How can AI
unlock value forindustrials?
Five ways for industrial companies to
use AI to unlock real value, now and in
the future.

1
2
3

Generative AI is spurring a manufacturing
renaissance, enabling new capabilities andelevating previous uses of AI.

AI deployment has been hindered by a focus
on point solutions and misalignment between
expectations and incentives.

Five initiatives, which require immediate andlonger-term actions, can help companies
become AI ready and can build on successful
pilots across the organization.

Artificial intelligence (AI) is much more than a “buzz phrase” and is set to have a deep and wide impact across industrial sectors.
While industrial companies already are using traditional AI approaches such as machine learning (ML) in operations technology(OT) point solutions, the rapid adoption of generative AI (GenAI) has raised awareness of AI’s capabilities and potential totransform and disrupt.
A few numbers indicate the progress and pace of change:• Forty-nine percent of advanced manufacturing and mobility companies have fully integrated AI-driven product or service
changes into their capital allocation process and are actively investing in AI-driven innovation.1• In the next 12 months, 41% will follow, leaving only 10% that do not plan to do so.
• By 2030, 96% of companies are expected to increase manufacturing AI investment.2• Beyond 2030, 59% of industrial leaders see AI as significant or game-changing for the future of manufacturing.

1. EY Global Outlook on AI strategy, 24 October 2023.
2. Manufacturing in 2030 Project — The Manufacturing Leadership Council, 31 October 2022.

2

How can AI unlock value for industrials?

AI deployment is hampered by a complex set of challenges
Despite the imperative to move fast, industrial leaders are often held back by uncertainty and constraints. In particular, industrial
companies face challenges across four key dimensions: supply chain, strategy, people and information technology (IT).

Supply chain andoperations

Strategy, innovationand growth

People, organizationand change

Information
technology

AI needs data andcooperation channels

AI needs C-suite advocacy
and accountability

AI needs people — theright people

AI needs infrastructureand enablement• Misalignment of supply
chains has led tocomplex and ineffective
digital infrastructure
and tools.

• To compete in atechnologicallyadvanced environment,
manufacturers needto avoid stagnant
business models andprocesses.

• Insufficient accessto talent due to highcross-sector demand
and undefined career
paths for technologists.

• Insufficient resource
allocation andcommitment fromthe C-suite can hinder
AI investment andimplementation.

• Low coordination has
caused inexistent or
unstructured data todevelop AI compatible
data aggregator
platforms.
• Lack of real actionable
data to improve
supply chain visibility,
collaboration andresiliency.
• Complex supply chainsare challenging to map,
monitor and manage.
• Inaccurate demand
forecasting andinventory management
systems have
prevented companies
from being proactive
rather than reactive.

• Rigid operations
impact the criticalchanges necessary
for AI integration intobusiness models.
• Without strategic
orchestration of AI
initiatives, point
solutions may bedisjointed andmisaligned.

• Mismatch between theskills needed in the
future and the current
industrial workforce.

• There is uncertainty
around the cost
implications and ROI
impact of AI.

• Ineffective knowledge
management, upskilling
and training programsin place that align tostrategic businessinitiatives.

• Companies are missing
the core capabilities toinnovate and grow in
the AI space.

• Uncertainty aroundwhich skills to target,
timing and potential
operational impacts.

Source: EY.ai AI Maturity Model.
3• Proprietary tools andon-site skills gainedaligned to a specificindustry have created a
specialization gap vs.
the current technology
workforce.

How can AI unlock value for industrials?

• Operations focused
legacy silos andincentive structures
limit IT’s ability to growbeyond its traditionalrole to support newtechnology integration.
• Budgets allocatedto CAPEX projects
with clear ROIs vs. IT
investments with lesstransparent costs andbenefits, such as AI andrequired infrastructure.
• Operational KPIs areoften prioritized overfoundational IT KPIs,
which can impact
the enablement of AI
initiatives.
• IT departments in the
sector often have underresourced small teams
to deliver against highexpectations or large
teams with legacy skills.

