Executive
Perspectives

Artificial Intelligence:
Ready to Ride the Wave?
December 2021

BCG Executive Perspectives

AI HAS STRONG POTENTIAL TO REDEFINE INDUSTRIES

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

IN THIS DOCUMENTACT HOLISTICALLY TO HARNESS THE POWER OF AI
Ambition, strategy,
and desired outcomes

At BCG, we believe AI is one of the main forces that willtransform industries in the next decades—and we have made it
one of our largest investment areas.
Many companies have invested in AI to unlock its potential. However,
only 11% have released significant value—as the majority
have failed to scale beyond pilots, reimagine the way they work with data,
and redefine human-AI interaction.

This document gives insight into the main successfactors that corporates should consider when deploying AI
solutions.

Sources: BCG analysis and case experience

Collaborative
redesign withbusiness owners

Ecosystem
orchestration

Responsible AI
governance

Talentmanagement

Need to get all
ingredients right

Data and techcapability
building
Enterpriseagility2

High investment

Accelerated adoption

Positive team impact

Initial value release

Headwinds to manage

$58B

55%

87%

11%

32%

Global AI investment
in 20211

Of companies
accelerated their AI
strategy andadoption becauseof COVID-192

Of teams saw
improvements incollective learning
after implementing AI
solutions3

Of companies reportsignificant financial
benefits throughrevenue/cost
improvements from
implementing AI4

Of citizens express
concern thatsignificant ethicalissues in AI have notyet been addressed5

1. MIT-BCG 2020 Artificial Intelligence Global Executive Study and Research Project; 2. AI adoption skyrocketed over the last 18 months, Harvard Business Review, Harris Poll 3. Out of those teams that saw improvements in both efficiency and decisionquality after implementing AI (~58% of teams); The Cultural Benefits of AI in the Enterprise, BCG-MIT, November 2021; 4. Based on a global survey in spring 2020, attracting over 3,000 total respondents representing 29 industries and 112 countries; ourassessment of “significant financial benefits” uses a threshold that varies according to organization size. For the largest organizations in our sample, with revenues of more than $10 billion, passing this threshold requires more than $100 million inrevenue and/or cost improvements annually from the use of AI. For smaller organizations, the thresholds were lower: $20 million in improvements for organizations with revenues between $500 million and $10 billion, $10 million in improvements fororganizations with revenues between $100 million and $500 million (or nonprofits), and $5 million for organizations with less than $100 million in revenues; 5. The Citizen’s Perspective on the Use of AI in Government, BCG

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

AI is gaining momentum given potential, yet headwinds must be addressed3

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

Summary (i/ii) | AI: current state and trends

potential of AI has driven high growth, underpinned by focused investments in optimization, predictive
1 Strong
maintenance, personalization, and automation
Yet, only 11% of firms report significant financial value from AI.Those that do, deliberately reimagine the way
2 theywork with data and successfully move from pilots and proof of concept to scaling

3 A critical element is people, with high value unlocked in effective organizational learning and change management
CURRENTSTATE ANDTRENDS

4 Apart from driving financial outcomes, AI can also reignite team effectiveness, morale, & culture in the long term
positive outlook and growth, there are several dynamics to manage: labor market shifts and talent
5 Despite
shortages, data capability gaps, ethicality and transparency concerns, and expected increase in regulations

Sources: BCG analyses, experience, and publications

4Embed AI
in yourbusiness

Prepare youroperatingmodel for AI

1

Clearly articulate the ambition for value creation from AI, linked to your company's strategy andpurpose—develop a clear perspective of where AI will drive business outcomes2

Treat AI development as a business transformation: redesign collaboratively with businessowners, iterate, and improve based on human-AI learning

3

Learn from digital natives: Change your ways of working and operating model to enable enterpriseagility, continuously reimagine business processes, and effectively orchestrate ecosystems

HR early on: Anticipate AI impact on jobs and skill sets, embed in strategic workforce
4 Involve
planning and internal upskilling programs, rethink employee value proposition to retain talent
Decouple data capability building from core IT systems, embed flexibility via reshoring, and5 modernize iteratively
6

Sources: BCG analyses, experience, and publications

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

Summary (ii/ii) | AI: implications for leaders

Invest early in responsible AI governance, policies and standards, structural assessment, expert
staff, and issues response plan; provide transparency to stakeholders

