations will have to make trade-offs on which aspects oftrustworthy AI are priorities for their business, which will have downstream impact on theirdecisions and interactions with solutions industrializing ML
McKinsey & Company

Source: Expert input; McKinsey analysis

69

Industrializing machine learning

Knowledge center
QuantumBlack, AI by McKinsey

Additional
resources

Related reading
Scaling AI like a tech native: The CEO’s role

Operationalizing machine learning in processes
Transforming advanced manufacturing through Industry 4.0
Derisking machine learning and artificial intelligence

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McKinsey
Technology Trends
Outlook 2022
Next-generation
software development
August 2022

Next-generation software development

What is this trend about?
The next generation of software development involves tooling that aids in the development of software applications, improving
processes and software quality across each stage of the software development life cycle, including AI-enabled development andtesting, as well as low-code/no-code tools

Technology or tool kit

Planning
Architecture
and analysis
designLife cycle stages affected

Development
and coding

Testing

Deployment andmaintenanceLow-code/no-code platforms
Graphical user interface (GUI)–based platforms
for nondevelopers to use in building apps
Infrastructure-as-code
Configuration templates to provision infrastructure
for applications using Terraform, Ansible, etc
Microservices and APIs
Self-contained modular pieces of code that canbe assembled into larger applications
AI “pair programmer”
Code recommendations based on context from
input code or natural language
AI-based testing
Automated unit and performance testing to
reduce developer time spent on testing
Automated code review
Automated software checks of source code
through AI or predefined rules
Source: McKinsey analysis

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Next-generation software development

Why should leaders pay attention?
Developers will focus more on the capabilities their applications would enable than on the details of building the apps

Growth in market and adoption

Augmented capabilities

Greater
adoption

Growth inmarket size

Fasterdevelopment

~70%

~21%

Up to

Share of new
application
developmentthat will leverage
low-code/nocode by 2025
(vs <25% in
2020)

Growth in size ofmarket forsoftwaredevelopment,
CAGR for2021–26,
reaching~$600 million
by 2026~90%

Reduction indevelopment
time due to lowcode/no-code
applications

Fasterdeployment

Faster code
testing

Reducedresolution
time

~2×

~37%

<1 dayIncrease in
deployment
speed reportedby ~60% of
developers,
driven bypractices suchas continuous
integration andcontinuous
delivery (CI/CD)

Share ofrespondents
saying they use
AI and ML totest better andfasterTime to resolve
configuration
issues reportedby ~75% ofcompanies with
automatedinfrastructureas-code security
testing

Source: Gartner; “Software development AI market overview,” IndustryARC; Intelligent process automation and the emergence of digital automation platforms, Red Hat, February 2018; A maturing
DevSecOps landscape, GitLab, May 4, 2021; Forrester Analytics Business Technographics Developer Survey, 2020; Infrastructure as code security insights, Snyk, 2021

As repetitive tasks
become automatedand resource
requirements tobuild digitalproducts decrease,developers will
focus on adding
new, innovative
features
Many methods,
including CI/CD
and infrastructureas-code, will
benefit from cloud
migration andaccelerate thistransition

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Why are the technologies
interesting, compared withwhat already exists?
From manual, time-intensive
work flows and techniques …

Next-generation software development… to automated, simplified, andfaster development techniques

Reliance on dedicated developers toparticipate in every step of the development
cycle, from planning to maintenance,
contributes to higher costs and talent gaps

Greater participation of ‘citizen developers’(business users who have insignificant
technical experience but are able to build
business applications without involving
technical teams) facilitates quick developmentof solutions more aligned to business needs

Manual infrastructure configuration andmonitoring involve high mean time to restore(MTTR), security risks, and task repetition,
leading to inefficient resource utilization

Automated configuration and monitoring
through infrastructure-as-code reduces
downtime and increases overall productivity
and security

Developers working together to write codeas ‘pair programmers’ on the sameworkstation expend a high number of personhours to build the program

AI-based pair programmers are making solo
developers more efficient and improving qualityof codeDevelopment cycles are slow because teams
experience interruptions, code has more
defects, and time is spent on manual tasks

Fully automated CI/CD pipelines enable
lower disruption, higher code quality, anddrastically shorter development cycles

Source: McKinsey analysis

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Next-generation software development

What are the most noteworthy technologies?
Across the entire software development life cycle, technologies are already improving developer velocity
Not exhaustive

1. Planning andanalysis

2. Architecture
design3. Development and coding

4. Testing

5. Deployment
and maintenance

Infrastructure-as-code

AI pair programmer

Microservices and APIs

AI-based testing

Automated code review

Higher scalability as
configuration templates areused to set up newenvironments rapidly andconsistently

Rapid development as
developers code fasterwith reduced friction to aid“developer flow”

Faster development as
microservices and APIs
serve as building blocks
companies use toeffortlessly add functionality
to software, unlocking
significant business agility

Faster, more efficienttesting via smarter
automation

Faster, more efficient code
reviews with greater
coverage as a result of
eliminating manual inputs
and removing human biases

Reduced risks asconfiguration templateseliminate possibility ofhuman error

Low-code/
no-codeplatforms
Source: Expert interviews; McKinsey analysis

Enabler of automatictranslations and lowcode/no-code tools

New revenue streams as
APIs can be provided tocustomers in an as-aservice model andexternally to otherbusinesses for integration

Improved test coverage
and effectiveness, with
automated script writing
expected to exceed 80% of
test coverage within the next
decade and to auto-generate
insights for improvement

Standardized tools and processes that scale tech innovation via reuse of components
Acceleration application development through plug-and-play software components
Stronger business alignment as a result of bringing technical requirements closer to business units
Automated deployment of models into production applications
Augmented monitoring and maintenance (eg, model retraining) to minimize performance degradation
McKinsey & Company

75

Next-generation software development

High industry relevance

What industries
could be mostaffected?
Beyond the information
technology andelectronics industry,these technologies will
have an impact on
software development
across all industries byreducing digitization
challenges
Many industries are
already reaping thebenefits of low-code/nocode platforms, given theircommon qualities andrequirements
Source: Expert interviews; McKinsey analysis

Industry

ExamplesFinancial services

Evolving business rules for processes suchas onboarding, know your customer (KYC),
and customer due diligence can becontinuously handled by business analysts for
efficiency

Healthcare systems andservices; pharmaceuticals
and medical productsCase management processes for handlingcustomer data, tailored and specificprocesses for high-risk patients, development
and testing of new drugs, etc, can becustomized by healthcare providers

Medium industry relevance

Common industry qualities
Compliancerequires a wide
variety offrameworks,
protocols, andregulations,which typicallyvary by region,
licenseagreement, etc

Heavily
processbasedindustries
Significant
customization
requirements
Rapid pace ofinnovation tomeet evolving
customerneeds

Manufacturing processes in Production floor management allows
automotive and assembly
industrial engineers to optimize operations,and aerospace and defense reduce training expenses for new developers,
reduce production floor failures, andstandardize safety/handover protocols
RetailConsumer-friendly front-end applications canbe rapidly created and tailored to the needs ofan organization and its customers

McKinsey & Company

76Next-generation software development

Who has successfully created impact with next-generation
software development?
Leading players across industries have already leveraged advanced DevOps tools to optimize their SDLC1
Stage of SDLC1

Technology

ExampleArchitecture
designInfrastructureas-code

Decathlon used infrastructure-as-code to automate infrastructure deployment, reducing
deployment time from weeks to 30 minutes, allowing IT teams to focus on more complex tasks

Development
and coding

Automated
CI/CD

Capital One leverages microservices and automated CI/CD to increase delivery speed
without compromising quality through reusable building blocks and generation of templated
pipelines

Testing

AI-based test
automation

Goldman Sachs uses the AI-based tool Diffblue Cover to generate unit tests for legacy
software, leading to a 180× increase in the speed of writing tests for a core back-end application

Deployment andmaintenanceAI-based code
reviews

Atlassian uses AI-based tools by Amazon Web Services to improve code performance byidentifying code paths that demonstrate poor CPU2 utilization or latency

1Software development life cycle.
2Central processing unit.

Source: Company websites; expert interviews

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Next-generation software development

What uncertainties must be resolved for the trend to achieve scale?

Low-code/nocode platforms

Infrastructureas-code

AI “pairprogrammer”

Microservices
and APIs

AI-based
testing

Automated
code review

Modest amount ofcustomization ispossible, comparedwith traditional
programming
languages

Comprehensive
monitoring andversion control isrequired to ensure
errors do not spread
across servers

Generated code
may be unusable
or inefficient andmay havesecurity
vulnerabilities

Customizing
APIs is difficult
without significant
time and effort

Autonomous
tools aretypically
specialized (eg,by programming
language, test
type)

Tools do not
identify all
defects andinefficiencies incode

Monitoring anddebugging
applications isdifficult, especiallywhen they areintegrated acrossseveral low-code/nocode platforms

Fragmented
vendors could
disrupt integrated
applications, given
uncoordinatedchanges andupgradesCoders can besteered in thewrong direction if
tools are notregularly updatedwith the standards
or trained onclean, fast code

Source: Expert interviews; McKinsey analysis

APIs introduce
security risks byadding anotherattack layer thatcan be exploited

Companies over-rely on
automated testing/reviews when
this tech scales; humans do notconsistently check for errors in test
and review outcomes
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78

Next-generation software development

1
What are some topics ofdebate related to thetrend?

To what extent can no-code tech reduce the need for
traditional software developers?
While low-code/no-code platforms help teams rapidly prototype or enablecitizen developers to take over some of the work developers do, they are still
not flexible enough to reduce development work at every stage of the softwaredevelopment life cycle (eg, when legacy systems require upgrades)

2

From a cultural standpoint, will teams—both
developers and non-developers—embrace or resist
next-generation technologies?
Automation technologies reduce time spent on development, which raises
concerns for employees whose workflows are highly automatable; developers,
testers, and analysts may be reluctant or eager to switch to new technologies,
depending on job security, technical comfort, etc

3What intellectual-property issues might affect code
written by an AI application?
As companies leverage AI generation tools, there is a concern aroundownership: Will the company that developed the application own it, or will it
belong to the AI-enabled code generation tool provider?

4

To what extent will business units take responsibility
for the ‘health’ of applications?
As next-generation software brings development capabilities to “citizen
developers” embedded in business units, questions about organizational
structure and responsibilities emerge—eg, as business users create
applications, who is responsible for maintaining them?

Source: Expert interviews; McKinsey analysis

McKinsey
McKinsey && Company
Company

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Next-generation software development

Additional resources

Related reading

Developer Velocity: How software excellence fuels business performance
Security as code: The best (and maybe only) path to securing cloud applications and systems
Developer Velocity at work: Key lessons from industry digital leaders

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McKinsey
Technology Trends
Outlook 2022
Quantum technologies
August 2022

Quantum technologies

What are the mostnoteworthy technologies?
Quantum technology has beenaround for a long time …

… but a few emerging technologies merit our focus now
These futuristic technologies aspire to change our computational, networking,and sensory infrastructure in the coming decades, unlocking use cases andcapabilities previously unimaginable

Lasers work
using thequantummechanical effectknown asstimulated
emission

Source: McKinsey analysis

Magnetic
resonance
imaging uses thequantum
phenomenon
known asmagnetic
resonance

Quantum computing
uses quantum propertiesof particles to process
information at a muchhigher rate than aclassical computer can

For some computational
problems, quantum
technology could makecomputation exponentiallyfaster than with classicalcomputers

Quantum
communication is thetransfer of encoded
quantum information
between distantlocations based on an
optical fiber network or
satellites
A central feature is thequantum-secureconnection throughquantum encryption

Quantum sensing
could provide
measurements ofvarious physicalquantities at asensitivity that isorders of magnitude
higher than classicalsensors can achieve
Applications include
radar, microscopy,
and magnetometers
McKinsey & Company

82Quantum technologies

Why should leaders pay attention?
The quantum age is just over the horizon …

$300 billion−$700 billion
Conservative estimate of
the value at stake ofquantum use cases inindustries such as
automotive, chemicals,
finance, andpharmaceuticals

Source: Worldwide quantum computing forecast,
2021–25, IDC, Nov 2021;
Quantum computing: An emerging ecosystem andindustry use cases, McKinsey, Dec 2021;
Frank Arute et al., “Quantum supremacy using a
programmable superconducting processor,” Nature,
2019, Volume 574, pp. 505–10 (2019)

Rapid acceleration
in investments

Technology
approaching
maturity

Market expected to
grow rapidly$1.7 billion<10 years~$10 billion

Investment inquantum start-ups in
2021, more than double
the amount in 2020

Estimated timeline tounlock several of thecurrently identified use
cases as the technology
matures and scales

Projected market sizeof quantum-computing
services in 2027, up
from ~$400 million in
2020, growing at 50%
per year

However, quantum technology is still very much in its nascent phase, and it would be difficult
to predict when or if this technology will mature and scale up
McKinsey & Company

83Quantum technologies

Why is quantum computing interesting compared with whatalready exists?
Classical computer
Information
storage

Information is stored in bits, where each bit can beeither 0 or 1

Quantum computer
Bit
0

The information is stored in qubits (quantum bits),
where each qubit represents any possiblecombination of 0 or 1 with each other

1

Qubit

0

1

Computation

Results can be read directly from the bit string of 0s
and 1sResults of the computation are retrieved via statisticalanalysis of repeated quantum measurements

Performance

The performance scales linearly with the number of bits

The performance may scale exponentially with thenumber of qubits for certain problems

Pros and
cons

Source: McKinsey analysis

Good for general-purpose computing

Cannot perform general-purpose computing

Mature technology with low error rates

Nascent technology with high error rates

Robust and cost-effective

Currently requires expensive specialized
infrastructure

Cannot scale well for certain problems

Good at solving certain specific problems
McKinsey & Company

84Quantum technologies

Why is quantum communications interesting compared with whatalready exists? (continued)
Quantum communications enables major applications

The ambition ofquantum
communications is tooffer transfer of
encoded quantum
information between
distant locations
through a universal
quantumcommunication network
Source: Expert interviews; McKinsey analysis

Enhanced security based on
quantum mechanics

Enhanced quantumcomputing power

Secure quantum communications
guarantees full security of informationtransfer in the presence of a quantum
computer

Quantum communications enables
 distributed quantum processing,where 2 or more quantum computers
are connected to enhance computing
power blind quantum computing, where aremote quantum computer isaccessed such that it learns nothing
about the performed operation

It will enable the following: verified randomness for generating
shared keys
 quantum encryption
 tamperproof communications

McKinsey & Company

85Quantum technologies

What disruptions could quantum computing enable?
Quantum computing
could unleash
significant businessvalue acrossindustries, butachieving this will
require extensive
research anddevelopment

Applications
Most known use cases can fit into 4 archetypes:
Quantum
simulation

Quantum linear
algebraSimulation of quantum-mechanical systems such as molecules, chemical
reactions, or electrons to enable use cases such as lead identification in
drug discovery or simulation of proteins in pharmaceuticals andagriculture
Algorithms that can provide an exponential speedup over conventionalalgorithms and be used in tasks such as providing financial advice,autonomous driving, automated trading, and predictive maintenance

Quantum
optimization

Real-time optimization by compressing computation times from hours toseconds, enabling use cases such as generative design, traffic
management, and portfolio optimization in almost every industry; current
quantum-optimization approaches yield only a quadratic speedup, which
when accounting for the overhead of using quantum versus traditional
computing does not yet yield a significant performance improvement

Quantumfactorization

The earliest identified application of quantum computing, efficient quantum
factorization is readily applicable to breaking RSA encryption, the basis of
most of today’s secure data-transfer protocols; achieving this will require
managing many more high-quality qubits than have currently been done

Source: Quantum computing: An emerging ecosystem and industry use cases, McKinsey, Dec 2021

McKinsey & Company

86Quantum technologies

What disruptions could quantum communications enable?
Quantum
communications
enables securecommunication ofquantum
information acrossdistant locations

