Systemic AI at the
root of manufacturing
The new growth infrastructure
for manufacturers
performance
Reinventing for Human + AI Engineering
2
Götz Erhardt
Senior Managing Director, Supply
Chain and Engineering,
EMEA Lead
Jean Cabanes
Senior Managing Director, Industry and
Enterprise, Industrials,
EMEA Lead
LinkedInLinkedIn
Authors
Götz Erhardt focuses on industrial
strategies, digital and AI-powered
transformations, performance
improvement and new business
models. He supports clients in
designing programs for value
chain reinvention by speeding up
engineering cycle time, product
launch and sustainable manufacturing.
He is known for a strong client focus,
helping organizations boost profits,
institutionalize process excellence
and improve reliability, quality and
throughput at scale.
Jean Cabanes is a senior leader at
Accenture, working with industrial
clients to drive reinvention across
complex products, systems
and process chains. He advises
executive teams on reinvention
through digital technologies, data
and new ways of working. He is
known for a strong client focus,
helping organizations strengthen
engineering effectiveness, speed
and industrial performance at scale.
Roland Mayr
Senior Managing Director, Industry
and Enterprise, Industrials,
Global Lead
LinkedIn
Roland Mayr leads Accenture’s global
Industrials industry, advising executive
teams on reinvention with digital,
AI and data across complex asset
structures and process chains. With
over three decades at Accenture, he
brings deep automotive experience
and has held senior leadership roles
across the company. He is known
for a strong customer focus, helping
companies move beyond products
to deliver relevant, meaningful and
sustainable experiences.
Andreas Egetenmeyer
Manager, Accenture Research,
Industrials, EMEA
Jeff Brehm
Managing Director, Supply Chain and
Engineering, Industrials, Americas
Andreas Egetenmeyer is a market
research expert with more than 15
years of experience. He focuses on
primary and secondary research to
support clients and shape Accenture’s
thinking on disruptive technology and
business trends in Industrials. He is
committed to delivering actionable
insights that empower clients to make
confident decisions and navigate an
uncertain future.
Jeff Brehm focuses on business
transformation within the engineering
and product development space.
He advises executive teams
on reinvention through digital
technologies and an enterprise-wide
engineering strategy. His industry
experience and passion for the
discipline bring credibility that has
made him a trusted advisor to clients
navigating complex engineering and
product challenges.
Tobias Geißinger
Managing Director, Supply Chain and
Engineering, Industrials, EMEA
Tobias Geißinger works with industrial
clients to drive engineering-led
transformation across complex
products, systems and process
chains. He empowers clients to
reinvent through digital tools, AI and
new ways of working. His client-
centered approach consistently
translates into stronger engineering
capability, accelerated delivery and
improved industrial performance.
LinkedInLinkedInLinkedIn
Reinventing for Human + AI Engineering
3
Contents
Page 4Engineering at an inflection point
Page 7
The enablers:
Building a cloud-based digital core
and a single source of data access
Page 9
Five moves to reinvent engineering
processes for growth
Page 22Engineering as a growth engine
Page 20
Integrate engineering across
the value chain
Reinventing for Human + AI Engineering
4
Software-defined products, tighter regulation and relentless cost
pressures are changing what engineering departments must deliver.
By 2030, adaptability and speed will be the defining measures
of performance.
Leading companies recognize that the compounding realities of this decade
cannot be resolved with incremental process tweaks or another tool added
to existing systems. Instead, they are reinventing processes, data, tools and
how work gets done across the value chain to dramatically improve critical
outcomes like cycle times, launch reliability and update velocity.
The findings and insights of this study draw on 136 interviews with
engineering leaders and practitioners at leading aerospace and
defense, automotive and industrial equipment companies across
Asia-Pacific, Europe and North America. The sample includes:
• 36 in-depth expert interviews with engineering decision
makers conducted in January and February 2026
• 100 AI-moderated interviews with front-line engineers
responsible for day-to-day engineering work conducted
between January and April 2026
Reinventing for Human + AI Engineering
5
Engineering at an inflection point
Our new research clearly confirms this. Across our interviews with 100
engineers and 36 engineering leaders, we found legacy systems nearing
their limits. Products are becoming more complex as cycles compress, and
engineers report spending roughly half their day on documentation, reporting
and information search rather than core engineering work. According to
our estimation, this represents a US$ 39 billion annual loss in productivity
The engineering system is under strain in both its operating model
and tool landscape (Figure 1).