Deep dives per implication—on pages 14-20

5

Context | AI comprises technologies complementing human capabilities tointerpret data, make decisions, and take actions

Artificial intelligence is enabled by tech-humaninteraction across three core buildings blocks

DATA

PROCESSINGACTION

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

1

AI has the power to support and enhance human capabilities,
beyond what is currently possible

1

Process big data in real time, without need for humans to input updated data

2

Achieve ultra-granularity, even while processing massive amounts of data

3

Improve continuously and autonomously through learning loops• Machine vision1• Info processing• Image generation• Speech recognition• Pattern recognition• Handling & control

4 Scale massively at a low cost, with computing power exponentially improving• Natural language
processing2• Learning from data

• Navigation &movement

5

Provide humans with actionable data difficult to comprehend otherwise

Strong growth: AI market expected to grow at 28%3 CAGR through 2023
1. Machine vision: methods to provide imaging-based automatic inspection and analysis, 2. Natural language processing: subfield of AI and linguistics, concerned with interactions between computers and human language;
3. IDC, Worldwide Artificial Intelligence Forecast, 2019–2023. Sources: BCG analysis, CB insights, IDC

66

AI growth underpinned by focused investments in four key
areas that are redefining industries and necessitating action

Four investment areas AI has revolutionized industries across variety of use cases
Example use cases

Example impact

Optimization

Travel | Optimized network planning and service quality with minimal operational
disruptions using passenger flows to adjust route structures and scheduling

27% opex reduction

Personalization

Consumer | Personalized marketing using anticipated customer behavior,contextual and churn probability data, leveraging database of 20M+ customers

3x incremental
revenue per customer

Automation

Insurance | Same-day claims payment enabled by medical imaging, face and voice1
prints, ML, data from many ecosystems (e.g., finance, health care, auto, real estate)

60%+ claims
self-service

Predictive
operations/maintenanceIndustrial goods | Risk-based system using manufacturing and inventory data
to simulate, predict, evaluate, and optimize logistical and supply bottlenecks

15% operations andmaintenance savings

Spotlight:
AI & CO2 emissions
Monitoring emissions• Collect data from every
part of the value chain• Approximate missing data
• Estimate the level ofcertainty of the results
Predicting emissions• Forecast emissions• Simulate reduction efforts,
new tech, and demand
Reducing emissions• Use prescriptive AI to
improve efficiency

Critical to invest to avoid disruption: 84% of firms believe AI helps them gain a competitive advantage2
1.Machine learning, 2. BCG-MIT Sloan Management Review 2019 AI Survey, n=2,555. Sources: BCG-Wired database, Fortune, BCG Henderson Institute, BCG cases and experience

7

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1.1

Significant value release from AI is rooted in reimagining the way
companies work with data and an ability to move from pilots to scaling

Four key steps to unlock financial benefit

Each step comprises critical activities for value release

Likelihood of achieving significant financial benefits with AI (%)

Discover AI

Launch pilots: Implement AI in targeted areas, e.g., models that
reduce customer churn

2%
~10x

Build AI
Only 11% ofcompanies reportsignificant financial
benefits throughrevenue/cost
improvements from
implementing AI1Advance to organizational use: Embed in overall strategy;
reimagine use of data; invest in data capability building,technology, and algorithms and in developing technical AI skills

21%
~2x

Scale AI

Scale to broader use cases and solutions, e.g., embed AI into
processes and solutions on both production and consumption sides2

39%
~2x

Organizational
learning with AI(deep dive on next page)

73%

Create opportunities for mutual learning between humans
and AI, e.g., learning how to adapt and use human-machine roles
and interactions in different processes and situations

1. Based on a global MIT-BCG survey in spring 2020, attracting over 3,000 total respondents representing 29 industries and 112 countries. Our assessment of “significant financial benefits” uses a threshold that varies according to organization size. For thelargest organizations in our sample, with revenues of more than $10 billion, passing this threshold requires more than $100 million in revenue and/or cost improvements annually from the use of AI. For smaller organizations, the thresholds were lower: $20
million in improvements for organizations with revenues between $500 million and $10 billion, $10 million in improvements for organizations with revenues between $100 million and $500 million (or nonprofits), and $5 million for organizations with less than$100 million in revenues; 2. Tech and business sides; Sources: MIT-BCG: findings from the 2020 AI global executive study and research project, Expanding AI’s Impact with Organizational Learning