Source: Expert interviews; McKinsey analysis

Applications
Quantum-enhanced(classical) cryptography

Quantum cryptography

Quantum internet

Quantum random-number
generators (QRNG)
Enhanced security ofclassical cryptography
protocols—eg, cryptography,
personal identification
numbers, lotteries, numericalsimulations

Quantum encryption
protocols
Secure communication
enabled by a quantumgenerated confidential key
shared between distantpartners—eg, quantum key
distribution (QKD), BB84

Quantum communication
infrastructure
Quantum-informationexchange across continental
or global distances to enable:•

long-distance securecommunication•

distributed quantum
computing
McKinsey & Company

87Quantum technologies

Who has successfully created impact with quantum technologies?
Recently, many public and private entities have made announcements regarding their early applications of quantum technologies

Case example
Quantum
communications

The University of Science and Technology of China, in collaboration with industry partners, hasdeployed an integrated communication network with QKD spanning more than 4,600 kilometers
Toshiba and the University of Cambridge have deployed quantum-encryption protocols through existing
citywide fibers with high-bandwidth data traffic

Quantum
computingCompanies such as Alibaba, Amazon, Google, and IBM have already launched commercial quantumcomputing cloud services with varying levels of customer adoption and technical maturity

BMW has used quantum machine learning for autonomous vehicles by using it to train highly accurate
models with massive amounts of data and has used quantum computing for car fleet routing optimization
Pfizer is applying quantum computing to predict the behavior of electrons in a molecule to determine its
3-D structure in order to understand more about new molecules that are potential drug candidates
Source: Company websites; expert interviews; McKinsey analysis

McKinsey & Company

88Quantum technologies

What industries could be most affected by the trend ?
Quantum computing is still in the nascent stage, with few, isolated examples of players adopting it for solving
optimization challenges

Quantum communications is relatively more mature, with several players globally establishing networks with QKD andreaping the benefits of this technology

Industries affected

Example impact of tech trend

Information technology
and electronics

Improving network security through QKD technology; providing capabilities orforming partnerships to offer quantum-computing services

Metals and mining; oiland gasIncreasing the efficiency of companies’ exploration and extraction activities

Aerospace and defense

Using quantum technologies to enable tamperproof communication systems anddevelop augmented navigation systems

Chemicals;
pharmaceuticals andmedical products

Leveraging quantum computers for molecular simulations involved in creating newmaterials and identifying potential drugs

Source: McKinsey analysis

McKinsey & Company

89Quantum technologies

What should leaders consider when engaging with the trend?
Initially, incremental value from quantum technologies will be created through hybrid solutions withhigh-performance computing

Before ‘impossible tasks’ become solvable, we expect
incremental value creation through hybrid solutions withconventional supercomputers:

Meanwhile, researchers work on improving quantum
computers with 2 major goals:• Solving business-relevant optimization problems in certain niches
would be 10% faster than previously possible• Improved processors: Create stand-alone, fully capable
quantum processors with a high count of quality qubits in orderto achieve “quantum advantage” over classical computers• Simulating the properties of small molecules with 5% higher
accuracy can enable the creation of new products, such as
simulating surfactants to develop a better carpet cleaner• Better data sampling to train AI may take longer, but the trained
algorithm gives 20% better answers

Source: Expert interviews, McKinsey analysis• Market-ready tech stack: Overcome engineering challenges
and build a technology stack of hardware and software in orderto make state-of-the-art quantum computers market ready

McKinsey & Company

90Quantum technologies

What should leaders consider whenengaging with the trend?
(continued)
Advantages

Uncertainties and risks

Early-mover advantage: Organizations can begin investing intalent and infrastructure, establish or join quantum-technology
ecosystems early, and prepare for upcoming disruption byidentifying relevant use cases for their businesses while thetechnology matures through fundamental scientific researchTechnical challenges: This includes the ability to manage asufficient quantity and quality of qubits over enough time toderive meaningful computational results

Short-term applications: Many industries stand to gain fromthe benefits of quantum computing in the very short term, even if
it needs to be paired with traditional high-power computation

Cost-effectiveness: Most calculations performed by enterprisequantum computers can be performed reasonably well bytraditional supercomputers and at a much lower cost; this is
expected to change once quantum advantage is achieved andgeneral-purpose quantum computers take center stage
Uncertain road map: Current advancements in quantum
technologies paint a promising future, but there may be potential
barriers to adoption (eg, regulatory, technological, financial) thatare not yet apparent
Nascent ecosystem: Only a handful of proven hardware
platforms are commercially available, and talent skilled inquantum computing is exceedingly rare; this may change as thetechnology matures and adoption increases

Source: McKinsey analysis

McKinsey & Company

91Quantum technologies

What are some topics
of debate related tothe trend?

1 Technology
readiness

Will quantum tech be ready in the next 10 years?
Quantum technologies are nascent,with many unanswered questions;despite generally optimistic outlooks,
these technologies still face anuncertain futureand2 Impact
disruption

Will quantum tech be as disruptive as projected?







3 Organizational
preparedness

Organizations from nearly every industry are already experimenting with or
showing interest in quantum computing, while quantum communications isalready being piloted by customers in many parts of the world
Despite years of research, quantum computers are still not consistently better
than classical computers at solving any major business problem
Currently, the best quantum approaches to optimization yield only a quadratic
speedup, which is not clearly superior to traditional computing when the additional
overhead associated with quantum computing (such as error correction) is
accounted for
Quantum computing is not expected to affect most computational work andwill be useful only for niche tasks

How should companies prepare for quantum tech?






Source: Expert interviews; McKinsey analysis

Many organizations have claimed that their quantum computers are outperformingclassical supercomputers, hinting at mature products within a decade
However, research is often disputed, and experts have refuted claims ofquantum supremacy in the pastMoreover, quantum computers have not yet replaced classical computers inany specific niche despite claims of supremacy, indicating that technologies have
yet to mature

Talent acquisition will be a major challenge for organizations in the near term, ascompanies rush to hire the few experts in the field
Identifying effective use cases before quantum technologies mature will give an
upper hand to forward-thinking organizations
Current quantum computers require massive investments to operate, yet they
provide computational power similar to that of less costly traditional computers
Since the quantum-technology stack has yet to mature, companies may find it
difficult to predict which hardware paradigm for quantum computers will
dominate in the next 10 to 15 years
McKinsey & Company

92Quantum technologies

Additional resources

Knowledge center

The Rise of Quantum Computing

Related reading
Quantum computing use cases are getting real—what you need to know
A game plan for quantum computing
The growing potential of quantum computing
Shaping the long race in quantum communication and quantum sensing

McKinsey & Company

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McKinsey
Technology Trends
Outlook 2022
Trust architecturesand digital identity
August 2022

Trust architectures and digital identity

What is the tech trend about?
Increasing cyberattacks and data breaches continually pose new challenges by leveraging trending technology (eg, quantum computing forencryption breaking). Digital-trust technologies empower organizations to gain a competitive advantage by building, scaling, andmaintaining the trust of stakeholders (eg, customers, regulators) in the use of their data and digital-enabled products and services.

High-growth technologies1

Zero-trust architecture(ZTA)

Digital identity

Privacy engineering

Explainable artificial
intelligence (XAI)IT security system design where allentities, inside and outside theorganization’s computer network,
cannot be trusted by default andneed to prove trustworthiness

Mechanisms for providing full
information that characterizes anddistinguishes an individual entity(eg, system, person, organization) in
the digital space

Techniques used to enable oversight,
implementation, operation, andmaintenance of privacy

Techniques for building
understanding of and trust in AI
models for real-world deployment

Includes access management, device
protection, network security, dataencryption, continuous monitoring, andmore

Entities’ identities consist of
distinguishing attributes (eg, name,identifier, characteristics)

Includes reducing risks to data
privacy, resource allocation, andembedding privacy enablement into
existing systems

Addresses fairness, accountability,
responsibility, transparency, andethicsDigital trust addresses digital risk across data, cloud, AI and analytics, and risk culture
1Technology areas and specific technologies are not exhaustive of all developments in cybersecurity.

Source: McKinsey analysis

McKinsey & Company

95Trust architectures and digital identity

Why should leaders pay attention?
Digital-trust technologies can reduce risk and potential negative impact from cyberattacks
Cyberattacks and cyber regulations are accelerating …

… so technology must keep up$101.5globalorganizations
experienced acyberattack in2019 (+36% vsprior year)

Increasing
complexity andfrequency of
cyberattacks

220

×

1 out
of 3

Increase in spamfrom Feb−Mar 2020
due to COVID-19
outbreakvulnerabilities

Rising costs
and losses
fromcyberattacks~$10.5trillion
Forecast costs
and losses related
to cybercrime by2025, representing
a 15% annual
increase

Growing
regulation in
the US andglobally+31
New national
cyber policies in
the US in the past 5
years, with greaternumbers globallybillion

3–13%

Projected spending onservice providers indigital trust by 20251

Predicted economic
benefits of digital
identity by 2030,
expressed as a shareof GDP

85%~85%

Share of small andmidsize enterprisesthat intend to increase
IT security spending
until 2023

Share of companies
that are victims ofdigital-identity-related
fraud each year

1Service providers include consultants, hardware support, implementation, and outsourcing.

Source: Center for Strategic International Studies; IBM; Identity Theft Resource Center; Kaspersky Lab; National Cyber Security Centre; PurpleSec data survey; Statista;
McKinsey Cyber Market Map, McKinsey Global Institute

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Why should leaders pay attention?
(continued)

Trust architectures and digital identity

Digital trust offers value creation, enabling organizations toscale faster and become more effective

Increasing
opportunities …

… in a landscape ofcomplications and pitfalls ...

 Exponential potential forstacked wins

 Increasingly aggressive regulatory
scrutiny, resulting in substantial fines andpenalties

 Increased speed of digitization

 High potential-market-value
advantage Better ability to engage in risk
reduction

 Heavy reliance on legacy governanceprocesses and technologies
 Hard-to-understand AI algorithms, which
are more complex and less predictable
than traditional analytics

 Growing scrutiny from public, media, andwatchdog organizations… leading to economic impact
and value
Build a strong foundation ofdigital trust with customers,
enabling increased acquisition
Leverage digital trust to scaleinternal data and analytic
programs sustainably
Advance strategic position foradvantage over competitors
across AI and analytics, data,cloud, and risk culture Increasing global uncertainty

Source: McKinsey analysis

McKinsey & Company

97Trust architectures and digital identity

What are the most noteworthy technologies?
Zero-trust architecture assumes “zero trust” for more robust and secure data flow across technical systems
From: Traditional, perimeter-basedarchitecture
After users are verified and gain access past perimeter controls,
everything within the network is assumed safe, which does
not robustly protect against inner threats

Organization

To: Zero-trust architecture

Benefits

The assumption is that all entities, within and outside theorganization, are not to be trusted
 Controls (eg, identity and access management, network
controls) are set up for any interaction by an entity
within the network
 Strength of controls depends on importance and risk
level of protected data and/or asset
 Network is micro-segmented to divide data and isolate
attacks on data segments

Increased security and reduced
risk from increased controls acrossorganizational network andcustomer data

Cost reduction as losses fromcyberattacks decreaseIncreased visibility andunderstanding of user access andtraffic across the network fromcontinuous monitoringUpskilled workforce andstreamlined technical stackprovide companies with stronger,
faster technical capabilities andmitigate technical complexity,
priming the company forincorporation of other cybersecurity
technologies



Improved reputation (due to fewer
breaches in security and stronger
technical stack), which can attract
customers

Organization
Data
Data

Internalcomputer
network

Stronger controls

Internal computer
network

Controls
External network

Controls
External network

Source: McKinsey analysis

McKinsey & Company

98Trust architectures and digital identity

What are the most noteworthy technologies? (continued)
Digital identity is enabling decentralization and new forms of verification – examples of innovation

DiagramSelf-sovereign identity (SSI)‘Passwordless’ identity

Data flow

Biometric verification1

Verifiable credential

ID provider

Proof

User

1

2

3

4

Generate
log-inchallenge

Approvechallenge

Sign
challenge

Log-inresponse

Organization

DescriptionUsers have control over their verified credentials (attribute information to identify
an individual); they can select the specific data for sharing (eg, name, password) andthe sharing audience (eg, employers, healthcare provider)

Users can verify and authenticate their digital identity without traditional
alphanumeric passwords but with other forms of identifying information

Functionality

Users interact directly with ID issuers and organizations without relying on anintermediary to facilitate data exchangeUsers can provide alternative identifying information, such as:Data and user credentials are stored on a decentralized ledger (eg, blockchain) foreasy access and verification

Benefits—

Biometrics (eg, facial scan, retinal scan, thumbprint, voice)—Devices and apps (eg, mobile phone, email)—Documents (eg, driver’s license, passport)

Increased individual control over identity for trusted transactions without anintermediary; users themselves control what data they share and with whom froman interoperable and convenient identity source

Alternative protections against rising vulnerability attacks (eg, phishing,brute-force password cracking)

Reduced inefficiencies for the user (eg, too many passwords, lost password)Improved security, because decentralized data storage limits vulnerability to
attacksEfficiency and convenience; users can rely on streamlined identifying
information, based on the level of risk associated with the system

1Diagram adapted from Alex Brown, “Passwordless authentication: A complete guide [2022],” Transmit Security, Jan 13, 2022.

Source: Expert input; McKinsey analysis

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Trust architectures and digital identity

What are the most noteworthy technologies? (continued)
Privacy engineering governs data privacy protection, while XAI builds trust in AI models

Description

Benefits

Privacy engineering

Explainable AIDesign techniques used to enable the practice governing
implementation, operations, and maintenance of privacyBroadly, these technologies support the strategic reduction ofprivacy risks, resource allocation, and implementation of privacy
controls

AI-related techniques combining social science andpsychology to enable people to understand, appropriately trust,and effectively manage emerging AI technologies

Types of “explainability” differ based on the explanation objective(eg, explaining how the model works, clarifying why a model input
led to its output, and providing additional information needed forpeople to trust a model and deploy it)More fair algorithmic outputs given that XAI technologies canhelp mitigate bias in the data, model, and other processesIncreased transparency, confidence, and reliability in AI
models, improving organizational performance, reputation,and relationships



Improved efficiency and effectiveness across AI model
pipeline, due to greater understanding of model data, inputs,
outputs, and algorithms

Increased safety and control over data for customers,
employees, and organizations, resulting from additional controls
and protective measuresEasier process to implement privacy changes, because thetechnologies form a privacy infrastructure that can facilitate privacy
updates from the continually evolving regulatory landscape

Source: Alejandro Barredo Arrieta et al., “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, Volume 58, June
2020; Katherine Miller, “Should AI models be explainable? That depends,” Stanford University Human-Centered Artificial Intelligence, Mar 16, 2021; McKinsey analysis

McKinsey & Company

100Trust architectures and digital identity

What industries could be most affected by the trend?
Digital-trust technologies could affect all industries leveraging digital technology via reduced risk
Information technology and electronics and financial services are leading adoption, followed by industries managing highly sensitive andregulated data (eg, healthcare, retail)Industry affected1

Impact from technology trend

Decreased losses and mitigated risk, because more secure systems (from ZTA and privacy engineering) prevent cyberattacks



Improved software solutions and AI model development and deployment via embedded protocols and controls from privacy engineering and XAIEnhanced customer experiences and reduced customer friction (eg, easier verification, log-in, etc) through easier, wider options for
digital identification

Support of Web3 and metaverse technologies such as digital avatars and blockchain-supported decentralized storage for SSIDecreased losses and mitigated risk where digital identity verification is crucial for transactions

Pressure on regulators to increase compliance related to digital identity and data sensitivity

Support for decentralized-finance (DeFi) applications (eg, verification for crypto loans)

Healthcare systems
and services;
pharmaceutical andmedical productsValue created by privacy engineering that balances protection of sensitive healthcare data with development of new uses for these dataImproved secure access to patient medical records; ZTA controls strength of protection, and digital identity can enable a single, unified
data source

Advanced development of AI models for healthcare diagnostics, drug design, and treatment, due to greater understanding from XAI

Consumer packaged
goods and retailImproved secure access to sensitive customer data, enabled by ZTA controls and digital identity

Advanced development of AI models to improve the customer journey and increase revenue, based on greater customer
understanding from XAIStronger brand reputation, as the technologies encourage stakeholder trust

Information
technology andelectronics

Financial services

1Not exhaustive; focused on industries leading business adoption.