Twenty-nine percent of advanced manufacturing CEOs consider adapting supply
Twenty-nine
percentof advanced
CEOsstrategic
consider actions
adapting
chainsfor better
resiliency
is one ofmanufacturing
the most important
forsupply
their
chainsforbetterresiliency
as
one
of
the
most
importantstrategic
actions
forcompany in the next six months, according to the EY CEO Outlook Survey 2023.
their company
the next
six months,
accordingto plansthe EYamidst
CEO Outlook
SurveyHowever,
32% sayinthey’vedelayedtheir supply
chain
the changing
2023.
However,32%
saythey’vedelayedtheirsupplychain
plans
amidstthegeopolitical landscape. Almost half (49%) of industrial products CEOs are adjustingchanging
geopoliticallandscape.
Almost
half
(49%)
of
industrial
products
CEOs
supply chains through supplier diversification, inventory build-up, technology
are adjusting
supply collaborations
chains throughand
supplier
diversification,
investment,enhanced
relocatingproductioninventory
closer tobuildhome.
up, technology investment, enhanced collaborations and relocating production
closer to home.

Around 45% of advanced manufacturing CEOs surveyed believe AI is a force forgood that can have a positive impact on business efficiency and innovation. Over
60% of manufacturing leaders are prioritizing enhancing technology capabilities,
sustainability integration, and introducing new products and services in theircapital allocation strategy. And 57% of manufacturing leaders are investing inorganic growth initiatives and M&A to improve technology and expand theirproduct and service offerings.

Industrial companies’ AI ambitions risk being stalled by a lack of suitable
technology talent. In response, 27% of advanced manufacturing CEOs say oneof their top strategic priorities over the next six months is to adopt new working
models and talent strategies to attract and retain employees. For a quarter (26%)
of manufacturing companies, scarcity and cost of talent with the right skill sets toaccelerate growth, are the greatest risks to their business growth. This explains
why 35% of advanced manufacturing companies plan to boost investment intalent including workforce wellbeing and skills development.

A separate survey by the Manufacturing Leadership Council found that 65% ofmanufacturing leaders see data issues — notably access, format, integration,
privacy and governance — as the main challenge for AI adoption in theircompanies. Forty-six percent of advanced manufacturing CEOs believe techand digital disruption, including cyber risk, poses the greatest risk to businessperformance over the next 12 months. To close this gap, 70% of advanced
manufacturing CEOs intend to increase capital investment in data, technology
and cybersecurity.

4

How can AI unlock value for industrials?

Supply chain prioritization49%
of leaders want to improve their supply
chain via tech investment, supplier
diversification and relocating production.
AI as a force for good45%
of manufacturing leaders see AI as a
key driver for business efficiency andinnovation.
Talent’s impact on growth26%
of companies consider scarcity andcost of talent with the right skill sets toaccelerate growth as the greatest risk
to the growth of their business.
Capital expenditure70%
of manufacturing leaders are looking
to increase their capital investment in
data, technology and cybersecurity.

Five initiatives industrial leaders can action to embed AI potential
Despite uncertainty, industrial leaders can take steps today to ensure their companies are AI-ready. We have identified
five cross-sector initiatives that address the challenges to AI deployment and differentiate between how companies can“act now” versus “evolve later.”
The pace of technological development calls for a two-speed approach to these initiatives. The “act now” recommendations
require minimal investment, cause minimal disruption, are modular, and should show tangible results in the short term. As thewins compound and achieve momentum, “act now” recommendations grow into “evolve later” suggestions. These forwardlooking suggestions require top-down strategic orchestration and capital allocation to create a sustainable flywheel for AI
initiatives to grow.
These five key initiatives will help guide AI deployment toward being strategically aligned, operationally purposeful andcommercially viable:

Establish an
AI value realization
office andevolve into acontrol tower

Explore futurescenarios to alignthe approach to AI

Create a dataarchitectureassessment andupgrade roadmap

Develop AI
ecosystem
partnerships

Create a skills
assessment
to identify
re-skilling needsDataarchitectureassessment

Map AI
ecosystems andcomplementary
capabilities andinitiate pilots

Develop andimplement a
re-skilling plan

Data strategy
with phased
upgrades by
ROI, impact andfeasibility

Develop aworkforce
re-skilling plan

How to act now

Set up an AI value
realization officewith a focused
scope to test
and learn

Future backplanning linking AI
to business value

How to evolve later

Scale up thevalue realization
office into acontrol tower

5

Continuous
resource
allocation toempower thecompany’stransformation

How can AI unlock value for industrials?