8

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1.2

A critical element is people's interaction with AI - high value is unlockedfrom zero-based design and effective organizational learning

To capture value, reimagine processes…

5x

more likely to realize significant value whenprocesses redefined to embed AI1… and rethink human-AI interactions
likely to release significant value if effectively rethink how humans and6x moreAI work together and deploy all appropriate human-AI interaction modes

Start with a zero-based mindset (reimagine input
data, collaboration between systems and processes
continuously—and not only incremental changes)

5xmore likely to realize significant value whenfacilitating continuous human-AI learning

Using all three ways:• Built-in AI learning from human feedback• Humans learning from AI to apply insights• Humans designing AI to learn autonomously

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

1.3

AI-dominant

Human-dominantMode

AI decides andimplements

AI decides,
human
implements

AI recommends,
human decides

Used
when:

Humans slow
down process

AI can capture
the context well

Personalized
offers

Predictive
maintenance

ExampleAI generates
insights, humanuses in decisions

Human
generates, AI
evaluates

AI cannot factor
all context fully

Creative work

Hypotheticalsituations

Trading

Hiring plans

Simulations

Agility and oversight are critical: Switch between modes as context changes (e.g., COVID)

1. Versus those making no or small changes
Sources: MIT-BCG: findings from the 2020 AI global executive study and research project, Expanding AI’s Impact with Organizational Learning

9

Apart from driving financial impact, AI can also improve teameffectiveness and bolster team culture

87%

FRENCH ENERGY COMPANY• Next-best-offer AI recommendation tool for vendors, now also used for training• Adoption varied by vendor’s tenure (more heavily used by newcomers)• Veterans made tool more useful by “teaching” it (to enable newcomers andminimize manual training), promoting a culture of AI use

65%

US HEALTH CARE COMPANY• Emotional AI “listens” to the conversation of pharmacists, picks up emotionalsignals, and suggests what the pharmacist could do to provide a better experience• Those who used AI tool ended up with more satisfied customers (higher NPS2)• Pharmacists understood how they can better execute their role (e.g., how tohandle customer calls, portray empathy, respond to situations)

79%

US EXCHANGE OPERATOR• AI tool scanning prospectuses for useful information that can benefit clients• From 1 person scanning 1-2 docs for hour to AI scanning 120,000 docs per hour
• Resulted in significant improvement in employee satisfaction and morale byimproving the type and caliber of job that team members do

78%
Of teams reported better collaboration

EUROPEAN AIRLINE• AI predicting passengers likely to miss their flight and sequencing luggage
loading, to limit disruption and ensure on-time departure• AI-driven process helps align teams, inform relevant members of whatbags to be loaded and offloaded when, and enable better coordination

Of teams1 indicated that they improved theircollective learning

Of teams1 reported improved clarity of roles

Of teams1 highlighted an increase in morale

1

1. Of the teams that implemented AI and saw improvements in both efficiency and decision quality (58% of teams); 2. Net Promoter Score; Source: The Cultural Benefits of AI in the Enterprise, BCG-MIT, November 2021

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ExamplesCopyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

1.4

10

Despite positive outlook, companies must manage labor, data,
ethical, and regulatory dynamics
Labor market shifts andgaps in talent capabilities

Limited data capability,
quality, and consistency

59%

Face challenges in acquiringand developing AI talent1

23% of firms view gaps indata capabilities as biggest
challenge1

56%

Increase in demand fordata scientists and dataengineers in the US in 2021

60% struggle with the large
number and inconsistency ofdata sources3

40%

Of world's jobs at risk ofbeing automated in next
15-25 years2

71% report at least threelarge data quality issues3

Expected increased regulatoryburden for companies

Unethical application andlack of transparency

55%

Overestimate theirorganization’s responsible
AI maturity and progress431%

Of citizens concerned withlack of transparency in AI
decision making5

50%

Believe insufficientunderstanding is thegreatest obstacle to AI6

Initial steps in place:• European Union Draft AI Act - released in April 2021
• US Fair Trade Commission guidance on truth, fairness, and equity of AI use