Figure 1: Engineering leaders face compounding realities
By the end of this decade, “perfect but late” engineering will leave behind
those who still cling to this paradigm: Late launches repeatedly erode
margin, trust and morale. Meanwhile, as hardware performance converges,
differentiation shifts to software, customer experience and data-driven
improvement. Launch day is no longer the finish line. It’s the start of version
. At the same time, uncontrolled portfolio growth and variant proliferation
are outrunning engineering capacity, while value chain integration has
become the strategy as manufacturing, procurement and suppliers shape
design choices earlier and every change impacts multiple decisions, systems
and schedules.
Crucially, yesterday’s toolchain is blocking AI’s potential: broken data flows,
disconnected systems and friction-filled handoffs force engineers to hunt
for answers, validate consistency manually and chase approvals—exactly the
work AI can eliminate. Finally, the scarcest asset is capability, not compute.
Organizations need cross-domain judgment, deep expertise and fast trade-
offs within safety and compliance guardrails just as they face a shortage of
experienced talent.
The implication is unavoidable: These converging pressures leave little room
for incremental fixes. Leaders who want software-speed innovation without
sacrificing safety, quality, compliance or cost discipline must reinvent
engineering as a system spanning processes, tools, roles, decision rights and
cross-functional interfaces.
2030 will punish
"perfect but late"
engineering
Hardware becomes
ubiquitous,
software becomes
the battlefield and
launch day is just
version
Uncontrolled
portfolio growth
has outrun
engineering
capacity
The value chain
is turning into a
value network
and integration
becomes strategy
The biggest
AI blocker is
yesterday's
toolchain
The scarcest asset
is capability, not
compute
Source: Accenture
Reinventing for Human + AI Engineering
6
Engineering at an inflection point
In this context, AI can move from a support tool to a digital assistant that
executes work in flow, under clear decision rights and controls. One early
market signal came at Hannover Messe 2026, where Siemens introduced the
Eigen Engineering Agent, a new class of industrial AI designed for automation
engineering. Unlike copilots that offer suggestions, the Eigen Engineering
Agent is positioned to plan, code, validate and iterate engineering work end to
end, producing ready-to-use outputs rather than drafts.
So, what does that reinvention require? It starts with an operating backbone
that connects the product story end-to-end: a digital thread built on a
cloud-based digital core. This provides a single source of access that links
requirements, designs, software changes, tests, approvals, quality records,
non-conformities and field data across existing systems. That backbone ties
information to decisions and workflows and establishes clear rules for what
counts as true.
With that foundation in place, leaders can reinvent engineering processes
through five system-level shifts that compress cycle time while strengthening
control: run the V-model as a continuous evidence system, move to model-
based, simulation-first development, automate verification and compliance
at scale, redesign the talent model for AI-augmented engineering and make
partner collaboration structured, not scrambled.
But even the best internal engineering reinvention will stall if work still waits
on cross-functional roadblocks. The same discipline must extend across
manufacturing, procurement, aftersales, suppliers and customers, because
engineering only moves as fast as the value chain around it.
Done well, this reinvention changes engineering’s role from a cost center to a
growth engine. Development cycles shorten because teams gain confidence
earlier and avoid costly rebuild loops. Engineering cost drops as duplication
and rework decline. Launch reliability improves as verification, compliance
and manufacturing alignment become integrated and continuous instead of
backloaded. Feature velocity increases through reusable and modular product
platforms and architectures designed for over-the-air updates and post-launch
iteration. And lifecycle performance strengthens as uptime, service revenue
and continuous improvement compound over time, turning engineering into a
source of competitive strength.