Source: McKinsey analysis

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101Trust architectures and digital identity

What industries could be most affected by the trend? (continued)
Digital-trust technologies could affect all industries leveraging digital technology via reduced risk.
The following industries also demonstrate high value-creation potential from digital-trust technologies.

Industry affected

Impact from technology trend

Aerospace anddefense Prevent data breaches that could threaten national security and classified information

Education

 Protect students’ digital identity and data while ensuring access to educational resources

Media andentertainment Protect intellectual property and media content across a fragmented industry value chain dependent onflows of consumer and sensitive data

Public and social
sectors

 Enable expansion of digital service opportunities
 Secure and verify digital identity in addition to privacy engineering to protect citizen data

Telecommunications






Source: McKinsey analysis

Build digital-identity services on next-generation networks to expand their offerings
Enable enhanced customer experience
Ensure security of 3rd-party partners on their networks
Apply ZTAs and privacy engineering to internal systems and processes
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What should leaders consider whenengaging with the trend?

Trust architectures and digital identity

Zero-trust architecture

Digital identity

Overarching risks and uncertainties

Long-term effort with incremental progress

Nascent ecosystem

Effective and full-fledged ZTA, privacy engineering,and XAI cannot be implemented immediately; forreliable results, organizations should gradually
increase their controls and test them

SSI has relatively few standards available, and Web3 is a
rapidly growing space

Implementation complexity will be significant given
resource requirements, talent scarcity, lack of sharedtaxonomies, coordination challenges across multiple
parties, and required shifts in organizational norms andpractices needed to achieve effective deployments
Compatibility challenges will be encountered when
updating or migrating technologies and integrating themwith legacy systems or with an abundance of fragmented
point solutions
Evolving regulations involving digital trust and privacy

Performance efficiency and scalability

Added authentication steps (eg, securecommunications using VPN and public key
infrastructure [PKI]) can slow daily work and network
efficiency; this can vary based on the frequency ofcontrols and size of the network

Various dependencies
Progress depends on use of existing standards andinfrastructures (eg, data regulations) and on development ofrising technologies; registering alternative verified credentials
can also be a complex process
Concerns over privacy of biometric data
Control, storage, and use of biometric data is a debated topic
regarding privacy and ethics

Privacy engineering

Explainable AI

Inherent tension between privacy and fairness

Lack of standardization

Privacy and fairness can conflict: privacy approaches
could restrict collection of personal data, while
fairness approaches would collect personal data todetect bias

Deciphering the “black box” of large AI models to provide ameaningful explanation is challenging and depends on thetask; resulting solutions could face new or unaddressedrisks and need to balance privacy, fairness, accountability,
responsibility, transparency, and ethics

Source: McKinsey analysis

have become a prominent topic, as past standards (eg, on
data privacy and data permanency) conflict with thesetechnologies; regulatory measures to reconcile thesedifferences and define newer areas will influence the directionof digital trust
Tensions between privacy and fairness can arise, forexample, tension between the avoidance of excessive
collection of demographic data and the need for that data toassess and mitigate bias
Lack of standardization and widely accepted best practices
for how or when to use trust architecture techniques acrossindustries will continue to be a challenge

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103Trust architectures and digital identity

Who has successfully created impact with trust architectures anddigital identity?

Zero-trustarchitecture

A Latin American oil and gas company with a small IT estate began maturing its
capabilities before establishing a ZTA rollout plan; rollout of the security update
occurred on a system-by-system basis, targeting high-risk assets first, with thefirst full ZTA proof of concept implemented 1 year following rollout start dateSelfsovereign
identity

BankID is a digital-identification service providing users in Sweden a single
source of ID through their mobile phones; with BankID, users can make payments,
participate in financial services, log in to government platforms, and access their
medical records.

Passwordless Apple has been working toward passwordless sign-ins, such as with Touch ID (ie,thumbprint) and Face ID (ie, facial recognition); As of May 2022, numerous
identitytechnology companies and service providers, including Google and Microsoft, areworking with the FIDO Alliance and World Wide Web Consortium to support
passwordless sign-in standards

Source: Company websites; McKinsey analysis

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104Trust architectures and digital identity

1 Stakeholder
What are some
topics of debate
related to thetrend?
Development of digital trust
depends on other trending
technologies and the overall
ecosystem, raising questions
about its path forward.

expectations

How are the expectations of customers, employees, andcommunities changing in terms of data (especiallyprivacy), transparency and outcomes of analytics, andtechnology security and resilience?
As new technologies seek to personalize the user experience, stakeholders
will expect a balance between privacy (a priority that calls for not collecting
demographic data) and fairness (a priority that can use demographic data totest for and correct biases). In one study, 97% of people surveyed expressed
concern that businesses and the government might misuse their data.1

2 Data andprivacy
regulation

3 Risk area

identification

How do regulators reconcile past standards with risingtechnologies that have inherent conflicts?
Existing data privacy regulations can be at odds with emerging Web3technologies. For example, the “right to be forgotten” from the General Data
Protection Regulation in the EU enforces people’s right to have their datadeleted. Storing data on the blockchain, however, creates the potential for an
immutable ledger from which past data cannot be “deleted.”

Where are companies typically most exposed to digital
and analytics risk?
Improperly-decommissioned legacy systems, breaches to 3rd-party partners,
and poorly configured database links, for example, are vulnerabilities thathackers can exploit. The right risk-management approach will requirecompanies to define a mature enterprise-risk framework and conduct formal,
holistic risk assessments tailored to their individual systems.
McKinsey & Company

1Theodore Forbath et al., “Customer data: Designing for transparency and trust,” Harvard Business Review, May 2015.

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Trust architectures and digital identity

Additional resources

Knowledge centers

McKinsey Risk and Resilience Practice
McKinsey Technology: Cybersecurity

Related reading
Getting to know—and manage—your biggest AI risks
Derisking digital and analytics transformations

Cybersecurity trends: Looking over the horizon

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McKinsey
Technology Trends
Outlook 2022
Web3
August 2022

Web3

What is the tech trend about?
Web3 includes platforms and applications that enable shifts toward afuture, decentralized internet with open standards and protocols while
protecting digital ownership rights, providing users greater data ownershipand control over how their data is monetized, and catalyzing new businessmodelsWeb2 (Current)

Web3“Internet users (mostly) exchange their data
for services based on TOS1”“Internet users own their data and shape
the terms of its use”

Highlights

Centralization where large platforms govern access andown the user data. Users have the “right to be forgotten”
in some jurisdictions

Disintermediation with a shift toward individual ownership and control
over data monetization, functionality, and value

Businessmodel

Data monetization by tech platforms (eg, personalized
ads, revenue sharing) where user audiences are often
the “product” sold to advertisers

Revenues are shared back to users and/or capital contributors
through smart contracts and decentralized autonomous organizations(DAOs)

Technical
infrastructure

Centralized and aggregated services (eg, cloud computing
services, security models, app stores)

Decentralized databases and software programs (eg, smart contracts)

Private code, private data, cloud platform-based security
models1Terms of service.

Accessible tools for building on the platform and services

Source: Expert input; McKinsey analysis

Open-source code, public immutable data, public-private key cryptography,
and composability (ie, ability to fork and integrate on top of existing projects)
Accessible tools for building on the services but often in new languages
and with Web3-specific components
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Web3

Why should leaders pay attention?
Web3 is based on a more decentralized, community-governed set of protocols that could represent a paradigm shift ofauthority and ownership to individuals with potentially far-reaching implications

Disruption of existing
business models

Rapid innovation
through open protocols

Increased accessand inclusion

Opportunity to build
new infrastructure

Unified customer
experience

Web3 enables thedisintermediation ofbusiness models byencoding existing
functionality into
autonomous smart
contracts. Web3 could
offer wider economic
opportunities (eg,decreased fees), moving
the accumulation of value
away from middlemen andtoward users and suppliers

Rapid innovation is
unlocked by the opensource and modular
nature of Web3, whichallows for rapid
development, testing,and scaling of
applications, built by aglobal developer base.
This composability andgrowing developer basecould increase the level ofinnovation exponentiallyover time

Web3 is public andpermissionless, meaningthat everyone canaccess, create, and own
information and assets,
globally, without
intermediaries

Web3 infrastructure isnascent; newapplications require newtools and infrastructure
to scale and meet
expected service
requirements (eg,development platforms
needed to build Web3
internet services,
middleware software, node
infrastructure, etc)

Web3 enables a transition
from omnichannel tounichannel, seamlessly
integrating customers’
digital identity acrossapplications by leveraging
a common decentralized
blockchain data layer, as
opposed to a siloed view ofcustomers that is limited to
an individual business’s
customer profile

Source: Expert input; McKinsey analysis

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Web3

Why is there enthusiasm for Web3?
Talent and investment have rapidly increased in the past year, with continued expectations of long-term growth

TalentCapital

Number of developers contributing to Web3 projects,1 2015–21
thousands

PE and VC investments2 in Web3 deals, 2017–21$ billion
36.63

40
40

34.4

30

30.2

30
20

20
10
10
0
2015

0
16

17

18

19

20

2021

2017

18

19

20

21

>34,000~70% per year

Active Web3 developers in 2021

Growth rate of PE and VC investments
in Web3, 2017–22

1Based on open-source repositories and code commits from GitHub.

2022

2Private-equity and venture capital investments.
3Extrapolated from 2022-H2 investment figures of $18.3 billion (“What winter? Crypto VCs continue their spending spree,” Fortune, Jul 2022).

Source: Dovemetrics; Electric Capital; GitHub; PitchBook; The Block Research

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Web3

What are the most noteworthy technologies and areasof interest?
Web3 can be decomposed by its foundational technologies and applications

Web3
applications
and use cases

Web3technologies

DeFi1

Art

Gaming

DAO

Identity

Web3
RetailMetaverseAnd
more …

3

2

1

Digital assets and tokens

Applications and use cases built on top of Web3 technologies,
including notable use cases in decentralized finance (DeFi),
entertainment (art and gaming), and retail, among others

Connection of these virtual experiences is sometimes referred to as
the metaverse

Digital assets: Digitally native intangible items such as native
cryptocurrencies, governance tokens, stablecoins, NFTs,2 andtokenized assets

Smart contracts

Smart contracts: Software programs established in immutable code on
a blockchain, which are automatically executed when specified
conditions (such as terms agreed on by a buyer and seller) are met

Blockchain

Blockchain: Digitally distributed, decentralized data ledger that exists
across a network of computers. These computers work together tofacilitate the recording and confirmation of transactions

1Decentralized finance.
2Nonfungible tokens.

Source: Expert input; McKinsey analysis

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Web3

What industries are beginning to adopt and shape Web3?
Many use cases across industries are being identified and built, especially as the tech infrastructure improves
Details on following pages

Media and entertainment:
GamingWeb3 enables interoperability acrossgames, digital assets facilitating new
gaming experiences, and play-and-earnbusiness models in which in-game rewards(eg, NFTs) are distributed with different
utilities and value

Media and entertainment:
Digital art and media
Creation and ownership of digital media(eg, artworks, video content)—sold as
NFTs—allow new business models andcreative possibilities while providing artists
with more control and, in some cases,
ongoing perpetual royalties

RetailRetailers are using Web3technologies to create
new offerings, devise newmodes of customer
engagement (eg,ecosystem loyalty
programs, access tounique experiences),
assure the authenticity ofgoods, tap into newroyalty-based revenue
streams, accept novel
payment methods (suchas “stablecoins”), andtrack and orchestratelogistics across loosely
coupled global supply
chainsFinancial services
DeFi is an ecosystem ofapplications that could
autonomously perform similar
services to traditional financial
institutions, albeit with very
different levels of protection,
and where the traditional
revenues are handed back tousers or liquidity providers ofthese applications. Many aregoverned through token-based
distributed governancesystems. Other areas offinancial services exploring
Web3 applications include
payments, asset management,
and some areas of capital
markets

IT andelectronics
Innovators will use Web3 tocreate decentralized, peer-topeer networks, enable socialmedia users to create and sell
their content, enable stronger
user control of digital identity, andlay the groundwork for the
adoption of metaverse platforms

Industries leading adoption
include financial services,media and entertainment,
retail, and information
technology and electronics

Emerging industries include
blockchain-based identity,1logistics, carbon markets, andpublic sector

1For more information about digital identity, see “Trust architectures and digital identity,” McKinsey, Aug 2022.

Source: Expert input; McKinsey analysis

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What are the examples ofbusiness disruption that Web3
could cause?
Web3 represents a new business paradigm in the gaming
industry’s revenue models: from pay-to-play1 and free-to-play2
today to play-and-earn3

Innovations
Incentives for players in a play-and-earn model where players earn ingame digital assets (eg, NFTs, in-game tokens) while playing, and caneasily monetize these rewards via peer-to-peer networks or exchanges
Distributed ownership opportunities, where players can realize true
ownership of in-game assets, with long-term potential for interoperability
across different Web3-enabled games and metaverse
Community-based development where the player community can share
feedback with developers and vote to change some in-game dynamics orcodevelop game patches and updates
Different business model for gaming companies through digital-asset
sales, secondary-market royalties (eg, for NFTs), and value exchange
channels (eg, dedicated marketplaces)
1With pay-to-play, accessing the game costs money.
2With free-to-play (“freemium” model), players do not have to pay for game access but usually pay in-game.
3With play-and-earn, playing earns rewards: players become owners of in-game items and earn rewards incryptocurrency.
Source: Expert input; McKinsey analysis

Web3Media and entertainment: Gaming

Potential risks and uncertainties
High barriers of entry due to up-front investment (eg, in-game NFT
and/or token purchases) and knowledge of existing Web3 tools (eg,self-custodial wallets) to start playing games
Low-fidelity gaming experience of present NFT games relative to
traditional Web2 gaming, largely attributable to nascency of industry
Incentive-driven player base, such that player retention is at risk if
rate of earnings decline, absent other incentives (eg, compelling
gameplay)
Controversy in the traditional gaming community, where lack ofunderstanding of Web3’s value proposition and skepticism toward
increasing monetization models is driving backlash
Nascency of infrastructure, in particular related to transaction
costs, throughput, and security, to access and play the game reliably
Regulatory uncertainty on asset classification of in-game NFTs anddigital assets, and of play-and-earn new business models
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What are the examples ofbusiness disruption that Web3
could cause? (continued)

Web3

RetailIn retail, engagement channels and data ownership will shift
away from business-centric to consumer-centricInnovations

Potential risks and uncertainties

New model for consumer data ownership and access, which could
enable greater control for users (eg, anonymous identity with permissioned
data sharing)

Unclear long-term viability of specific digital assets and communities,
coupled with (unwanted) volatility of digital asset markets

New channels and methods for customer acquisition, engagement,
and retention (eg, community-based retention models, Web3 loyalty
programs, Web3 marketing through airdrops, etc)
New product categories to express brand identity in digital native
form (eg, Web3 native items, digital twins of physical products)
New digital environments for customer engagement, potentiallyconnected across different Web3 channels (eg, metaverse)

Immature user experience and security for digital goods to assureownership and trusted transfer of assets (eg, self-custodial wallets)
Considerations on how to retain control over brand identity as new
platforms and experiences proliferate in public permissionlessinfrastructure

Evolving regulatory environment for consumer protection andpersonally identifiable information building from early implementations
notably in Europe (GDPR1) but expanding to many other jurisdictions

New value exchange models for global commerce (eg, stablecoins)
Provable authenticity and record of ownership for digitally native goods
1The EU’s General Data Protection Regulation.