Deep dives on each area are in appendix (pages 23-26)
1. "Artificial intelligence in business gets real," MIT Sloan Management Review and BCG, BCG Center for Sensing and Mining the Future; 2. "AI expert says automation could replace 40% of jobs in 15 years, Fortune. 3. “O’Reilly State of Data Quality in 2020” report.
4. Responsible AI constitutes a company's governance framework that documents how a specific organization is addressing the challenges around AI from both an ethical and a legal point of view; From BCG’s Responsible AI Survey 2021. Note: n = 1,034. Based onorganizations’ responses to 21 questions about their implementation across generally accepted RAI dimensions. 5. The Citizen’s Perspective on the Use of AI in Government, BCG; 6. The Cultural Benefits of AI in the Enterprise, BCG-MIT; Additional sources: BCG analyses

11

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1.5

December 7, 2021

17 AI innovative development pilot
zones built in China

November 5, 2021

Alphabet has set up a new lab that will
use AI to try to discover new drugs

Source: Press releases

October 29, 2021

‘Yeah, we’re spooked’: AI starting tohave big real-world impact, says expert

November 3, 2021

AI is not ready to make profounddecisionsNovember 8, 2021

November 3, 2021

The founder of Google Brain has raised$57 million for his A.I. start-up

Facial recognition firm Clearview AI toappeal order to stop collecting images ofAustralians

November 10, 2021

White House AI director says US shouldmodel Europe’s approach to regulation

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

In the news | Significant advancements in AI, but a need to manage headwinds

December 8, 2021

Meta: Facebook and Instagram parent
company hails new AI system to detectharmful content
12

To take advantage of AI, leaders must embed it into their businessstrategy and transformation, and prepare their operational models

Embed AIin yourbusiness

Prepare youroperatingmodel for AI

articulate the ambition for value creation from AI, linked to your company's strategy and1 Clearly
purpose—develop a clear perspective of where AI will drive business outcomes2

Treat AI development as a business transformation: redesign collaboratively with businessowners, iterate and improve based on human-AI learning

3

Learn from digital natives: Change your ways of working and operating model to enable enterpriseagility, continuously reimagine business processes, and effectively orchestrate ecosystems

HR early on: Anticipate AI impact on jobs and skill sets, embed in strategic workforce
4 Involve
planning and internal upskilling programs, rethink employee value proposition to retain talent
Decouple data capability building from core IT systems, embed flexibility via reshoring and5 modernize iteratively
6

Sources: BCG analyses, experience, and publications

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

2Invest early in responsible AI governance, policies and standards, structural assessment, expert
staff, and issues response plan; provide transparency to stakeholders
13

Clearly articulate the ambition for value creation from AI and develop
a perspective of where AI will drive outcomes

Prioritize revenue growth to unlock higher value

Identify AI value creation
linked to your businessstrategy and purpose

2x more likely to reportvalue generation if AI is on
the CEO agenda (vs. if only
managed at IT function level)

Critical to decide what to do
and what not to do—focusedinvestment is key

Place calculated “bets” on larger projects
Of firms investing in higher-risk AI projects seevalue add (vs. 23% for lower-risk, smaller ones)

2x

More likely to obtain value from AI if
focus on revenue (vs. cost savings alone)

50%

72%

Of those that have seen revenue growth
expect it to continue in the next 5 years (vs.
only 44% of those focused on cost cutting)Selectively deploy small, cost-cutting ones
• Quick wins–build buy-in and fund the AI journey• Of these, focus on productivity improvements with
low incremental complexity or resource intensity

Identify desired AI outcomes at department level to support corporate strategy1
Marketing• Personalization• Cross-sell & upsell• Churn & retention• Next best productand action

Product

Manufacturing

Supply chain

Risk• Promo & pricing• Predictive• Assortment
maintenance• Salesforce• Quality analytics
effectiveness• Advanced asset• Zone-based pricing
management• Digital twins• Advanced
forecasting• Inventory,
network, & route
optimization• Simulations• Automated risk
monitoring• Claims
management• Fraud detection

1. Non-exhaustive list; Sources: BCG-MIT Sloan Research report Winning with AI Pioneers Combine Strategy, Organizational Behavior, and Technology, Oct 2019; BCG analyses and experience