Use key
evaluation
criteria tosupport a narrow
but strong AI
ecosystem ofpartners

01
Initiative

Establish an AI value realization officeand evolve into a control tower
Industrial companies should form a unit that streamlines
experimentation and resources around AI and ties it to businessoutcomes. The value realization office coordinates knowledge
sharing and shapes governance, but its primary goal is to realize
benefits, conduct project and risk management, and optimize
resources.
The value realization office can start with a smaller scope, but toextract maximum value from AI initiatives across the enterprise, it
must evolve into a fully-fledged “control tower.” The control tower
is a C-suite business unit, tasked with AI strategy and steering crossorganizational initiatives. It has the authority to allocate capital andcoordinate resources across different business functions.

Act now: Set up an AI value realization
office with a focused scope and C-suite
accountability to test and learn
One way to experiment with AI is to create a value realization
office via a simple project management office within a single
business unit. This should involve stakeholders with different
specializations, with C-suite support, focused on high priority
or quick-win projects, and actively analyzing and adapting
governance practices to new business requirements. Leveraging
easier-to-use technologies like GenAI and low-code or no-code
can lower technical barriers to experimentation, enabling
nontechnical people to participate earlier. All of which increases
buy-in, develops expertise, improves agility and enables activities
that create value.

Evolve later: Scale up the value realization
office into a control tower
As the value realization office grows in creditability and scope,companies can increase its autonomy and responsibility to scale
6

How can AI unlock value for industrials?

up AI deployment with increasing C-suite oversight. It should
evolve into a control tower, with formal positions, governanceand resources to orchestrate projects companywide. The
control tower would also orchestrate re-skilling needs, data
infrastructure upgrades and ecosystem strategies.

Case study: EY teams helped establish
a new technology scale-up to
support software development for an
automotive company
EY teams helped a leading automotive organization
establish a progressive and aggressive technology
scale-up company in three months, while simultaneously
and seamlessly integrating with an emerging joint
venture (JV), with different cultural and governancebackgrounds.
EY professionals coordinated multiple workstreams (IT,governance, legal, HR, sales and marketing, finance etc.),
in the development of an operating model and businessplan, provided operative support in the workstreams andtook on interim roles in the senior management teamsuch as CIO, CTO and CHRO. As a result, EY professionals
helped create a software development function from theground up with operating model, tools, frameworks andnew ways of working.
The company now has a deep knowledge and network
within the mobility sector, providing customized
insights that guide strategic direction and master plans –including the project management office (PMO) engine.

02
Initiative

Explore future scenarios toalign the approach to AI
There is no shortage of potential use cases for AI, butorganizations often struggle to align these use cases withoverall strategy and vision.
By identifying scenarios for potential AI impacts and benefits,industrial companies can more efficiently allocate resources
and prioritize initiatives.

Act now: Use future-back planning linking
AI to business value
To develop focused initiatives aligned with an overall AI
vision, industrial companies should begin with future-backplanning, to identify the potential impact of AI on the businessand the sector. Future scenarios should consider regulatory,

macroeconomic, supply chain and resource constraints,
and link AI activities to business value.

Evolve later: Allocate resources
continuously to empower the company’stransformation across dimensions
To transition the value realization office into a control tower,link the top-down scenario planning and capital allocation withbottom-up learnings and activities. Scenarios can also informwhich skills are needed for re-skilling plans, data requirements
for data architecture upgrades, and which competency whitespaces exist for ecosystem strategies.

Case study: How AI predicted market adoption of advanced driver assistancesystems and connected vehicles
EY teams helped a US Department agency to develop a bottom-up quantitative model to predict market adoption ofadvanced driver assistance systems and connected vehicles. The model was based on qualitative assessment of technical,
production, supply chain, maintenance and regulatory factors.
The complex market forecasting models can process granular inputs and apply ML techniques to forecast market
penetration. With a powerful visualization tool, users can build and assess different technical, production, supply chain andregulatory scenarios, and derive insights to guide business planning.

7

How can AI unlock value for industrials?

03
Initiative

Develop a workforce re-skilling plan

AI is widely anticipated to have a sweeping impact on workand talent, as its rapidly advancing capabilities allow it toperform a wider variety of work with increasing ease andsophistication.
GenAI, in particular, can produce creative and analytical
output like code, emails, images, slide decks — as well as
taking over “blue collar” or “new collar” jobs in production.
AI-enabled industrial robots are widening their roles byimproving skills such as object recognition, learning andcoordination, enabling them to play an increased role.
GenAI could make factories more adaptive and efficient,by enhancing their ability to reduce unplanned downtime,enhancing forecasts.
The expanding role of AI will require workers to re-skill,both to gain proficiency with using it and to enhancecompetencies that will become more valuable in the eraof GenAI, such as quality assurance, content integrationor customer engagement.