Source: Expert input; McKinsey analysis

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What are the examples ofbusiness disruption that Web3
could cause? (continued)

Web3

Financial services

Web3 offers the promise of decentralization and potential
disintermediation throughout financial services, offering better
price and generating cost efficiencies

Innovations

Risks and uncertainties

Greater cost efficiency through employing smart contracts whose
programmable logic determines automated decisioning and disbursement(eg, in decentralized lending, swaps)

Unsettled regulatory picture, including unclear classification of assets
and jurisdictional authority

Interest revenues and trading fees returned to users (eg, depositors,
liquidity providers, lenders) instead of flowing back to the central enterprise

Limited or absent consumer protections (eg, for funds held in custody),
including irreversibility of any fraudulent transactions

Broader investor base and enhanced liquidity for traditionally illiquid or
inaccessible investment assets (eg, fractionalized commercial real estate)

Immature user experience, often with poor interface design and lacking
seamless integration with traditional financial services (eg, onboarding,funds transfer)

Greater transparency of transactions since all ownership andtransaction data reside on, and are potentially discoverable on, theblockchain

Security concerns, including the compromise and collapse of somenascent projects in DeFi, wallet theft, and poor AML/KYC1 controls

Low credit risk and delinquency in applications such as DeFi lendingthat entail overcollateralization requirements and automatic liquidations

Nascent technology, lacking traditional data privacy when using open,
permissionless distributed ledgers and reliant on software (smart contracts)and data (oracles) that are not yet battle-tested at scale

Facility for providing always-on financial services (24/7), such as
lending, trading, derivatives, mortgage, and insurance
1Antimoney laundering/know your customer.

Source: Expert input; McKinsey analysis

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Web3

What are some broader uncertainties affecting Web3 adoption?
Nonexhaustive

Evolving regulation as authorities choose
approaches to governing issues such as consumer
and investor protection, asset classification (eg,security, commodity, currency) and its implications,
legality, and enforceability of blockchain-based
contracts, accounting and tax standards, capitalprovisioning, accountability mechanisms, and knowyour-customer and antimoney-laundering standards

Consumer protection is increasingly becoming a
focal point for regulators, especially during times offailure of several nascent Web3 projects (eg, theFTC’s Consumer Protection Data Spotlight of June
2022 found that consumers lost $575 million fromJanuary 2021 to March 2022 as a result of bogus
cryptocurrency investment opportunities)

User experience and value proposition of Web3
alternatives compared with incumbent systems(which also are continuing to evolve) are often eitherpoor, unclear, or poorly understood

Robustness of new technologies that depend oncode (eg, smart contracts) or data (eg, oracles) is
improving but has experienced some notable
failures. The composability of underlying Web3 code
can perpetuate vulnerabilities

Ecosystem infrastructure is nascent and willcontinue to mature as business models (eg,merchants accepting digital loyalty tokens) and value
chains (eg, creation, trading, and secure storage of
NFTs) are tested and refined or discarded

Sustainability differences among Web3
platforms—relative to conventional alternatives—are
shifting, given the high-energy consumption of older
proof-of-work-based systems and the enhanced
efficiency of alternatives such as proof-of-stakebased systems

Source: McKinsey analysis

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Web3

How can leaders begin engaging with Web3?
Reevaluate your business model,
value chain, and industry
ecosystem to understand where
Web3 disruption may happen.
Likewise, keep an eye out for the riseof new competitors and businessmodels in industries leveraging Web3(eg, stablecoins, cryptocurrency
payments) that will challenge thestatus quo

Partner with Web3 players tofacilitate entry into new services andproduct offerings (eg, branded assets
with NFT creator studios, Web3
gaming experiences)

Source: Expert input; McKinsey analysis

ExplorePartner

Upskill

Upskill workforce, from leadershipto developers, on Web3 and its
applicability to the organization’s
relevant applications. Start
experimenting on small pilots andscale as teams learn by doing

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Web3

Who are industry players leveraging Web3 with impact?

Industry

ExampleDescription

Financialservices

USDF consortium
and Figure

USDF: a consortium of FDIC-insured banks—aims to promote the adoption andinteroperability of a bank-minted stablecoin in the form of tokenized deposits (asopposed to the fully reserved model) to facilitate the compliant transfer of value on
blockchains. This stablecoin will run on the Figure network, a public blockchainMedia andentertainment:
GamingNike acquisitionof fashion-gaming
company RTFKT
to enter Web3 andthe metaverse

RTFKT—a creator-led studio that uses the latest in game engines, NFT, blockchain,augmented reality, and other technologies to create one-of-a-kind sneakers and otherdigital artifacts—is widely regarded as a metaverse-native fashion brand that merges
culture and gaming. Nike acquired the company in late 2021; earlier that year, RTFKT
had a valuation at $33 million

RetailAura Blockchain
Consortium for
tracking andtracing

The Aura Blockchain Consortium (including, among others, LVMH, Prada,
Richemont) is a not-for-profit, collaborative industry initiative that enables
tracking and tracing for luxury goods. Using a universal private blockchain, it has
the goal of enhancing customer service by enabling consumers to access details of aluxury product’s entire supply chain, product history, and authenticity. Members pay
licensing fees and a fixed fee per product to track and trace any product with an NFT,
attached to the product through a QR code or RFID tag

Source: Company websites

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Web3

What are some
controversial
topics regarding
Web3?

1

Reliability andsustainability

Which Web3 business models and value chains will emerge astechnically reliable, scalable, and commercially sustainable?
Business models must show they can produce more value for users than existing systems,
achieve uptake beyond an enthusiastic cohort of early adopters, and overcome volatile periods.
They must also satisfy evolving regulations for consumer protection, asset classification, andknow-your-customer standards

2

Patterns ofadoption

Given Web3’s nascency and rapid development, what will unlock
mainstream adoption? As a cultural phenomenon, how will patterns of
Web3 adoption vary among different populations?
In order to become mainstream, Web3 tech needs to improve in terms of scalability, reliability, andsecurity, with accessible developer tooling and improved user experience for consumers. Adoption
across geographies will differ depending on a variety of factors, including government regulations,public interest in an open internet, and access to adequate connectivity

Nonexhaustive

3

Enterprisearchitecture
integration

How will Web3 ecosystems coexist and interconnect with enterprisesystem architectures and with hyperscale Web2 platforms?
Interoperability of platforms will be necessary for users to transfer assets (eg, enabling the transfer
of avatars between different virtual worlds). This interoperability is a stark departure from thecentralized business models of many web 2.0 businesses

How will regulatory action influence trust in Web3 and affect

4 Regulating trust potential future innovation?

Insufficient clarity around regulatory frameworks on Web3 technologies may contribute to reduced
trust among users and developers. At the same time, Web3 may address a growing need for
empowerment of the individual across data, functionality, and value, enhancing the trust of users
and developers in the Web3 fundamentals

5

Source: McKinsey analysis

New models
and themetaverse

How will the Web3 and immersive-reality trends influence existingmodels for accessing and building systems on the internet and cometogether to enable new experiences in the metaverse?
As the Web3 trend blends with others, new propositions and platforms will emerge. The
metaverse, for example, could leverage tech trends like immersive reality and advanced
connectivity to enhance and enable innovative user experiences in Web3 environments.
Future use cases of open learning, working, entertaining could take place here
McKinsey & Company
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119

Web3

Additional resources

McKinsey Blockchain & Digital Assets

McKinsey Metaverse
Telecom’s future in the Web3 era: José María Álvarez-Pallete López

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Trend summaries:
Engineering
Tomorrow

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McKinsey
Technology Trends
Outlook 2022
Future of bioengineering
August 2022

Future of bioengineering

What is the trend about?
Focus for tech trend

From the cellular level to complex living systems, the future of bioengineering reflects the convergence of biological andinformation technologies to transform business and society
It is defined by 4 arenas: biomolecules, biosystems, biomachine interfaces, and biocomputing; in recent years,
biomolecules and biosystems have experienced widespread developments1

Biomolecules

Biosystems

Biomachine interfaces

Biocomputing

Mapping and engineering
intracellular molecules (eg,DNA, RNA, proteins)related to the study ofomics (eg, genomics,
proteomics)

Mapping and engineering
complex biologicalorganizations, processes,
and interactions (eg, cells,
tissues, and organs)

Connecting nervous systemsof living organisms tomachines

Using cells and cellularcomponents for computationof information (eg, storing,
retrieving, processing data)

1Technologies featured are a selection of growing and promising technologies but are not exhaustive of all technologies in the field.

Source: McKinsey
Bioengineering
Report transforming
2020
Source:“The Bio Revolution:
Innovations
economies, societies, and our lives,” McKinsey, May 13, 2020

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Future of bioengineering

Why should leaders pay attention?
Across industries, efforts in thedevelopment and adoption of
bio-related technologies are increasing

400

78%

These efforts could unlock transformative newcapabilities, with a strong impact on scope and scale

Number of scientificallyfeasible use cases with implied
economic impact acrossmultiple industries identified

Providing new
business opportunities

Addressing global
issues

$2T–$4T

45%

Share of top global revenuegenerating companies withsome level of sustainability
commitments related toscope 1 or 2 emissions

Forecast annual global impact ofbioengineering in 2030–40

Share of global disease burdenthat could be addressed

Transforming
production processes

Shifting
investment focus

60%

30%

Share of world’s physical outputs
that could be made using biologicalmeans

Share of private-sector R&D
that could be spent in biorelated industries>$400 Investment in cultivated meatin the first half of 2021,
million projected to increase rapidly

Source: “The Bio Revolution,” May 2020; CDP Worldwide; PitchBook; S&P Capital IQ; Science Based Targets Initiative; McKinsey analysis

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Future of bioengineering

What are the most noteworthy technologies?
Across biomolecules and biosystems, several technologies have recently made significant progress
Nonexhaustive

Topic

Technology1

Description

Benefits

ExampleViral-vector
gene therapy

Permanent replacement of poor-functioning genes
Treats previously uncurable diseases
to treat genetic diseases, where modified viruses act Can address diseases before they are
as drug-delivery vehicles of genetic sequences
symptomatic

Treatment forcystic fibrosismRNAtherapy

Temporary use of synthetic mRNA translated into
protein to compensate for missing or mutated genes

Offers temporary alternative to gene therapy
that aids gene expression without risk

COVID-19
vaccineTissueengineering

Cultivatedmeat

Meat made by taking a small sample of animal cells
and growing them in a controlled environment,
emulating conventional meat qualities

Combines attributes of animal meat and plantbased meat with strengths in taste, food safety,
animal welfare, and worker welfare

Cultivatedchicken meat for
consumption

Biomaterials

Drop-in

Materials that replace fossil-fuel-derived chemicals
with biochemicals without changing existingproduction processes

Create cost-effective materials with minimal
production disruption
Offer more environmentally friendly alternatives
to traditional chemicals with carbon emission
reduction

Bioethanolbasedpolyethylene

Bioreplacements

Materials using biochemicals that provide similar
quality and cost but have better environmental
impact than traditional chemicals

Improve sustainability but require complex
value chain changes
Minimize regulatory hurdles with low entry
barriers

Vegan leather
made frommushrooms

Biobetter

Materials with new combinations of properties
developed from unique biochemical synthesis

Improve sustainability
Offer strong quality and technical performance

Bio-based
optical films

Omics

1Technologies are nonexhaustive; they were selected based on their combination of innovation, business adoption, and impact.

Source: “The Bio Revolution,” May 2020; expert interviews; McKinsey analysis

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Future of bioengineering

What industries are most affected by the trend?
Healthcare, including pharmaceuticals and fitness, is the leading industry in adoption of bioengineering, especially in development of new
medical treatments
Other industries scaling adoption are retail, consumer goods, agriculture, energy and utilities, and materials

Industries affected1
Healthcare systems;
pharmaceuticals andmedical products

Impact from technology trend
Advancements across healthcare, pharmaceuticals, wellness and fitness, and biological sciences for improved
understanding of health conditions and diseases (eg, diagnosis, monitoring), treatment, patient outcomes, and scientificdiscovery
Ethical and long-term health concerns around use of novel and innovative technologies on humans (eg, impact of germ line
gene editing on future generations)
Increased access and shift to more sustainable and cruelty-free food sources through cultivated meat

Agriculture

Potential economic disruption across supply chain for food
Ethical and long-term health concerns associated with unconventional production of food sources

Chemicals

Advancements in sustainable, cost-effective, and higher-quality biomaterials and production processes

Consumer packaged
goods

Creation of sustainable, cost-effective, and higher-quality materials and production processes for consumer goods,
such as clothing, accessories, shoes, beauty products, and packaging

1Nonexhaustive; focused on industries where technology has widespread applications with mature adoption.

Source: Expert interviews; McKinsey analysis

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What disruptions could the trend enable in healthcare systems andservices and in pharmaceuticals and medical products?
$0.5–$1.3
trillion

Benefits

Examples of technologies

Forecast global
impact, 2030–40
Increasing
number oftherapies,
including thosethat can treat oreven prevent
previously
uncurablediseases

Risks and
uncertainties

Viral-vector gene therapy

mRNA therapy

As of Feb 2022, there are 8 FDA-approved therapies, with
25 in late-stage development and another 120 in Phase II
trials, and growing work on more therapies

As of 2022, there are ~130 RNA assets in the pipeline, witha predicted 40% annual growth rate for ~1,800 RNA assets
by 2030

Expected outcomes
Treatment for monogenic and polygenic
diseases
Treatment for ~10,000 diseases caused by a single
gene (eg, sickle cell anemia, hemophilia, inherited
blindness, immune deficiencies) and diseases caused
by a combination of genes (eg, cardiovascular,
neurodegenerative, metabolic, reproductive)

Novel cancer treatment
Treatments addressing all stages of cancer (from screening to treatment to cure), especiallycancer linked to genes (eg, BRCA1 and BRCA2 for breast cancer)

Aging prevention
Anti-aging therapies that eventually assist with tissue repair, longevity, mental cognition, andphysical capabilities

Health risks
Personalized treatments

Long-term health effects are also still being investigatedBespoke treatments using genetic data to identify riskof certain diseases (eg, COVID-19, HIV) and toprovide targeted treatment

Ethical concerns
Ethical and moral concerns about potential unintended side effects of modifying genes, andwhen applied to embryos/germ lines, its impact on future generations
Source: “The Bio Revolution,” May 2020; expert interviews

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Future of bioengineering

What disruptions could the trend enable in consumer goods?
$200B–$800B
Forecast global
impact, 2030–40
Improvingproduction
processes forsustainability andcost-effectivenesswhile maintaining
end-productquality
Adding new
capabilities to
products

Benefits

Risks and
uncertainties

Examples of technologies
Drop-in

Bioreplacements

Biobetter

Sustainability-oriented clothing lines
leveraging biochemicals (eg,biomass waste streams) can be
implemented with minimal disruption

Biotech textiles (eg,mushroom leather, spidersilk) are growing among
apparel manufacturers

Cosmetics can be produced
more easily, with newqualities, and personalized toindividuals’ skin microbiomes

Expected outcomes
Reduced carbon footprint

Alternative renewable resources

Production can utilize sustainable processes, such as
leveraging biomass waste to synthesize materials

Difficult-to-access or costly materials can be derived from bio
routes (eg, using fermentation-based manufacturing to extract
complex natural fragrances)

Personalization in beauty and cosmetics

Disruption in value chain

Technologies offer advancements in omics and biomaterials
to better cater to individual customer needs

Bioreplacements can cause complex disruption in the value
chain; vegan leather is often a popular topic of debate on its
widespread implications (eg, economy and consumer
perception) beyond environmental impact

Source: “The Bio Revolution,” May 2020; expert interviews; McKinsey analysis

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Future of bioengineering

What disruptions could the trend enable in agriculture?
$0.8T–$1.3T
Forecast global
impact, 2030–40
Sustainable andcruelty-freealternatives to
traditional food
options

Benefits

Examples of technologies

Risks and
uncertainties

Cultivated meat
Lab-grown meat, such as beef, poultry, and seafood, can be produced and harvested

Expected outcomes

Sustainable, accessible food source

High prices and limited variety

Production techniques are more accessible, environmentally friendly,
friendly to animal welfare, and friendly to worker welfare

As a relatively nascent product, cultivatedmeat is priced higher than traditional meatand has limited variety; as the industry
scales, consumer prices should decrease(with reduced production costs), andproduct variety is expected to increase

Consumer acceptance and unknown long-term healthimpactConsumer perception is crucial for adoption of cultivated meat;producers need to strengthen confidence in safety and nutritional
value, which varies depending on meat type; novel processes may
use ingredients with unknown long-term health effects

Economic disruption and scale

Limited regulatory approval
Singapore is currently the only country toapprove sales of cultivated meat

Cultivated-meat adoption could disrupt existing agricultural value
chains if society decides to adopt alternative foods broadly

Source: “The Bio Revolution,” May 2020; expert interviews; McKinsey analysis

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Future of bioengineering

What disruptions could the trend enable in chemicals, materials,
and energy?
$200B–$300B
Forecast global
impact, 2030–40
Alternative,
sustainablesources andprocesses formaterials andenergySource: McKinsey analysis

Benefits

Risks and
uncertainties

Examples of technologies
Drop-in

Bioreplacements

Biobetter

Environmentally friendly
replacements for popularfossil-fuel-derived chemicals(eg, polyethylene, plastics)

Biofuels, alternative
renewable-energy sources(eg, oil from geneticallyengineered microbes), andraw materials

Novel biotech films thatdeliver unique material
properties (eg, opacity,
oxygen/water permeability)

Expected outcomes
Sustainability and reduced carbon footprint

Uncertainty around timing of impact

Biomaterial-based production processes can lead to
reductions in carbon footprint by as much as ~50%

Current solutions are not cost-competitive with existingfossil-fuel technologies

Increased source material optionality

Scalability challenges

Incorporation of biogenic carbon into the materials value
chain provides wider material sources and novel production
methods; when coupled with carbon capture, this can also
result in carbon-negative products

Bio-based solutions are not necessarily scalable to theextent of full replacement of fossil fuel

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Future of bioengineering

What should leaders consider
when engaging with the trend?