Climate &sustainability• Carbon emission
optimization• Predictive carbon
storage• Yield prediction

14

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2.1

2.2 Treat AI as a business transformation: don’t focus only on tech
and algorithms—design collaboratively with business owners

Create an environment in which tech can co-develop
and implement AI solutions with business team

Key value drivers in enterprise AI adoption

Critical to design for consumption: Business teams whose work is
affected must be engaged to provide feedback and test prototypes

Algorithms

37%

Of cases where business leaders pioneer AI projects see value
added (vs. only 17% where tech solely leads similar projects)

10%
20%

Technology

Upon new AI investments, it is important to build in cost lines forboth producing and consuming AI (e.g., developing expertise among
business users, building processes and metrics to support adoption)

70%
Business processtransformation

88%

Of companies that generate value from AI tightly link their
AI initiatives to company-wide digital transformations

Sources: BCG-MIT Sloan Research report Winning with AI Pioneers Combine Strategy, Organizational Behavior, and Technology, Oct 2019; BCG, How to Win with Artificial Intelligence; BCG analyses and experience

To facilitate collaborative,
cross-functional
approach, the company
operates an AI lab thatpulls people out of their
daily jobs for 1-2 weeks towork with data scientists
on prototype solutions
Yearly internal
conference on AI for
relevant employees
All-staff AI inductions
and training to raiseoverall understanding

15

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10-20-70 rule: Realizing value from
AI is rooted in business adoption

Example: Industrial
manufacturer MNC

Change your ways of working and operating model to enable enterpriseagility by leveraging cross-functional teams, platforms, and modularity

Enterprise agility neededStart with pilots, move to portfolios, and ultimately scale to enterprise agility

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

2.3• Break silos by bringing together small, empowered, multidisciplinary
teams (tech and business combined) to build AI solutions

1
Pilots2
Adoption of AI at scale will
require more agile structures
focused on business value andfaster time to market bybreaking the compromise
between scale and agility

Portfolios

3Enterprise agility• Ensure autonomous teams moving in unison through strong alignment
around a shared purpose, strategy, and priorities• Make the matrix work by separating "what" and "how" at team level• Ignite, articulate, activate, and embed new leadership behavior• Allocate resources continuously, aligned with outcome-based goals• Build platforms1 to benefit from scale and drive long-term innovation• Embed modularity to reduce interdependencies and allow teams to work
autonomously end-to-end2• Remove boundaries of the organization to seamlessly integrate externaltalent from the broader ecosystem

1. Platforms can be business area (for example, core process flow oriented) or technology area (for example, core IT system oriented) that require persistent funding and staffing of teams-of-teams to drive design, delivery, and operations of AI
products/capabilities; 2. Removing legacy “spaghetti” structures with heavy interdependencies between teams. Source: BCG experience16Effectively orchestrate an AI ecosystem to enable fast learning, scaling,
and lower technology costs

AI requires access todata and collaboration

Determine ecosystem potential, rethink role of players, orchestrate effectively

1

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2.3

Do not treat AI solutions as a plug-and-playBring about systemic change to optimally leverage other players' capabilities
Do not treat the same as investing in startups to gain a possible use of off-the-shelf techEngage ecosystem players: rethink mutual workflows

2
To drive transformative impact withAI, companies need access to:• Large diverse sector-specific andcustomer-wide data
• Potential for deeper experimentswith vertical AI• Faster feedback loops to enable
quick development cycles• Support in use case expansion
Sources: BCG experience and analyses

Carefully scout for and validate AI pioneers and partners

Change mindset on how to define strategic segment (e.g., from car manufacturer to mobility)
Study the emergent AI and trends, identify internal gaps

3

Collaboratively innovate on how to exchange information

Take the lead in establishing new data-sharing policies with large digital firms, AI pioneers/
startups, universities, think tanks, NGOs
17

Embed AI skill set shortages in your strategic workforce planning anddevelop internal upskilling programs, do not focus only on external hiring

Perform strategic workforce
planning, regularly reassess

Supplement hiring with upskilling—
given likely shortages soon

86%

59%

Of companies that are actively reskilling their workforce see
impact from their AI efforts (vs. 19% that rely solely on hiring)