Siemens reports performance gains of up to 5x
faster execution and 50% higher
Reinventing for Human + AI Engineering
7
To make the shift from cost center to growth engine, organizations
need a cloud-based digital core and a single source of access to data.
The core standardizes data, governance and integration so the
continuous, traceable record we call the digital thread can link
requirements, designs, changes, tests, approvals, quality records and
field signals across the product lifecycle. This is hard to achieve. More
than three quarters of C-suite and senior engineering leaders (77%)
maintain more than 200 engineering applications, 29% maintain more
than 500 and only 4% have implemented a digital end-to-end thread,
according to a separate survey we conducted of 234 engineering
Aerospace decision maker:
"Companies are spending 80% of their IT budget
on just keeping the lights on with legacy systems
and they're living with a dinosaur back office."
Reinventing for Human + AI Engineering
8
The practical goal is to make existing systems behave like one governed
product story. Rather than replacing existing systems, the single source
of access connects them, linking data through integration and shared
governance so critical records stay consistent, current and traceable
(Figure 2).
Requirements, approvals and related artifacts should fit together across
the product lifecycle. This allows engineers to work with applications and
AI tools grounded in one trusted view of the product, making it faster to
find the right record, understand its relevance and act with confidence.
CNH Industrial, an agricultural equipment manufacturer, demonstrates
how this looks in practice. The company established a shared single
source of data access for its engineering, sourcing and manufacturing
teams. This helped it embed automated cost intelligence early in the
engineering process, before teams locked in designs. The result was
faster product development. In some cases, design iterations dropped
from as much as 40 hours to minutes, and earlier evaluation surfaced
nearly US$9 million in cost-saving
ERP=Enterprise Resource Planning; MES=Manufacturing Execution System; EBOM=Engineering Bill of Materials;
MBOM=Manufacturing Bill of Materials; MRO=Maintenance, Repair and Overhaul; IP=Intellectual Property;
API=Application Programming Interface
Figure 2: The layers of the single source of access
The enablers: Building a cloud-based digital
core and a single source of data access
Source: Accenture
Reinventing for Human + AI Engineering
9
With that foundation in place, the reinvention moves that
follow (Figure 3) become practical: evidence can accumulate
continuously, models stay linked to requirements, teams generate
compliance in flow and partners can work against the same
governed baseline.
Just as important, AI stops being a set of isolated tools and becomes
a working layer of the engineering system by classifying artifacts,
flagging broken trace links, drafting compliance narratives, identifying
skill gaps and detecting interface conflicts.
In a reinvented engineering system, AI turns governance and
execution into something that happens continuously, at the speed
and scale modern engineering demands.
Reinventing for Human + AI Engineering
10
Five moves to reinvent engineering
processes for growth
Many teams still run the V-model like a relay race burdened with overly
sequential execution, fragmented handovers and weak evidence flow
across functions.
Bringing the V-model to the next level starts with making the process
explicit. Define what “good” looks like at each stage, including the
required inputs and outputs, named owners, decision gates and a
small number of deliberate freeze points. When these rules are clear,
AI can surface where decisions slip, draft decision gate checklists and
help teams arrive at reviews with complete, comparable evidence.
From there, build a minimum viable digital thread across one
product line and one critical path through the V-model. The goal is
practical coverage, not universal perfection. Connect requirements
to architecture, designs, tests and approvals so every claim has a
visible chain of evidence. AI speeds this build by classifying artifacts,
Run the V-model as a
continuous evidence system
Figure 3: The roadmap to reinvent engineering processes
Engineering
today
Engineering
of the futureRun the V-model as a continuous evidence system1
1
2
3
4
5
Move to model-based,
simulation-first development
Automate verification
and compliance at scale
Redesign the talent model for
AI-augmented engineering
Make partner collaboration
structured, not scrambled
Source: Accenture
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Reinventing for Human + AI Engineering
11
suggesting trace links and flagging broken chains before they
become late surprises. Over time, that traceability turns validation
from a late-stage scramble into a continuous activity that keeps pace
with shorter cycles.