Benefits

Risks and uncertainties

Opportunity to address global challenges
through improved/enhanced healthcare solutions
and accelerate environmental impact throughrenewable-energy sources, and more

Nascent biomarkets, which need to address thechallenges of consumer perception, safety, cost,and quality of end products

Novel sustainable production practices thatare more environmentally friendly than traditional
methods while often being cost-effective

Source: McKinsey analysis

Lack of regulation due to nascency of markets,along with existing regulations on geneticallymodified organisms
Ethical concerns about the extent of modifying
living systems, such as human genes

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What are some topics
of debate related tothe trend?

and
1 Riskbioethics

How should we use bioethics to determine theappropriate extent for genome editing?
With its cross-disciplinary innovations
and potential cross-cutting impact,bioengineering ventures into
interconnected areas of debateBiology is self-replicating and self-sustaining; it lacks boundaries; due togaps in knowledge and interconnections among the biological sciences,
experimentation could lead to unintended, potentially harmful consequences
Some gene therapies and other methods (somatic gene editing) are generally
viewed as appropriate for treating rare diseases; other gene methods that could
affect future generations (germ line gene editing) are contentious
Likewise, different values and principles can influence different perspectives on
ethical use and misuse in bioengineering, such as editing human traits, dubbed“playing God”

to
How does cultivated meat fit within existing diets? Is it
2 Changes
existing daily life vegetarian, vegan, kosher, etc?
• Cultivated meat can benefit welfare for animals and human workers (eg, it's crueltyfree), which makes it a more ethical as well as sustainable option• However, cultivated meat is an unprecedented and nuanced area for dietary
restrictions (eg, some consider it to still be an animal), and individual consumers
make take a different stance; in the future, cultivated meat could receive
standardized certifications (eg, cruelty-free, kosher) to facilitate consumer decisions

3 Outlook

What will shape the long-term impact and implicationsof bioengineering technologies?


Source: Expert interviews; McKinsey analysis

Varying perspectives debate timeline, type and scale of impact, and level of
disruption (eg, regulatory changes) in society and the economy
Based on their execution, these technologies could reinforce or widensocioeconomic disparities due to unequal levels of technological accessAlongside the digital debate on privacy and consent, these topics also touch on
debates related to individual personal biological information (eg, ancestry,
hereditary traits)
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Future of bioengineering

Related reading

The Bio Revolution: Innovations transforming economies, societies, and our lives

Additional
resources

The third wave of biomaterials: When innovation meets demand
Cultivated meat: Out of the lab, into the frying pan

Inside the fact-based report on biological science that reads like science fiction
How could gene therapy change healthcare in the next ten years?
COVID-19 and cell and gene therapy: How to keep innovation on track

Viral-vector therapies at scale: Today’s challenges and future opportunities

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McKinsey
Technology Trends
Outlook 2022
Future of clean energy
August 2022

Future of clean energy

What is this trend about?
The clean-energy future is a trend toward energy solutions that help achieve net-zero emissions across the energy
value chain, from power generation or production to storage to distribution
Power generation
Renewable energy

Sustainable fuels

Hydrogen (H2)-based fuels

Solar photovoltaics (PV) and thermo-solar, wind,geothermal, nuclear

Including biofuels

Production of hydrogen as an energy source

About 84% of global power demand, which isestimated to grow 3× by 2050, can be met using
renewable energy

Sustainable fuels could
decarbonize high-energydensity requirements ofaviation, maritime shipping,and heavy freight

Producing decarbonizedhydrogen (blue, using carbon
capture; green, using
renewable electricity) is
projected to cost less than
producing conventionalhydrogen (gray, from naturalgas) by 2030

Solar photovoltaics are expected to cover ~60%,
onshore wind power generation to cover ~20%,
and offshore wind power generation to cover ~4%

Demand growth rate is
expected to outpace thatof fossil fuels

Limited capital is required
to transition; these “dropin” fuels do not require
upgrading existing engines

Source: McKinsey analysis

Electrolyzers’ critical role inunlocking demand forgreen hydrogen is thatthey reduce the cost of
production

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Future of clean energy

What is this trend about? (continued)
Power storage

Power distribution

Energy storage
Battery technologies, battery recycling/second use,
long-term storage, gravity-based storage, etc

Energy optimization and distribution

Stationary storage system

Smart gridEV1 charging infrastructure (EVCI)Long-duration energy storage
technologies are expected to drive ~20%of renewables adoption, enabling~2.4 gigatons (Gt) of renewables abatement;
short- to mid-duration storage is expected
to expand renewables penetration from
30–80%, indirectly enabling up to ~6 Gt ofabatement

Advanced, intelligent electric grid
system could provide real-time insights
and control for the distribution grid

EVCIs compete primarily on charging
time and cost, with wide ranges in both:charge times range from ~8 hours to just
10 minutes, and prices range from €7,500
to €110,000

Increasing AI applications across smart
grids could leverage big data’s potential(eg, improving accuracy of demandpredictions)

1Electric vehicle.

Source: McKinsey analysis

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Future of clean energy

Why should leaders pay attention?
Overall trend

Significant near-termvalue at stake

Bolder environmental
regulation

Increasing power demand

Increasing corporate
commitment~$2.4 trillion

~20%

~3.3×

>1,000

Annual capital spending
required in 2031–35 for the netzero transition: $1.2 trillion in
power generation, $1 trillion in
the power grid, and $200 billion
in energy storage in the NGFS1Net Zero 2050 scenario

Increase of climate-related
laws and policies since 2020 in
China, EU, and US2

Increase in global electric
power demand in a 1.5°C
scenario by 2050

Number of companies that in
2021 set science-basedtargets toward 1.5–2.0°C goals,
growing by ~3× from 2020 andrepresenting a market cap of~$23 trillion

1Network for Greening the Financial System.
2Current number of policies is 11 in China, 17 in US, and 48 in EU.

Source: Major risk or rosy opportunity: Are companies ready for climate change?, CDP Worldwide, 2019; “Nuclear fusion market could achieve a $40 trillion valuation,” Bloomberg, Dec 28, 2021; The
net-zero transition: What it would cost, what it could bring, McKinsey, Jan 2022; Global energy perspective 2022, McKinsey, Apr 2022; McKinsey analysis

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Future of clean energy

Why should leaders pay attention? (continued)
Energy tech

RenewableenergySustainable fuels

Nuclear fusion

Energy storage

Energy storage

>80%

3.3×

>$4 billion~30% CAGR

30−60%

Share of 2050 global
power demand that
could be generated byrenewable energy, withsolar PV generating~43%, onshore windmills
generating ~26%,
and offshore windmills
generating less than ~7%

Growth in sustainable
fuels until 2035, driven
primarily by roadtransport, reaching 290
Mt in the Further
Acceleration scenario,
while aviation plays an
increasingly importantrole thereafter

Investment across
35 nuclear fusion
projects, focused on
tackling engineering
challenges

Growth in battery
demand by 2030,
driven mainly byelectrification ofmobility applications,which account for>90% of 2030
demandDecrease in battery
prices expected by
2030; however,offering bespoke
battery solutions tofulfill segment-specificrequirements
presents profitable
opportunity

Source: Global energy perspective 2022, McKinsey, Apr 2022; "Unlocking growth in battery cell manufacturing for electric vehicles," McKinsey, Oct 2021; Major risk or rosy opportunity, CDP Worldwide, 2019;
Science-based net-zero: Scaling urgent corporate climate action worldwide, Science Based Targets initiative, June 2022; McKinsey analysis

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Future of clean energy

What are the most noteworthy technologies?
Renewable energy
Solar PV and thermo-solar, wind, geothermal, nuclear
Solar
photovoltaics (PV)Maturity in tech has
driven down costs
below costs oftraditional fossil fuels(ie, vs coal)
Advancements in 3rdgeneration solar PVs areprimarily manipulating
semiconducting
materials (organics1 andperovskites2) at nanoscale to achieve higher
efficiencies

On- and offshore wind generation

Nuclear fusion

Wind power plants with larger rotors, blades, andheight are better suited to harvest lower windspeeds at higher altitudes

Fusion is the process of
combining atoms under hightemperatures and pressure torelease clean energy

Offshore plants (expected by 2025) faceengineering challenges (eg, marine
infra-structure); onshore turbines facenontechnical limits3
Wind turbines mounted on floating structures
allow power generation in water depths where
bottom-mounted structures are not feasible
Current global shift from single-turbine pilots tomultiturbine projects is expected by 2025 or later

Fusion power research is
could yield commerciallyviable plant within a decade,driven by advancements inmaterials research and AI, withcommercial launch of a
nuclear fusion plant expectedin the next decade3

H2-based fuels
Production of hydrogen as an energy
sourcePrimary methods forhydrogen production are
gray/brown (unsustainable,
being replaced), blue
(affordable, lower-carbon
alternative), and green (zerocarbon emissions) hydrogen4

Electrolyzers
Electrochemical energy
conversion technologies
convert water into greenhydrogen (sustainable energy
source), with the only
byproduct of the process being
oxygen (ie, zero carbon
emissions)

1Use of organic electronics for light absorption and charge transport. 2Hybrid (organic-metallic) semiconductor material composition tweaked to absorb broader light spectrum. 3Including transportation and infrastructure choke points, land use,view, birds, shadows, etc. 4More mature technologies include water electrolysis and steam reforming of biomethane/biogas with or without carbon capture and utilization/storage. Others include biomass gasification/pyrolysis, thermochemical
water splitting, etc.
Source: Andy Ridgway, “Why the promise of nuclear fusion is no longer a pipe dream,” BBC Science Focus, Dec 3, 2021; Philip Ball, “The chase for fusion energy,” Nature,
Nov 17, 2021; The global fusion industry in 2021, Fusion Industry Association and UK Atomic Energy Authority, Oct 2021; McKinsey analysis

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Future of clean energy

What are the most noteworthy technologies? (continued)
Energy storage
Battery tech, recycling, second use, long-term
storage, gravity-based energy storage, etc.

Energy distribution

Battery storage system

EV-charging infrastructure (EVCI)

Smart grid

Lithium-ion batteries’ price declined>90% in past decade, and they can only
shift energy for <8 hours without becoming
very expensive and incurring issues with
their high self-discharge rate
Other solutions (ie, long-duration energy
storage, gravity-based energy storage) are
required for weeks or months of storage

Extensive networks of stations boost
the accessibility and speed ofrecharging EV batteries

A smart grid is an advanced, intelligent
electric-grid system that can provide realtime insights and control for the distribution
gridSource: McKinsey analysis

EVCI hardware includes grid and site
electrical upgrades, on-site energy storage,
and charger unit
EVCI software and services include
energy management, electrical installation,
operations and maintenance, and customer
apps

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Future of clean energy

What disruptions could renewableenergy enable in the electric power,
natural gas, and utilities industry?
Technology
Solar PVCapabilities required
Cost-efficient manufacturability withimproved stability/reliability would
accelerate scaling of solar panels
globally

On- andoffshore wind
generation

Ability to generate power efficiently in
low-wind scenarios could unlock newsites for wind energyLong-durationenergy storage

More efficient energy storage
capabilities are required, given
increased solar and wind power
generation; often, power demand andsupply don’t match simultaneously,
especially in “off seasons” when solar orwind farms produce little energy

Smart grid

Changes to grid operation andinfrastructure to optimize supply-side
responses to demand in real time; eg,
augmented integration of distributed
renewable energy resources and reduced
reliance on fossil fuels

Source: Global energy perspective 2022, McKinsey, Apr 2022; McKinsey analysis

Key disruptions enabled

Net-zero power
Targets set by developed economies for 2040 and by
emerging economies for 2050

80−90%
Share of 2050 global energy mix to be sourced fromrenewable generation

8×
Growth in annual solar PV capacity installations (gigawatts
per year) from 2020 to 2030 in a 1.5ºC pathway

5×
Growth in power generated via onshore wind energy from2016 to 2030

Access deep-water regions
Ability to access new sites (where water depth is ≥60
meters) for development of offshore wind parks by not
requiring solid foundation
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Future of clean energy

What disruptions could hydrogen
enable in the electric power, naturalgas, and utilities industry?
Technology
Hydrogenbased fuels

Capabilities required
Drastic reductions in production
costs, coupled with infrastructuredevelopment (to enable adoption), arerequired to scale hydrogen production
across a wider set of applications
Lower production costs ofelectrolyzers must be paired withhigher efficiency to improve hydrogendensity, purity, lifetime, etc
Dispatchable electrolyzers will allow
for the integration of moreintermittent renewable energy
sources in the system

Additional enablers include greater regulatory clarity,
government decarbonization commitments,1 and deployment oftransport and storage infrastructure
1About 40 countries already have dedicated hydrogen strategies in place (eg, French government’s target of 10%

green hydrogen use in industry for 2022 and 20–40% for 2027).
Source: Global energy perspective 2022, McKinsey, Apr 2022

Key disruptions enabled~28%
Share of final energy consumption could
be met by green hydrogen by 2050

5×
Growth in hydrogen demand by 2050, driven
primarily by road transport, maritime, and aviation~0.5 Gt
Carbon abatement by 2030, reaching 2.5 Gt by
2050, which is particularly critical for some hard-toabate sectors (eg, iron and steel production,
chemical and refining, long-haul trucks, cargo ships)~65%
Share of hydrogen supply mix coming from greenhydrogen by 2035—and up to ~80% by 2050
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Future of clean energy

What other industries are most affected by the trend?
Other industries are experiencing implications of clean-energy tech, primarily focused on supporting the clean-energy
transition, meeting changes in resource demand, and shifting value pools

Industry affected

Implications of technology trend

Metals andminingGrowing demand for specific raw materials (eg, copper forelectrification, lithium and cobalt for batteries)

Oil and gas

Decarbonizing upstream operations and exploring alternative lowcarbon technologies and shifting value pools (eg, hydrogen) byleveraging strengths in access to capital and operational expertise

Construction
and building
materials

Constructing additional transmission and distribution infrastructure
to enable the delivery of electricity generated by renewable sources towhere it is demanded

Chemicals

Increasing demand for chemicals needed for the production of renewables (eg, silicon for the developmentof photovoltaic cells)

Public andsocial sectors

Prioritizing clean energy on governments’ agendas by providing greater regulatory clarity, government
decarbonization commitments, investment incentives, among other actions

Source: McKinsey analysis

The construction industry is
involved in decommissioning
fossil-fuel assets (eg, coalmines, fossil-fuel power plants)
and environmental
remediation of industrialsites and infrastructure for theenergy and utilities, oil andgas, and mining sectors

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Who has successfully created impact withclean-energy technologies?