At a company level, assess workforce size, composition, and development

65%

Of those with multiple approaches to AI talent have seen
business impact from AI

Evaluate future skill set demand given strategic direction and AI projects—
e.g., including skills mapping and advanced supply and demand models

Create a lifelong learning culture: Deliver content and skill upgradesin a variety of formats so that they can be integrated into the daily routine

Plan globally to bridge the gaps:• Assess future talent in your markets based on government AI policy• Engage with top universities early on to tap into STEM talent• Consider recruiting from more fluid talent pools outside of core markets1

Develop flexible employee pool for people with AI skills, without yet
knowing which function or use case they’d be best suited for

Of firms believe existing workers will need to change skill sets

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2.4

Shift the recruitment focus from hiring for skill to hiring for "will":comprehensive upfront training and room for self-teaching AI skills

1. Especially given postpandemic remote-working era
Sources: BCG's The Future of Jobs in the Era of AI, 2021; BCG-MIT Sloan Research report Winning with AI Pioneers Combine Strategy, Organizational Behavior, and Technology, Oct 2019; BCG's How to Win with Artificial Intelligence; BCG experience18

Enable data and AI via five “mental flips”: decouple data capability building
from core IT, embed flexibility via reshoring, and modernize iteratively

Decouple data capability
building from the core IT
transformation - to introduce
tremendous business agility
and reduce IT risk

Liberate the data and focus on business outcomes business (not IT) owning data to prioritize and drive use cases.
Data & digital platforms approach:

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2.5

Reshore and build in-houseengineering capabilities –to improve time to value• Create a data layer to liberate data from core systems
across enterprise (e.g., ERP, CRM)
• Build more modular interfaces between systems• Adopt cloud infrastructure for speed and agility

Apply a broad-based approach: Rather than overemphasizing a particular data capability, the best companies invest tobuild their maturity in all core capabilities of the pyramid (see page 24)
Build incrementally and iteratively modernize over time: Don’t aim for a “big bang”—build data capabilities ina deliberate, incremental manner over time. Apply test, learn, and improve mindset

Sources: BCG's DACAMA research, BCG experience19

Invest early in responsible AI (RAI) governance, policies, expert staff, andstructural assessment—and provide transparency to stakeholders

Need to manage
AI ethical issues 82%

Of the public wants
responsible AI to
reduce ethical risks78%

Of workers want help
managing product dev
ethical challenges

But…

21%

Of companies have afully mature RAI
program in place

Four steps organizations should take to get started
Empower responsible-AI leadership

Establish governance: trust but verify• Appoint a leader to design program, engage inforums, identify champions, establish principles• Diverse committee to steer complex issues• Establish mechanism for review andadherence, escalation paths to raise concerns,
and integrate into existing risk processes• Refine roles—accountability for RAI outcomes

Develop principles, policies and training

Conduct structured reviews of AI systems• Build, refine, communicate RAI principles,policies, & training to all AI team members• Regularly refine based on company feedback1• Adopt a structured assessment tool to proactively
identify risks during project life cycle• Undertake a comprehensive assessment

55%

Overestimate theirorganization's RAI
maturity and progress2

Read more on the topic in:
Microsoft-BCG study on
implementing AI responsibly

Guidelines to support AI innovation
with responsible outcomes andanticipate and mitigate risks of AI
systems, with a focus on productdevelopment and delivery processes

1. Feedback around employee concerns, high risk areas, etc.; 2. From BCG’s Responsible AI Survey 2021. Note: n = 1,034. RAI = responsible artificial intelligence. Based on organizations’ responses to 21 questions about their implementation across generally
accepted RAI dimensions; Other sources: Center for the Governance of AI, University of Oxford, Doteveryone, BCG analyses and experience20

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2.6

Are You OverestimatingYour Responsible AI
Maturity?

Is Your Company Gaining
Momentum in Data?

The Future of Jobs inthe Era of AI

The Hidden Cultural
Benefits of AI

How Automation and AI
Can Unlock Value in IT
Operations

Use AI to Measure
Emissions—Exhaustively,
Accurately, andFrequently

Are You Making theMost of Your
Relationship with AI?