Act now: Create a skills assessment to
identify re-skilling needs
Begin by assessing what tasks AI is likely to take on andwhat competencies are required for workers, especially for blue
collar work, where the impact of GenAI has beenless explored.

Evolve later: Develop and implement a
re-skilling plan
Organizations should strive to create a continuous learning
culture to enable them to adapt to constantly changing skills
needs. Incentives can play an important role, linking re-skillingto career advancement and financial compensation. And, byidentifying and encouraging pockets of innovation, companies
can keep talented employees engaged and attract new talent
with in-demand skill sets.

Case study: Mercedes-Benz invests in an AI re-skilling program to upskill its employees
As the automotive industry becomes increasingly digital and more reliant on AI, upgrading employees’ digital skills is
crucial. Mercedes-Benz plans to invest more than US$2.2 billion by 2030 to train employees as data and AI specialists.
Their “Turn2Lean” initiative’s two pilot programs are training over 600 employees in production, production-related andadministrative roles in digitalization and AI, with ‘graduates’ of the program awarded certificates.3

3. Mercedes-Benz invests over $2.2B in AI re-skilling, Automotive Dive,1 Aug 2023.

8

How can AI unlock value for industrials?

04
Initiative

Create a data architectureassessment and upgrade roadmap
Industrial companies have traditionally deployed OT focusedpoint solutions without a cohesive data strategy but having theright data architecture is critical for the effective deployment of
AI across an organization. The challenge is compounded by thedifferent kinds of data used by traditional AI (structured data)
and GenAI (which excels at working with unstructured data).
To use GenAI across the workforce, large language models(LLMs) should be trained on operating procedures and bestpractices — building a “knowledge graph” for the organization.
But much of this information often resides only in the minds of
employees, and may not be formally codified, much less stored
in digital form.

Act now: Conduct a data architectureassessment
The data architecture should be evaluated to identify
process design, dependencies, and data quality and security.
Appropriate benchmarks can provide a performance baseline

and support future AI business cases. Map the potential
system upgrade scenarios by pairing phased upgrades with thepotential ROI of corresponding use cases. Governance should
be reviewed and strengthened, to cover legacy risks (suchas data privacy, bias and cybersecurity), as well as new risks
created by new AI use cases.

Evolve later: Execute a data strategy
with phased upgrades by ROI, impact andfeasibility
After the infrastructure is mapped, companies need astrategy to collect, store and manage the data needed for
AI applications. The first step is to identify and implement
processes for boosting data quality. Secondly, they can adopt
an ROI-driven, phased approach to capture data and introduce
new use cases. Explore opportunities for new data acquisition,
including “synthetic” data as needed to compensate for thelack of historical data for AI model testing and deployment.

Case study: EY teams helped deliver AI solutions for a large industrial products
company
EY teams advised a large industrial products company on how to manage data science initiatives and build a technicalteam skilled in Azure Data Services, Python and Databricks programming, to help develop bespoke AI models. EY teams
collaborated with the client’s subject experts, business analysts, data architects, data engineers and data scientists todesign, model and help deliver AI solutions. A range of models were designed for demand forecasting, financial forecasting,customer health scoring and manufacturing risk to improve business efficiency and customer mapping.

9

How can AI unlock value for industrials?

05
Initiative

Develop AI ecosystem partnerships

Industrial companies are used to managing complex supply
chain partner ecosystems. For both supply chain and AI
partnerships, it’s important to vet partners, set performancestandards and manage partnership costs.

Evolve later: Use key evaluation criteria tosupport a narrow but strong AI ecosystemof partners

However, AI partnerships bring greater complexity anddepth of integration because AI solutions need to connect
to central systems, be adaptable and managed over time.
Each partner adds to the integration and management costs,
impacting on the orchestration of the technology stack.
Underperforming AI partners can cause greater harm than
traditional supply chain partners.

As the AI partner ecosystem evolves, it is essential toestablish key criteria to evaluate the ecosystem relationships.
This helps to select and develop relationships with priority
partners and act decisively to remove unnecessary partners
that fail to deliver value or adapt to future needs. As theecosystem evolves, so should the function, governance, andbest practices playbook.

Act now: Map AI ecosystems andcomplementary capabilities andinitiate pilots
When choosing partners for AI projects, companies should
compare their AI capabilities, maturity and ecosystems
against emerging best practices. Partners and ecosystems
with complementary capabilities and experience canaugment gaps in skills, technology and implementation.
However, new partner and ecosystem relationships also
require new governance. By establishing early partnerships
with multiple entities, and identifying small pilot project
opportunities, companies can build experience ahead oflarger AI projects.