Industry

Future of clean energy

Case example
Electric
power,
naturalgas, andutilitiesØrsted, a Danish energy company, committed to reducing greenhouse-gas
emissions from energy production by 96% from 2006 to 2023 through building>1,000 offshore wind turbines, reducing offshore wind technology costs by>60% since 2012, and reducing coal consumption by 82% in power stations
since 2006 by switching to sustainable biomass, among other actions. Ørsted alsodivested its oil and gas business to focus on expanding its international renewableenergy operations
Iberdrola, one of the world’s largest utilities (by market cap), aims to reduce all
emissions 43% by 2030 (from 2017) and achieve carbon neutrality in Europe by2030 and globally by 2050; key actions include drastically increasing renewablecapacity and increasing investments in smart grids and green hydrogen forindustrial use

Source: Company websites; Science Based Targets initiative

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Future of clean energy

What uncertainties must be resolved for the trend to achieve scale?
Not exhaustive

Renewables

Hydrogen production

Electrification

Energy storage/smart grids

Cost-efficientmanufacturability isrequired to acceleratescaling of solar-power
and wind generation techSignificant cost reductions ingreen hydrogen production (eg,electrolyzers) are needed toscale

High production costs (eg, EV
battery pack currently is 30–40%
of total EV cost) are expected todrop as consumer demand
accelerates by 2030, unlocking
economies of scale

Long-duration energy storage
technologies remain under R&D,
requiring major leaps in the short run
and continuous innovation in the longrun to optimize costs and storage
duration

Current limited distribution ofEV-charging infrastructureneeds scaling to accelerate EV
adoption

Smart grids face integration, costly
installation, and deployment
challenges that require further
research investments

Higher capacity,
stability, and reliability
are needed in solar PVs
and on- and offshore
wind generation plants
Supply chain risks
persist amid global
economic uncertainties

Higher production efficiency inelectrolyzers is crucial to
improve hydrogen density, purity,and lifetime
Hydrogen use is currently
confined to a few sectors,
pending wider applications

The slow pace of infrastructuredevelopment inhibits adoption

Overarching uncertainties include supply chain risks amid global economic uncertainties, as well as insufficientregulatory clarity on decarbonization commitments, renewable-energy requirements, and uncertain carbon pricing
Source: McKinsey analysis

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Future of clean energy

What are some topics of debate related to the trend?
Not exhaustive

1. Will traditional renewables be
outpaced by newer technologies?
Supportiveview

Opposing
view

Source: McKinsey analysis

2. Is it feasible to switch to 100%
renewable energy?

3. Will business opportunities inclean tech continue to grow?

Solar and wind renewables are “battle
tested,” with clear business case andcost advantageThe long-term cost of renewable energy
is competitive or lower than today’s
energy sources

The global consensus is for clean
energy, with initiatives beyond investing(eg, emission penalties, mine shutdowns)

Solar and wind capacity is expanding







Solar and wind costs are decreasing
every year

Solar, wind, and geothermal capacity is
expanding every year

B2C market is growing as consumers
increasingly favor sustainable products

Fossil fuels have both environmental
costs and national security risks insome countries



B2B market is growing as businesses
are anticipating sustainability regulations
and seeking energy and cost savings

Hydrogen is attracting significant
investments and already hascommercialized use cases



Most renewable energy sources areintermittent; therefore, storage systems
remain a bottleneck

Many tech projects remain costly interms of capital expenditures and bearhigh execution risks



Nuclear power could become more
attractive, thanks to new yet untested
designs that would reduce environmental and national-security risks

In some countries, 100% renewableenergy is more difficult to achieve, since
it depends on one’s resources

Some technologies will prove to be more
effective than others; not all clean techwill remain viable

Fusion power may or may not become
scalable in the foreseeable future

Political forces and incumbent players
may stifle the transition

In 2006–11, a clean-energy bubble
burst

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Future of clean energy

Additional resources

Knowledge centers

Insights on the net-zero transition
Innovate to net zero

Related reading
Global energy perspective 2022
The net-zero transition: What it would cost, what it could bring

An AI power play: Fueling the next wave of innovation in the energy sector
Decarbonizing the world’s industries: A net-zero guide for nine key sectors
Failure is not an option: Increasing the chances of achieving net zero

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McKinsey
Technology Trends
Outlook 2022
Future of mobility
August 2022

Future of mobility

What is the trend
about?
Mobility is undergoing its“second great inflection
point”—a shift towardautonomous, connected,
electric, and smart (ACES)
technologies
This shift promises to disrupt
markets while improving
efficiency and sustainabilityof land and air transportationof people and goods

Source: McKinsey analysis

Mobility is defined by several arenas across 4 disruptive dimensions ofmobility (ACES) and adjacent technologies that enable more sustainable
and efficient transportation
ACES
Autonomous
technologies

Adjacent technologies
Connected-vehicle
technologies

Lightweighttechnologies

Automated systems withsensors, AI, and analyticalcapabilities able to makeindependent decisions based on
the data they collectEquipment, applications, andsystems that use vehicle-toeverything communications toaddress safety, system efficiency,
and mobility on roadways

Incorporation of new materials (eg,carbon fiber) and processes (eg, engine
downsizing) to boost fuel efficiency andimprove transportation sustainability

Electrification
technologies

Smart mobility
solutions

Value chain
decarbonization

Solutions replacing vehicle
components that operate on aconventional energy source withthose that operate on electricity

Hardware and advanced digital/
analytics solutions enabling use ofalternative forms of transportation(eg, shared-mobility solutions) inaddition to or instead of owning a
gas-powered car

Technical levers to abate emissions
from materials production and end-toend manufacturing process andincrease use of recycled materials
across the value chain

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Future of mobility

Why should leaders pay attention?
Physical supply chains are vital to many industries, and today transportation is at a major inflection point, as mobility
ecosystems are simultaneously affected by regulation, shifting consumer preferences, and technology disruption

1. Regulation is enabling a mobility revolution

2. Consumers are accepting new mobility solutions

Carbon targets
and subsidies

Urban accessregulation

Alternative ownershipmodelsGreener attitude

50%

150+

2/3

60%

Amount by which emission targets for 2030
could be tightened by the EU

Number of EU cities with accessregulation for low-emission vehicles andpollution emergencies

Portion of US consumers expecting their useof shared mobility to increase in next 2 years

Year-over-year increase in inner-city trips
with shared bikes and scooters (136 million
trips in 2019)

3. Technology disruption is happening at an unprecedented pace, and availability challenges remain
Autonomous driving

Connectivity

Electrification

Smart mobility

8x

6 months

1:1

50%

Increase in average annual investments inautonomous vehicles over past 5 years

Length of delays in some recent vehicle
launches due to software integration issues

Cost parity for small EVs1 with ICE2 today,with fuel-cell parity expected by 2030

Portion of miles traveled with sharedtransportation modes expected by 2030

1Electric vehicles.
2Internal combustion engine.

Source: McKinsey analysis

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Future of mobility

What are the mostnoteworthy
technologies?

ACES

Autonomy

Connected vehicle

Electrification

Smart mobility

Radar and camera

Infotainment

Digital twin

Lidar
Steer/brake/shift-bywire

Vehicle-toinfrastructure (V2I)
connectivity

Lithium-ion battery(LIB)

Transportation
demandmanagement(TDM)

HD maps plus SLAM1

Cybersecurity

Battery analytics

Beyond LIB

Object detection

Hydrogen fuel cells

Driving strategy

Hybrid propulsion

Hardware Software/AI

Adjacent tech

Lightweight technologies

Value chain decarbonization

Advanced composites

Green primary materials

Advanced ceramics

Parts and materials circularity

Metamaterials
Nanomaterials
1High-definition maps and simultaneous localization and mapping.

Source: McKinsey analysis

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Future of mobility

What are the most noteworthy technologies? (continued)
HardwareTech cluster
ACES

Technologies

Description

Radar and camera

Sensor with algorithms to automatically detect objects, classify them, and determine the distance from them

Lidar

Range detection system relying on light travel time measurement

Steer/brake/shift-by-wireElectrical or electromechanical systems for vehicle functions traditionally achieved by mechanical linkages

HD maps plus SLAM

Simultaneous mapping and localization solution to map out unknown environments

Object detection

Perception technologies used for behavior planning, route planning, motion planning

Driving strategy

Solutions integrating hardware and software components in a full-stack autonomous vehicle

Infotainment

In-vehicle infotainment solutions (eg, augmented reality, voice recognition, and gesture control)

V2I connectivity

Software and hardware enabling vehicle-to-infrastructure (V2I) connectivity

Cybersecurity

Security solutions to protect connected cars and commercial vehicles against cyberattacks (eg, encoding)

Digital twin

Real-time virtual model of a system or process mirroring key attributes of the existing power infrastructure

Lithium-ion battery (LIB)

Advanced battery technology that uses lithium ion as a key component of its electrochemistry

Beyond LIB

Sodium-ion (Na-ion) and potassium-ion (K-ion) batteries, which might solve the resource issues facing LIBs

Battery analytics

Intelligence to extend battery life, improve manufacturing, unlock end-of-life markets, prevent safety hazards

Hydrogen fuel cells

Propulsion system where energy stored as hydrogen is converted to electricity by the fuel cellHybrid propulsion

Propulsion system including several propulsion sources used either together or alternately (eg, fuel–electric)

Smart mobility

Transportation demand
management (TDM)

Solutions optimizing use of locally available transportation resources to incentivize transition to more efficient andsustainable modes of commuting

Lightweighttechnologies

Advanced composites

Polymer matrix composites with unusually high strength or stiffness (eg, carbon fiber)

Advanced ceramics

Advanced composites such as carbon-fiber-reinforced plastics, which could substitute for steel

Metamaterials

Materials measuring 10–100 nanometers in at least 1 dimension (eg, graphene or carbon nanotubes)

Nanomaterials

Engineered materials that have properties not found in nature and that can modify wave properties

Green primary materials

Green steel, carbon-reduced production technologies, green aluminum, and green plastics 1

Parts and materials circularity

Reuse, refurbishment, remanufacturing of modules or parts, and recovery of high-quality materials from end-of-life
vehicles and other products to enable low-carbon vehicle production

Autonomous

Connected
vehicleElectrification

Adjacent
tech

Software/AI

Value chain
decarbonization

1Green steel is made with mass balancing or innovative technology. Carbon-reduced production technologies include using direct reduced iron (DRI) and an electric arc furnace (EAF). Green aluminum is made with more widespread use ofrenewable electricity in smelters and multiple technology innovations flushing out most of the residual production emissions over the next decade. Green plastics include those made from bio-based feedstock and electrified production assets.
Source: McKinsey analysis

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Future of mobility

What disruptions could the trend enable?
Ground transportation
With driver

Passenger
transport

Advanced driver assistancesystems (ADAS), ie,autonomy level of L2 andbelow1
Dynamic shuttle services/pooled e-hailing

Air mobility
Autonomous

CrewedUncrewed

Vertical takeoff and landing (VTOL) air taxis
Autonomous vehicles (eg,Level 3 or higher
autonomy,1 robo-taxis)

Passenger
transport

Wingless multicopters
Supersonic/hypersonic airtransport

Hyperloop

Peer-to-peer mobility(including car sharing)Transportof goods

Autonomous trucks
Same-day delivery
Trucking marketplace2

Last-mile delivery solutions(eg, last-mile robots on roador sidewalk)Transportof goods

Conventional air freight withnovel propulsion

Unmanned aerial vehicles,
such as freight or delivery
dronesUnmanned traffic
management systems

1Autonomy is categorized across level of supervision needed: L1 is execution of steering and acceleration/deceleration; L2 is monitoring of driving environment; L3 is fallback performance of dynamic driving tasks; L4 is system capability(ie, driving modes).
2With AI to manage logistics networks and fleet parks.
Source: McKinsey analysis

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Future of mobility

What industries are most affected by the trend?
Among the most affected industries are automotive and assembly; aviation, travel, and logistics; and telecommunications

Industry affected

Implications of technology trend

AutomotiveandassemblyAviation,travel, andlogistics



Improvements in operational setup, with higher asset utilization, increased flexibility, improved safety
New business models, as asset ownership may shift from small carriers to large integrators
Shift of volume from rail to road as cost advantage shifts to longer distances with autonomous trucks
Improved efficiency of public transport from dynamic shuttle services and pooled e-hailing

Telecommun-ications

Significant pressure for higher bandwidth as mobility fuels exponential growth in data traffic and for global coverage to meetthe need for vehicles to be connected everywhere, at all times
New opportunities for telcos to monetize value-added services (eg, by combining core connectivity with vehicular technologies
and real-time mobility data)

Aerospaceand defense

Source: McKinsey analysis










Changing pockets of growth as a revolution in urban mobility creates a shift from personal ownership to shared vehicles (global
vehicle sales volume is at best projected to remain constant through 2030)
Exploration of new mobility verticals and operating models to take part in the novel mobility solutions arena
Drastic increase in OEM market entrants after decades of primarily mature-player presence
Increased investment in tech R&D and ecosystem partnerships (revenues from ACES may account for 1/5 of OEM value poolby 2030)

New modes of aerial transportation of passengers and goods (eg, aerial autonomy for freight delivery, small size VTOL enabling
air taxis) which will expand aviation use cases
Novel propulsion drastically changing unit economics
Security pressure as in-vehicle systems and connected infrastructure are more exposed to security threats
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Future of mobility

What industries are most affected by the trend? (continued)
Diverse stakeholders across industries are experiencing second-order implications of novel transportation
technologies. Disruption is primarily driven by macroeconomic impact, changes in resource demand patterns, novel
modes of transportation, and changes to vehicle ownership models, as well as shifting value pools
Industry affected

Implications of technology trend

Metals and mining

Change in material usage patterns (eg. steel for new powertrain types) and increased demand for sustainable materials (eg. green
steel, green aluminum)

Electric power, naturalgas, and utilities

Need for more generation capacity and for reinforcement of transmission and distribution networks to meet increased demand forelectricity from EVs

Information technology
and electronics

Increased demand for solutions enabling, supporting, and integrating technological advancements across ACES

Financial services

Change in claims portfolio (eg. impact of increasing car safety with ADAS and autonomous-vehicle systems)

Oil and gas

Change in demand for gasoline and diesel once EVs reach critical scale

RetailNovel modes of delivery with airborne drones

Public and social
sectors

Changes in city infrastructure from sustainability-focused regulation promoting smart mobility

Source: McKinsey analysis

Revisions to land-use planning (eg. autonomous vehicles and shared mobility reducing the need for parking lots)
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Future of mobility

What are some use cases for the technologies that drive this trend?