How Digital Giants andEurope Can Cooperate
to Win in AI

Click here to read past editions of Executive Perspectives

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

Additional perspectives on AI trends and implications

How Humans and AI
Can Work Together toCreate Better
Businesses

21

Executive
Perspectives

Labor
Page 23

Labor market shifts andgaps in talent capabilities

Page 24

Limited data capability,
quality, & consistency

Data

EthicsAppendix

Page 25

Regulation
Page 26

Unethical application
and a lack of transparency
Expected increase inregulatory burden

22

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

Executive
Perspectives

Deep dives in AI dynamics which companies should manage

Labor | AI likely to shift labor markets—with growth in tech, creative, andmanagerial jobs, and a decline in routine, manual-labor jobs

Likely job automation:• Processing of big data
• Repetitive motions• Control precision, etc.

6:1

For 6 jobs automated oraugmented, 1 additional
job is required

41% see increased risk of automation for their job in 2020, but 68% willing to retrain1

Most
concerned
employees

1. Finance and auditing
2. Customer service
3. Human resources
4. Administration and secretarial
5. Media and information

1. Social work
2. Law
3. Science and research
4. Health and medicine
5. Management

Expected widening labor supply-demand gaps necessitate reskilling of employees
Net surplus in 2030 vs. 2020

Low automation probability:• Collaboration• Management• Empathy• Creativity, etc.

Least
concerned

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AI drives shift in required skills

Admin support

Production+1,650 K+490 K

Expected overall labor
supply shortages—
resulting in low surplus
numbers across jobs

Production+37 K

Net shortage in 2030 vs. 2020
IT and math-based jobs Management Architecture-5,567 K-4,150 K -1,198 K

IT and math-based jobs Management-1,052 K-1,026 K

1. 2020 BCG/The Network proprietary web survey and analysis. Sources: Modelling workforce supply and demand for the next 10 years based on digital, demographic and COVID-19 implications, Faethm and BCG analysis; 2020 BCG/The
Network proprietary web survey and analysis, Decoding Global Reskilling and Career Shifts-Apr 2021

Finance-280 K

23

Data | Data availability, quality, buy-in, & governance centralization
critical to scaling of AI—continued gaps but progress underway

Data maturity index(change 2018-20217)

+12%

Use
cases2Analytics
set-up3+13%

Governance4

+16%
+20%

Data ecosystem6+19%

Overall data maturity index improvement

Gap reducing versus previous 3-years (2015-2018),
yet remains wide

+5%

Data platform5+15%

Gap in achieved maturity level vs.

21% ambition from 3 years ago7

Key challenges include:
Lack of business buy-in and management
Foundations

Vision1

Yet, actual data maturity falls short ofcorporate ambitions

Limited central governance and management
Underinvestment in data quality, variety, &standardization
View gaps in data capabilities as main

23% challenge8

Strong investment in central
data housing and governance

83%

40%

39%

Of pioneers9 manage corporate
data centrally

Access to large sets of data via
orchestrated ecosystems~30% of Chinese companies
are declaring significant financial
benefits10 with AI, vs 10% in US

1. Objective and outcomes we aim to achieve from using data; 2. Main macro use cases for the data, value we expect from each of them, ownership and accountability for each of them; 3. Talent pool, analytics functions, process and results; 4. Data management, data hygiene
actions (e.g., key quality indicators); 5. End-to-end infrastructure suitability for vision and future use cases (e.g., from modelling to industrialization); 6. Understanding, leveraging and role in the ecosystem; 7. BCG Data Capability Maturity Assessment 2021 vs expectations in 2018;
8. "Artificial intelligence in business gets real," MIT Sloan Management Review and BCG, BCG Center for Sensing and Mining the Future; 9. Defined as organizations that both understand and have adopted AI. These organizations are on the leading edge of incorporating AI into boththeir offerings and internal processes. They comprise 18% of the survey conducted as part of MIT-BCG 2019 Artificial Intelligence Global Executive Study and Research Project; 10. Based on a global survey in spring 2020, attracting over 3,000 total respondents representing 29
industries and 112 countries. The assessment of “significant financial benefits” uses a threshold that varies based on organization size (e.g., for companies with revenue of $10B or more, threshold is >$100M in revenue and/or cost improvements annually from the use of AI, etc.)24