10

How can AI unlock value for industrials?

Impact of the five initiatives to address key challenges for industrial companies

Dimension

AI value
realization
officeChallenge

Misalignment of supply chains
Low coordination leads to poor data quality
Lack of real actionable data
Supplychain

Difficult to map, monitor, and manage
Inaccurate demand and inventory management
Stagnant business models
Rigid operations
Lack of strategic orchestration

Strategy

Opaque ROI vs impact
Lack of capabilities to innovate
Insufficient access to specialized talentOn-site skills vs. the current tech workforce skills
Mismatch in skills needed in industrial workforce

PeopleIneffective knowledge management and upskilling
Uncertainty on which skills to target
Insufficient commitment from the C-suite
Legacy silos and incentive structures
Lower capital availability for IT investments

IT

Operational KPIs prioritized over IT KPIs
IT teams unable to deliver against high targets

Low impact

Medium impact

Source: EY analysis, December 2023.

11

How can AI unlock value for industrials?

Transformative impact

Futureorganizational
scenarios

Workforce
re-skilling
plan

Data
assessment
androadmapAI
ecosystem
partnerships

Key considerations for industrial
companies when deploying AI
Manufacturing companies have always been led by OT. AI
won’t reverse that trend but will support a deeper IT/OT
convergence. Business-driven point solutions are likely to growin number and require the standardization and interoperabilityof OT data. Industrial companies should then recognize theneed for a mature data architecture, to justify IT investments,and ensure AI brings value sustainably and safely. AI isenabled by technology but should be led by business.
Many of the benefits AI can bring to industrial
companies come from the foundational work that needs
to happen inside companies pre deployment: It’s not solely
a technology upgrade, it’s also an organizational andcultural upgrade.
The supply chain has existed for centuries and has always
leveraged new technologies to adapt and improve. Today,
supply chains that are more resilient, more cost-effective
and more predictable are AI-enabled supply chains.
Some see AI as a buzz phrase, but it’s worth rememberingthat AI isn’t a switch, it’s a journey.

12

How can AI unlock value for industrials?

Summary
Industrial manufacturing companies areintegrating AI into their operations to harness its
transformative and disruptive potential. However,AI deployment also brings complex challenges,
particularly regarding supply chain, strategy,
people and IT.
These five initiatives can help guidecompanies toward more strategically aligned,
operationally purposeful and commercially
viable AI deployments. These initiatives include
establishing a value realization office, aligning
AI strategy with future scenarios, developing aworkforce re-skilling plan, creating a roadmap
to upgrade data architecture, and curating AI
ecosystem partnerships.

Key SMRs
Alison C Clark

EY UK Advanced Manufacturingand Mobility Leader
alison.clark@uk.ey.com

Siddharth Sapolia

Principal, Technology
Consulting, Ernst & Young LLP
sid.sapolia@ey.com

Farooque Munshi

EY Americas Data and AI
Advanced Manufacturing Leader
farooque.munshi@ey.com

Contacts
Anil Valsan

EY Global Advanced
Manufacturing and Mobility
Knowledge Leader

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About the EY Global Advanced Manufacturing& Mobility Sector

Regan Grant

Urbanization, changing consumer expectations and emerging digitaltechnologies are reshaping what’s possible, from the production anddistribution of goods to the transportation of people. To succeed in this newworld of mobility and smart manufacturing, incumbents must transformthemselves at unprecedented speed — to think like an innovative start-up,
tap into new talent and engage the customer. With experience across thevalue chain and key technology alliances, our teams show clients how tocreate efficiencies now while adopting digitization and optionality for longterm growth. Automotive, transportation, aerospace, defense, chemicals
and industrial products companies can draw on the strength of our networkof cross-industry players and put our diverse range of approaches to use

EY Global Advanced
Manufacturing and Mobility BMC
Leaderregan.e.grant@ey.com

Francisco Almeida

EY Global Advanced
Manufacturing and Mobility
Knowledge Analyst
francisco.almeida@uk.ey.com

Michael Fiske

EY Global Advanced
Manufacturing and Mobility
Knowledge Analyst
michael.fiske@ey.com

Harsha AnandEY Global Advanced
Manufacturing and Mobility
Knowledge Analyst
harsha.anand@gds.ey.com

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