Function-specific use cases

Industry use cases

Function affected

Technology use caseIndustry affectedTransportationof goods








Transportationof people

Source: McKinsey analysis

Technology use case

Autonomous trucks inlong- haul supply chain
Freight drones for lastmile delivery
Supply chain
optimization solutions
enabling same-day
delivery
Trucking marketplaces
for efficient freight
management

Electric power, natural  Vehicle-to-grid systems (in which EVs return excess
electricity back to the grid or throttle their charging rate)gas, and utilities

Novel mobility services
such as robo-taxis
Purpose-built vehicles
with longer durability(eg, designed
specifically for sharedmobility)

Financial services

Information
technology
and electronics



Public and social
sectors









Media andentertainmentSoftware/AI solutions for simultaneous mapping andlocalization, object detection, driving strategy
Hardware for autonomous vehicles (eg, lidars, cameras)
Mobility-as-a-service for integrated commuterexperiences across public transit, ride sharing,and micromobility
Congestion pricing (ie, dynamic pricing based on traffic)
Personalized insurance rates based on driving patterns
from connected-vehicle data
New insurance use cases for autonomous vehicles (eg,insurance for vehicle intelligence)
Novel ways of engaging a passenger during commute

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Future of mobility

Who has successfully created impact with these technologies?
Mobilitytechnology

Examplecompany

Aviation, travel,
and logistics

Autonomous trucks

UPS

Automotive andassembly

Advanced driverassistance systems

BMW

Safer driving: BMW’s Driving Assistance package cut property damage
claims 27%, bodily injury claims 37%, and collision claims 6%2

Telecommunications

Connected vehicles

Deutsche Telekom

New revenue streams: DT is actively codeveloping connected-vehicle
solutions in partnership with OEMs and identifying new customer
connectivity needs (eg, Wi-Fi hotspot within BMW ConnectedDrive)

Electric power,
natural gas, andutilities

Electrification ofvehiclesE.ON

Business diversification: In 2016, E.ON established a business unit to
expand EV-charging infrastructure in the EU, signaling a strategic focus one-mobility

Information
technology andelectronics

Smart mobility

The Routing Company

Dynamic public transit: TRC offers an on-demand vehicle routing andmanagement platform for cities to power the future of public transit

Metals andminingLightweight materials

General Motors

Efficient aviation: “Microlattice” metal, codeveloped by Boeing, Caltech,GM, and UC Irvine, is reported to be 100x lighter than Styrofoam but
strong enough to be used in structural components of airplanes 3

Industry

TuSimple

CaltechBoeing

Disruption caused by technology
Environmental benefits and fuel savings: TuSimple partnership withUPS North American Air Freight has delivered >13% fuel savings,1 withpotential to lower customers’ freight costs significantly

UC Irvine
1Savings achieved when operating in the optimal long-haul operating band of 55–68 miles per hour.

| 2Package includes forward collision and lane departure warnings, autobraking, and adaptive cruise control.

3In the case of the Boeing 787-9, which burns approximately 5,400 liters of fuel per hour, a 10–12% improvement in fuel economy amounts to 540–650 liters saved per hour.

Source: McKinsey analysis

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Future of mobility

What should leaders consider
when engaging with the trend?

Benefits

Risks and uncertainties

Cost savings from supply chain improvements

Safety and accountability concerns surrounduncrewed and autonomous mobility technologies

Market expansion from reaching new customer
segments in otherwise unserviceable locations or withimproved delivery speed
Sustainability as new modes of ground and air mobility
prioritize electric, hydrogen-based, or hybrid propulsion

Technology uncertainties about batteries with
sufficient range to support more applications (such as
air mobility) may hinder greater adoption
Customer perceptions of noise and visual impact
remain in play

Equipment and infrastructure costs are factors for
new modes of transportation
Regulation shifts will occur as mainstream certification
frameworks are developed

Privacy and security concerns for underlying
algorithms and workflows must be addressed
Source: McKinsey analysis

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Future of mobility

What are some topics of debate related to the trend?
Ground transportation

Air mobility

What share of vehicle sales will autonomous vehicles account for?
1 Marketpenetration andtiming ofautonomy

While autonomy offers significant benefits (eg, reduction in traffic deaths, improvements in fuel economy), widespread adoption may be hindered bysafety concerns (eg, several high-profile accidents), data protection issues, high upfront costs (vehicles and infrastructure), and insufficient regulation

2 Future of smart How will future-of-mobility trends shape cities?
mobility incities3 Impact ofshared mobility

Smart mobility reduces traffic congestion and air and noise pollution, and it improves safety, speed, and cost of travel; however, urban infrastructureplans are often criticized for imposing heavy investment requirements and creating security/privacy concerns

Will advancements in shared mobility deliver on hoped-for financial and environmental impact?
Shared mobility has not yet proved its long-term economic viability, as many operators struggle with profitability; further, shared mobility must prove its
sustainability impact as a full replacement for private cars, with an associated shift away from private-vehicle ownership (rather than its primary role
today as an extension of private vehicles, thereby increasing the vehicle fleet)for new
What scale will advanced air mobility achieve in the next decade?
4 Timing
aerial modes oftransport

While air mobility enthusiasts project that over the coming decade (or soon after), an electric aircraft could become a popular mode of transportation
and a viable alternative to traditional taxis, few players have so far managed to bridge the engineering-to-scale chasm, overcome product and businessmodel uncertainties, or bend customer perception challenges related to noise and visual aesthetics

and When should customers expect affordable advanced air mobility solutions?
5 Sustainable
inclusive air
mobility
Source: McKinsey analysis

Novel, subscale modes of aerial transportation with a premium price tag may become available to customers in the next decade, but the industry may
take significantly longer to scale and bend the cost of a short-haul flight to the equivalent of a taxi ride
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Future of mobility

Additional resources

Knowledge center

McKinsey Center for Future Mobility

Related reading
Mobility’s second great inflection point
The future of mobility is at our doorstep
Advanced air mobility in 2030
Reimagining mobility: A CEO’s guide
The zero-carbon car: Abating material emissions is next on the agenda

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McKinsey
Technology Trends
Outlook 2022
Future of space technologies
August 2022

Future of space technologies

What is this trend about?
Moving down the cost curve has unlocked use cases that werepreviously cost-prohibitive

Key trends

Heavy launch1

GEO2 communications satellites1

Cost per kg to low-Earth orbit (LEO), $ thousand

Cost per gigabit, $ million

70

4.0

60

3.5

Planned

2  One of the drivers of costeffectiveness has been the reductionof size, weight, power, and cost
(SWaP-C) of satellites and launchvehicles

3.0

50

2.5

40

2.0
30

1.5

20

1.0

10

0.5

0
1980

85

90

95

2000

05

10

15

2020

0
2010

2012

2014

Year of first successful orbital launch
1.

2.
3.

1  The largest shift in space tech overthe past 5–10 years has been theacceleration down the cost curve,
which is increasingly unlocking new
capabilities, use cases, and users
for space tech and satellite data andscaling accessibility

2016

2018

2020

2022

2024

Launch year33  SWaP-C reduction has led toarchitectural shifts, eg, fromindividual, large GEO satellites tosmaller, distributed LEO satellites

Figures reflect estimates only, based on analysis using publicly stated information and expert estimates.
Satellite lifetime not factored into cost per gigabit.
Geosynchronous equatorial orbit.
Launch years are actual or planned per company announcements.

Source: Ryan Brukardt, Jesse Klempner, Daniel Pacthod, and Brooke Stokes, “The role of space in driving sustainability, security, and development on
Earth,” McKinsey, May 2022

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Future of space technologies

Why should leaders pay attention?

>$1 trillion estimatedmarketIncreased participation globally

Significant cost
reductions

New businessmodelsIncreased focus
on software>10% estimated annual
growth by 2030 in thespace market from avalue of ~$447B today>1,400 companies
involved in the new spaceindustry, fromgovernments to start-ups,
which are expected to
grow from 600+ today to
1,000+ in 2030

Significant cost
reductions are alreadyoccurring, and further
reductions in launchand return-to-Earthcosts could enable
even more disruptive
applications, such as
space mining andcommercial humanspaceflight

New business models,
including vertical
integration required tomeet increased
demand, are driven by
movement toward
value-added
services, given higher
margins

Value-added services
necessitate a higher
degree of digitalapplications (eg,autonomous landing oflaunch vehicles, AI
delivering real-timeinsights to clients)~15% CAGR up to 2030
estimated for Earthobservation information
products, data analytics,
data downlink, and space
tourism and travel, all ofwhich are expected todrive long-term demand1

New entrants are
moving faster thanlegacy players byfocusing on first-moveradvantages andcommercial
opportunities

1“NSR’s global space economy report projects $1.25 trillion in revenue by 2030,”

Northern Sky Research (NSR), January 2022.
Source: “The role of space in driving sustainability, security, and development on
Earth,” McKinsey, May 2022

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Future of space technologies

What are the mostnoteworthy technologies?
Nonexhaustive

Technological advancements and the reduction of size, weight, and power of satellites and launch vehicles have contributed to costeffectiveness, making new space applications more economically feasible

Satellites

Architectural shift

Application of new technologies

Industrialization of assembly

LEO constellations

Higher computing power leveraging consumer
processor tech across distributed satellite
networks to support data collection fromincreasingly high-resolution sensors

Design for modularity: Manufacturing
approach that enables faster design,development, and assembly via cube-sat
architectures (built using standard dimensions,ie, units of 10 × 10 × 10 cm) used as
extendable building blocks

Low-Earth-orbit (LEO) satellites,
which orbit close to Earth’s
atmosphere (altitude 300–2,000 km)

Less expensive, higher-resolution sensors that
conduct observation of their targets (eg, Earth,planets), typically using passive observation inseveral spectrums (eg, optical, infrared) and active
sensors (via radar)Less costly, more efficient power systems using
smaller, lightweight solar panels and more efficientbatteries, allowing small (cube) satellites to havegreater power availability for expanded missions
Greater capabilities in a smaller size,
weight, and power (SWaP) package, enabling
new missions

Shift from job shop to assembly line: In
response to increased demand (for proliferated
constellations) and investment in facilities,changes in satellite production from one-off,hand-built examples to a more industrial
process
Democratization of production: Lower costs
due to new manufacturing processes,
including additive manufacturing and modular
designs, enabling market entry of new players

Source: Evangela Rodgers, “With satellite internet, network capacity is key,” Viasat, Sept 2021; Katja Lenz, “Smartphone technology provides satellites with increased computing power,” German Aerospace Center (DLR), June 2022

Enabled a shift
in architecturefrom individual
GEO satellites
to proliferated
architectures in
LEO

Proliferation in number ofactive satellites, from ~4,100 in
2021 to ~2,700 in 2020, with a focus
on mega-constellations using smaller
satellites
Pros: Increasingly dense coverageand capacity globally, lower latency,
higher flexibility and revisit
To learn more about this technology, see“Advanced connectivity,” McKinsey
Technology Trends Outlook 2022,
August 2022

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Future of space technologies

What are the mostnoteworthy technologies? (continued)
Nonexhaustive

Other emerging technologies will build on the sector’s transformation over the previous decade

Communications

Digital capabilities

Deep-space
exploration

Laser communications

Edge computing and AI

Nuclear propulsion

In-orbit servicing

Laser links would allow
satellites to communicate
using pulses of light for datatransmission

With the growing launch ofsatellites and spacecraft for
activities such as Earthobservation, higher volumesof data will be collected,
introducing a need for edge
computingNuclear thermal/electric
propulsion could propel
spacecraft at higher speeds
for longer distances, enabling
deep-space exploration1

Satellite refueling/mods: Satellites refuel or modify
satellites in orbit to extend mission lifetime andcapabilities and to reduce replacement costs

Potential exists to increase
data transfer speeds by
100x–1,000x vs traditional radio
frequency
Ability to direct laser emitted
to very specific locations(both to satellites in space andground stations on Earth),which mitigates coverageoverlap and interference

Edge computing allows forprocessing data closer to thepoint of collection in thecloud, leveraging AI andmachine learning
capabilities, reducing latency,
and saving bandwidth to delivernear-real-time insights

Technological advancements
are optimizing performance andreliability while improving
affordability to enable acadence of more frequent
launches
Currently in R&D; may carrysafety risks, and most missions
don’t have a need for rapidtransit that would justify it

Operations

Orbit Fab developed end-to-end refueling using its
Rapidly Attachable Fluid Transfer Interface (RAFTI), a
fueling port that can also be used as a drop-in
replacement for existing satellite fill-and-drain valves
Orbit repositioning involves raising the orbit orchanging the inclination of a satellite
Launch: Reuse of booster structures, engines, orotherwise, coupled with technology advancements (eg,material sciences, computer-aided design, 3-D printing)and increases in launch rate, are contributing to a
reduction in operational costs and an increase inaccessibility to space
End-of-life disposal is pulling space debris to reenter
Earth’s atmosphere for disposal, reducing collision risks

1As opposed to current chemical and solar electric propulsion technologies, which suffer from significant energy inefficiencies.

Source: Stew Magnuson, “Laser communications to thwart jamming, interception,” National Defense, Nov 2014; Daniel Baird, “LCRD: Your Questions
Answered,” The Invisible Network Podcast, NASA, Apr 2022

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Future of space technologies

What disruptions could the trend enable?
Enabled by remote sensing
Advancements in applications of Earth observation data
Forestry
Commercial forestry (inventory
and mapping applications)
Reconnaissance mapping
Environmental monitoring

Hydrology
Soil moisture estimation
Flood mapping and monitoring
Irrigation scheduling andleakage detection

Agriculture
Crop type classification, conditionassessment, yield estimation
Mapping of soil characteristics andmanagement practices
Compliance monitoring (farming practices)

Land cover and use
Routing/logistics planning (eg,seismic activities, urban
expansion, resource extraction)
Target detection
Damage delineation

Geology
Mapping (eg, structural, terrain,
geologic unit)
Exploration/exploitation (eg,mineral, sand, and gravel)
Baseline infrastructure

Mapping
Planimetry/surface geometry
Digital elevation models
Baseline thematic/topographic mapping

Oceans and coastal
monitoring
Ocean pattern identification
Storm forecasting
Environmental evaluation (eg,fish stock and marine mammal
assessment, oil spills)

Sea and ice assessment
Tactical identification (eg,detection, tracking, navigation)
Shipping/rescue routes
Global-change monitoring (eg,
ice conditions, pollution
indexing)

Source: McKinsey analysis

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Future of space technologies

What disruptions could the trend enable? (continued)
The future space economy and human spaceflight could consist of activities not currently employed in space
today, enabled by drastic reduction of launch costs, AI applications in space, and advancements in powertransmission
Scaling humanspaceflightSpace economySpaceminingIn-orbit construction
and manufacturing

In-orbit power
generation

Cislunar activity

Mine asteroids and space
objects for materials toreturn to EarthSeeks to capitalize on thebenefits of zero gravity
and supply future spacetravelBuild space-based solar
power generator
leveraging 24/7 exposure
to sunlight to offset
emissions on Earth

Public- and private-sector
exploration missions anddevelopment of
infrastructure on thesurface and in orbit

Source: McKinsey analysis

Commercial
tourism
Scale paying customers tospace for short
experiences of zerogravity and Earth views

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What industries
could be mostaffected by the trend?
While applications forspace technologies arebeing developed acrossall industries, two areprimarily impacted:

Telecommunications
Providing broadband internet toplanes and remote areas, including
emergency backup coverage

Future of space technologies

Emerging use cases are being built across other industries,
especially as costs decline and accessibility increases
Electric power,
natural gas, and utilities
Monitoring methane emissions;informing development of sustainableenergy services; providing imagery ofmining sites

Agriculture
Monitoring soil, rainfall, andsnow cover to inform irrigation plans,predictions of agricultural output, etc

Automotive andassembly

Aviation, travel, andlogistics

Collaborating on lunar rovers;
enabling autonomous driving and incar entertainment

Tracking moving shipping containers;
providing positioning and navigation
information; monitoring temperatureof sensitive containers and roadcongestion

Consumer packaged
goods

Pharmaceuticals
and medical products

Experimenting in space under
specific space environment
conditions to inform design andmanufacturing of sneakers, soccerballs, etc

Conducting experiments leveraging
microgravity (eg, protein
crystallization) to improvepharmaceuticals

Information technology
and electronics
Developing in-space computing
offerings

Financial services
Using radar satellite-based
flood-monitoring capability to informrisk management and tailor solutions;leveraging commodities geolocation
tracking (eg, vessels) to inform trades

Aerospace and defense
Providing satellite imagery for
navigation and monitoring to achieve
security and intelligence objectives

Source: “The role of space in driving sustainability,
security, and development on Earth,” McKinsey, May 2022

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Future of space technologies

Who has successfully created impact with space technologies?
McKinsey deployed remote-sensing analytics to unlock new insights across industries
Case examples (nonexhaustive)A

Field-level insights for agriculture input players

B

Optimized vegetation-trimming cycles around major
utility grids by combining lidar and high-resolution
optical images to map vegetation attributes

Used local agronomic data and various satellite
imagery to inform marketing strategy, identify growth
opportunities, match offerings to grower needs, adjust
to changing conditions

C

Commodity tracking and procurement

D

Helped companies that trade/process commodities toenhance their purchasing/trading activities throughearlier insights on drivers of supply and demand (eg,by predicting and monitoring refinery shutdowns or
port congestion)
E

Building and construction detection
Supported NGOs and public organizations by tracking
urbanization and building-level features (eg, to identify
unreported property development or, in post-disaster
relief effort, to identify damaged buildings via highresolution satellite images

Source: McKinsey analysis

Vegetation detection for utility players

Supply chain traceability and forest carbon
Helped consumer packaged goods (CPG) companies
verify their zero-deforestation or sustainable-sourcing
commitments by monitoring natural assets in key
production areasF

Oil and gas shale activity monitoring
Helped oil and gas companies monitor the life cycle of
shale oil exploration and production, including drillingand fracking events

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Future of space technologies

What are unresolved risks related to space technologies?