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

Companies have advanced the data “foundations”
in the past 3 years

Strong data focus underpins
China's advancement in AI

Ethics | Ethical and transparency concerns around AI applications can raise
reputational risk and be a limiting factor—if not appropriately addressed… and “black box” perception

49%

Of respondents believe thatmistrust of AI stems from alack of understanding1

34%

Of respondents believethat AI decisions provide
too little context2…with varied sentiment by nation3
Average net perception: Percent of individuals “not concerned” less “strongly concerned”(i.e., negative % indicates that more surveyed individuals are concerned than not)What decisions should we let AI make?
Who decides how to deploy AI?
Who should be responsible for AI's mistakes?

Lack of transparency indecision making

How to eliminate AI bias in decision-making?
How to address the perceived lack oftransparency ("black box" nature) of AI?
…and many more..

Ethical issues that havenot been resolved
Potential for bias or
discrimination

10%
-26%

-17%

-12%

-11%

-11%

-9%
3%

-24%

-18%

-11%

-8%

-12%

-4%

-9%

-3%

20%

-14%
9%

-12%

15%

20%

23%

-9%

1. The cultural benefits of AI, MIT Sloan-BCG November 2021; 2. OECD Science and Technology "42 countries adopt new OECD principles on AI"; 3. Survey questions: What concerns you most about the use of AI by governments? Response options range
from 1-7, where 1 = Extremely concerned, and 7 = Not at all concerned. Respondents who selected '6' or '7' have been included as not concerned, '1' or '2' have been included as strongly concerned, '3', '4' and '5' have been counted as neutral.
Source: BCG 2018 Digital Government Satisfaction Survey

25

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

AI faces many ethical challenges…

Regulation | Expected increase in scrutiny, amplifying compliance burden:
EU's draft risk-based proposal provides example
Risk-based approach (higher risk, higher requirements), with evolving list ofprohibited practices to reflect new AI uses
Low orminimal risk

Compliance burden:• Significant documentation, record
keeping, and disclosure required• Fines up to 6% of global annual
revenue for failure to comply

Business
implications

Wide application:• Any AI systems with
impact/”output” in the EU1

Activities

Proposal focused on:• Protection of general interestsand fundamental human rights• Limiting bias and security risks
of AI

High
risk

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

EU Draft AI Regulation 2021,
likely to launch in 2020s

Unacceptablerisk

AI systems that do not fall inother two risk categories, incl.:• “Obvious” interaction withhumans (e.g., chatbots)• Operations optimization• Behavior prediction• Digital solutions personalization,etc.

• Public safety management• Educational assessment &admission• Recruitment, promotion, andcontract termination decisions• Administration of justice andlaw enforcement• Creditworthiness and publicbenefits eligibility assessment• AI systems likely to cause harm:
subliminally manipulate,
exploit vulnerabilities, or
distort behavior• AI-based social scoring• “Real-time” biometricidentification in public spaces
for law enforcement2

Customer transparency: AI use
disclosure

Stringent requirements: Customertransparency, human oversight,
government registration, monitoring& reporting, conformity assessment,
data & risk management

Prohibited AI practices: monitor
closely and work with EU bodies toassess “gray-zone” applications

1. Including: providers that first supply or put an AI system into service in the EU (putting into service involves making it available for use by a “user” or for use by the provider itself), regardless of whether the providers are located inside or outside the EU;
users of AI located in the EU; and providers and users located outside the EU, if the output produced by the system is used in the EU). 2. Note: Biometric identification and people categorization in nonpublic spaces considered 'high risk';
Source: EU Regulation of the European Parliament, Allen Overy

26

BCG does not provide fairness opinions or valuations of market transactions, and these materials should not be relied onor construed as such. Further, the financial evaluations, projected market and financial information, and conclusions
contained in these materials are based upon standard valuation methodologies, are not definitive forecasts, and are notguaranteed by BCG. BCG has used public and/or confidential data and assumptions provided to BCG by the Client.
BCG has not independently verified the data and assumptions used in these analyses. Changes in the underlying data or
operating assumptions will clearly impact the analyses and conclusions.

27

Copyright © 2021 by Boston Consulting Group. All rights reserved. BCG Executive Perspectives updated 13 December 2021 Version 2.0

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