Cost-effectivenessGovernance

Cyberrisks

Space technologies must be cost-effective
for space services and human spaceflight to bescalableGovernance should encompass usage
rights and space activities

Risk and complexity of cyberthreats are
growing

Uncontrolled proliferation of all possible
space concepts increases the risk ofspectrum interference, physicalcollisions, etc

As dependency on space techincreases across different use cases,
the potential damage resulting from
exploitation of a cybervulnerability also increases

Trade-offs exist between more cost-effective(higher-risk) commercial technologies andhigher-performance, more reliable “spacequalified” technology
Careful risk assessment is required on theimportance of mission assurance/
accomplishment (eg, extensive use ofcommercial tech in constellations increases therisk of satellites dying prematurely and adding
to the space debris challenge)
Source: McKinsey analysis

Governance mechanisms need to better
define allocation of spectrum and orbit
usage rights in order to accommodate
the increasing number of players,satellites, and applications

Proliferation of commercial players
raises a question of whether all services
will be well protected from cyberrisks

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Future of space technologies

What are some
topics of debate
related to the trend?

How can leaders define rights and norms?
1 Spacemilitarization Governments recognize space as a war-fighting domain(eg, GPS jamming, antisatellite weapons), as demonstrated
by recent organizational changes (eg, space commands
established in France, Japan, UK, US)

Nonexhaustive

2 Legalconflicts

How can leaders define ownership and accessrights?

betweenstates

A key need for the sector is to reach a common
understanding about access rights and usage ofproperties and resources (eg, for Lagrange points,
spectrum, and minerals found in space); such rights can help
create a democratized setup whereby all can participate in thebenefits of space

Space debris
and traffic
management

Should LEO have limits?

3

Source: McKinsey analysis

As more companies access space, concern arises
regarding space debris, space traffic management, andcongestion (eg, uncertainty about what the ~27,000 pieces
of debris in space might hit and when)

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Future of space technologies

Additional resources

Related reading

The role of space in driving sustainability, security, and development on Earth
The potential of microgravity: How companies across sectors can venture into space
The future of space: It’s getting crowded out there
Expectations versus reality: Commercial satellite constellations
Look out below: What will happen to the space debris in orbit?

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McKinsey
Technology Trends
Outlook 2022
Future of sustainableconsumption
August 2022

Future of sustainable consumption

What is the tech trend about?
Sustainable consumption centers on the use of goods and services that are produced with minimal environmental impact, using low-carbon
and sustainable materials. Enabling technologies transform industrial and individual consumption to address environmental risks, including
climate change

6 main patterns reflect enhancements in conscious consumption
Low carbon

Reduce, reuse, and recycle

Biodegradable

Minimizing greenhouse-gas (GHG)
emissions over life cycle ofproduction, use, and disposal

Reusing materials previously used
in a product or created as amanufacturing by-product

Using materials that can be broken
down into chemical constituents inambient conditions (ie, landfill)

Waste conscious

Biobased

Nontoxic

Minimizing waste through optimized
consumption (eg, of water, plastic)

Prioritizing materials intentionallymade from substances derived fromliving (or once-living) organisms

Following processes that emit fewer
chemicals and environmental
pollutants during production and use

Consumption types
Industrial






Industry (eg, mining, chemicals)
Sustainable agriculture
Public and industrial transport
Commercial buildings

Individual Residential buildings
 Passenger transportation (eg, personal vehicles) Household consumption (eg, food)

Energy end use contributes to ~50% of GHG emissions, vs ~20% from energy supply and ~30% from non-energy-related emissions
Source: McKinsey analysis

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Why should leaders pay attention?
At a macro level, sustainability is no longer optional: 90% emission reduction paired with emission removal is needed to avoid anenvironmental crisis, creating a $4 trillion–$6 trillion addressable market focused on industrial and individual end use by 2030
For companies, production of sustainable goods and services can support compliance with emerging regulations, create growth
opportunities, and help attract talent

Macro level

Micro level90%$4T–$6T

GHG emission reduction
required to comply with
1.5oC pathway1

global sustainability
addressable market by
2030 focused on industrial
and individual end use

Applying bolder
sustainableconsumption
regulation

Shifting
customer
expectations

Talent moving
to sustainable
companies

First movers
capturing value

30%–50%

≤50%

80%

50%

corporate profits atstake (eg, fromcarbon pricing,sustainablepackaging and waste
regulations)‘green premium’customers are willingto pay for sustainably
produced products
and services across
B2C and B2B sectors

millennials want towork for a company
strong on
sustainability andother ESG2
dimensions

faster growth insustainable brands vsothers (eg, Unilever’s
Sustainable Living
Brands vs the rest of
the portfolio)

Global sustainability-addressable market$280B–
380B
$80B–
200B

$530B–
1,200B

Carbon management

Ag and land management

Water

Waste

Consumer

Buildings$850B–
1,200B$1,100B–
1,200B$1,300B–
1,800B$5,980B

1The 1.5°C pathway refers to goal of holding global warming to 1.5°C above preindustrial levels through achieving net zero by 2050 and halving carbon emissions by 2030.
2Environmental, social, and governance.

Source: Major risk or rosy opportunity: Are companies ready for climate change, CDP Worldwide, 2019; McKinsey analysis

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What are the most noteworthy technologies?
Many sustainable end-use solutions are past the initial proof of concept phase and innovating to become costeffective; the next economic battleground is to scale them over the next decades
Sustainable agriculture;
alternative proteins

Natural capitaland nature

Micro-irrigation; vertical farming; hydroponics;
plant-based and cultured meats; methane
inhibitors; green fertilizers

Technologies for restoration of forests and natural
ecosystems; coastal vegetation; biodiversity;
freshwater basins; etc

Raw-material
consumption

Circular technologies

Green construction

Design; production; recycling and reuse;
waste management

Energy and water efficiency; waste reduction;eco-friendly materials use (eg, green cement,
green steel)

Sustainability
enablers forhard-to-abate
industries

Carbon capture
use and storage(CCUS)

Land
consumption

Capture of CO2
directly fromindustrial-emission
sources1

Carbon removals

Electrification technologies

Nature-based solutions(eg, tree planting);
engineered carbon
removal (eg, direct air
capture, biomass tocapture CO2 during
energy generation)

Electric heat pumps; electric
propulsions for terrestrial and aerial
vehicles (eg, magnetic levitation
trains); electric arc furnaces forsteelmaking; electrification of farmequipment

1Excluding bioenergy with carbon capture and storage (BECCS), covered under carbon removals.

Source: McKinsey analysis

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Future of sustainable consumption

What industries are most affected by the trend at present?
Industry affected

Implications from technology trend

Automotive andassemblyElectrification of global fleet, slowly replacing oil-powered internal-combustion-engine vehicles as costs, battery ranges, andcharge times improve

Agriculture

Digitally enhanced agronomy services (up- and downstream) for precision agriculture
Innovative agriculture technologies (eg, indoor, vertical farming, drip irrigation, GHG-focused animal breeding, gene editing to
improve carbon sequestration of plants)
Alternative proteins (eg, plant or microorganism based, cultured)

Aviation, travel,
and logistics







Fleet modernization (eg, electrification, vehicles with higher fuel efficiency)
Decarbonized fuels (eg, sustainable aviation fuel)Fleet dispatch and travel route optimization for sustainability (eg, shift toward more rail use)
Truck load optimization (eg, redesign of boxes, double stacking of pallets)“Green corridors” (trade routes among major port hubs where zero-emission solutions are supported)

Construction andbuilding materialsNovel building techniques (eg, insulation to lower space heating/cooling demand, electrification for small-carbon-footprint heating)
Increased use of sustainable materials (eg, green steel, recycled plastics)
Change in material usage patterns (eg, more scrap steel, less carbon-intensive materials)

Pharmaceuticals andmedical products

Optimized manufacturing processes to improve energy efficiency and reduce water consumption
Substitute traditional single-use plastics for more sustainable, recycled materials for packaging

Public andsocial sectors

Organizations can incentivize the market for sustainable goods and services and mandate shifts, boost innovation by securing
funding, and deliver important initiatives to other parts of the economy

Source: McKinsey analysis

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Future of sustainable consumption

What industries could be most affected in the long term?
Industry affected

Implications from technology trend

Chemicals

Growing markets for recycled plastics and specialty plastics created from captured CO2; conversion of CO2 into
polyurethane foam, displacing hydrocarbon that would otherwise come from fossil fuels

Oil and gas

Increasing adoption of carbon sequestration to support enhanced oil recovery (EOR); CO2 EOR technology injects CO2 into
partially depleted oil fields to force out additional volumes of oil, with CO2 being residually trapped and permanently stored

Metals and mining

Decarbonization of operations and offset of production activity effects on natural capital, as well as increasing production of
the minerals needed for clean energy and other sustainable tech

Electric power, naturalgas, and utilities

Variable demand for electric power based on sustainable consumption trends such as electrification

Consumerpackaged goods

Circular economy solutions and business models enabled by product optimization (eg, material selection, product/packaging
design); improved product and material flows (eg, optimized reverse logistics); and enhancements in recycling (eg, newmaterial recovery technologies); as well as rising demand for products with legitimate sustainability attributes

Aerospace and defense

Designing and manufacturing aircrafts that rely more on sustainable fuels and increased energy efficiency

Information technology
and electronics

Optimizing electric power consumption in data centers; powering data centers by renewable energy; and reducing waste
across the consumer electronics value chain

RetailGreen product sourcing; in-house facility management (energy, water, and packaging waste minimization); appropriate
management of customer returns/disposal of products

Real estate

Market shifts in response to changes in consumer preferences; urban planning; and infrastructure development

Telecommunications

Optimized energy consumption by upgrading to 5G, and networks operated with renewable energy

Source: McKinsey analysis

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Who has created impact with technologies enablingsustainable consumption?

Future of sustainable consumption

Relevant technologies are already enabling climate impact across a variety of industries; today’s main challenge remains scale

CCUS

Occidental Petroleum and Cemvita Factory launched a pilot project for conversion of captured CO2 tobioethylene; OxyChem can then use the bioethylene as feedstock, and resulting chlorovinyls are used in
manufacturing of plastics, including foams and PVC pipes

Carbon removal

Several start-ups, such as Running Tide and Kelp Blue, have introduced technologies that grow significant
amounts of seaweed, seagrasses, and algae through artificial farming and pregrown seeds, using CO2 to accelerate
their growth; the plants are then used to absorb CO2 or converted into food sources for fish and marine animals
Frontier is an advance market commitment by Stripe, Alphabet, Shopify, Meta, and McKinsey, to incentivize
accelerated development of permanent carbon removal by guaranteeing future demand

Green construction

ArcelorMittal is developing a series of industrial-scale hydrogen projects for use in steelmaking that will start todeliver substantial CO2 emission savings within the next 5 years

Natural capitaland nature

IKEA Systems includes biodiversity and deforestation considerations in its value chain partnerships (eg,supplier code of conduct), restricting business activities in areas of high conservation value and encouraging
suppliers to follow the lead

Alternative proteins andsustainable agriculture

Nutrien drastically reduced upstream emissions in fertilizer production, became a leader in blue ammonia/blue
nitrogen production, and created one of the industry's first and broadest carbon marketplaces for farmers

Circular technologies

The Hong Kong Research Institute of Textiles and Apparel (HKRITA) has partnered with Gap to develop ecofriendly production processes and technology solutions, with an initial focus on separation of spandex fromused garments and denim decolorization for recycling
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Future of sustainable consumption

What should leaders whenengaging with this trend?

Benefits

Risks and uncertainties

Operating savings in the long run: Cost-effective investments
for rapidly scaling end-use-focused clean technologies (eg,green construction)

Commercialization pathways for climate tech mean they aren’t
yet cost competitive with conventional tech

Early-mover advantage: Network benefits for companies thatjoin climate tech ecosystems early
Incentives: Support or guarantees for new technology takeoff
and increase in adoption (eg, green bonds, loan guarantees,
decarbonization subsidies)
Transparent industry standards: Mature clean-energy
standards in developed countries and global decarbonizationcommitments

Availability of critical input materials might be insufficient

Upfront and ongoing costs to decarbonize production facilities
and value chains are of concern (eg, green-steel production>40% more expensive than conventional)
Regulatory action and alignment of standards across borders
and regions will be important

Changes in consumer behavior, compared with statedcommitments, can affect sustainable consumption initiatives (eg,willingness to pay “green premiums”)

Vibrant carbon markets: Rapidly growing global markets forCO2 permits traded among clean-energy-ecosystem players
Source: McKinsey analysis

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Future of sustainable consumption

What are some topics of debate related to the trend?
Overall

1 Capital
reallocation toaccelerate
decarbonization

Land consumption

Material consumption

Sustainability enablers for hard-to-abate industries

How will companies and governments mobilize capital flows in support of sustainable consumption?
Estimated capital spending of ~$9.2 trillion per year (an annual increase of as much as $3.5 trillion from 2022) is required for a global
transition to a net-zero economy; ~85% of technologies needed to meet this target already exist, highlighting the importance of closing thecapital funding gap to deploy these technologies across sectors and geographies

2 Consumer
behavior shift

How will consumer mindsets and behaviors change? Where and how will they diverge or converge? What new
behaviors and habits will become mainstream?

of
3 Feasibility
sustainable

Is global adoption of sustainable agriculture practices feasible?

agricultureof circular
4 FutureeconomyMore than one-third of global consumers are ready to pay a green premium as demand grows for environmentally friendly alternatives;
however, attitudes vary across generations, countries, and industries; relative importance of sustainability during the purchasing process willcontinue to increase

Sustainable agriculture benefits the environment through helping maintain soil quality, reducing erosion, and preserving water; however,such practices are often hard to abide by for mass agriculture farmers, given implications for crop yields, particularly challenging in regionswith food security concerns

To what extent will circular-economy practices (those favoring the comprehensive recovery and reuse of materials withminimal losses of quality) replace conventional practices?
Current momentum in circular technologies is generating a seismic shift across manufacturing industries globally; however, an attempt toreach a 100% recyclability rate might prove counterproductive if the price of recovery remains higher than the value of the materials
recovered; furthermore, the existing regulatory landscape does not incentivize all ecosystem players to pursue a circular economyof
5 Balance
decarbonization
levers
Source: McKinsey analysis

What is an appropriate balance between carbon removal and other decarbonization levers?
CCUS is necessary in industries without other decarbonization alternatives and is already cost effective for some industrial processes;
however, investments in carbon removals may divert funds and attention away from the critical business of reducing emissions, further
propping up the fossil fuel industry
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Additional resources

Knowledge center
McKinsey Platform for Climate Technologies

Related reading
The net-zero transition: What it would cost, what it could bring
Delivering the climate technologies needed for net zero

Decarbonizing the world’s industries: A net-zero guide for nine key sectors

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August 2022

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