Finally, extend the V-model beyond launch. Treat field signals as the
opening input to the next cycle so updates, upgrades and fixes start
with real use, then move through the same disciplined evidence flow
as initial development. AI helps teams to make that loop actionable
by clustering issues, suggesting likely impact areas and highlighting
changes that require fresh verification evidence. When engineering
works this way, teams stop relearning lessons late and audits stop
becoming fire drills.
BMW shows what happens when traceability becomes an operating
capability. The global car manufacturer needed faster access to data
from its development vehicles and a better way to track signals across
prototyping cycles. It built a Mobile Data Recorder (MDR) solution on
Azure that monitors and records more than 10,000 signals twice per
second across a fleet of 3,500 development cars. BMW then added
an AI copilot that lets engineers query this vehicle data in natural
language. Together, the solution delivers data to engineers, and
supports analysis, about 10x
Leads to systemic changes:
• Teams continuously capture evidence
• Decision ownership is explicit
• Traceability supports decision making
• Field signals feed the next cycle
Five moves to reinvent engineering
processes for growth
Running the V-model as a continuous evidence system
Reinventing for Human + AI Engineering
12
As differentiation shifts to software, learning late in hardware builds or
software integration costs months, not just money. One engineering
leader told us, “When we build a circuit, we may have to rebuild that
circuit three to four times, and it takes two to two and a half months
each time. If we get it right the first time, we save almost half a year’s
worth of work.” That is the real cost of low-trust models: Teams
discover architectural or interface issues after build, integration or
release, then burn months validating and refactoring across sprints
what could have been proven earlier.
Our survey respondents expect AI-enabled process planning,
simulation and optimization to deliver at least 20% productivity
That upside depends on being explicit about what
each model can and cannot predict, and on linking hardware and
software architecture and interface models to requirements so issues
surface before teams deploy code. AI accelerates the diagnosis by
mining program history for recurring failure modes and surfacing
where design assumptions most often collapse in test.
Model-based development becomes robust and highly effective when
models connect directly to requirements and architecture, and when
teams reuse validated work instead of rebuilding it. A reusable model
library helps teams carry forward proven assumptions, boundary
conditions and verification results across programs. AI supports that
reuse by summarizing assumptions and surfacing prior models that
match a new design context. As one industrial leader we interviewed
put it, “We used to need a CAD operator and a simulation engineer.
Move to model-based,
simulation-first development
Five moves to reinvent engineering
processes for growth
At least
20%
increase in
productivity
improvements
with AI-enabled
process planning6
2
Reinventing for Human + AI Engineering
13
Today, an agent can generate design options and run them through
simulation automatically. Teams get more designs without the
handover delays, so development moves faster.” The lesson is simple:
Speed comes from removing handoffs and making exploration fast,
while maintaining standards for evidence and aligning hardware and
software early.
Siemens Energy shows what changes when simulation and virtual
development are embedded parts of the core product development
system. To build hydrogen-capable gas turbines and reduce CO2
emissions, it created an engineering digital thread that integrated
all product data and engineering tools like Siemens Teamcenter and
Simcenter. This helped engineers visualize, simulate and optimize
turbine designs globally across engineering teams. As a result, Siemens
Energy’s engineering team completed 26 design iterations—four times
more than with traditional methods—and delivered the world’s first
100% hydrogen gas turbine in record
Leads to systemic changes:
• Learning moves upstream
• Models stay linked to requirements and architecture
• Product teams rationalize platforms and variants
• Engineers reuse validated models and
routinely correlate them
• Development programs reserve physical prototypes for
proof that the virtual model cannot provide
Five moves to reinvent engineering
processes for growth
Moving to model-based, simulation-first development
Reinventing for Human + AI Engineering
14
Verification demand is rising faster than testing capacity. Half of
surveyed C-suite and engineering leaders cited validating frequent
software releases in regulated or safety-critical environments as a
top When compliance becomes a late scramble, speed
collapses at the gate and teams burn scarce engineering time
rebuilding evidence instead of improving the product. Leaders can
reverse that pattern by treating verification and compliance as a
continuous system, not an end-phase activity.
Start by standardizing requirement quality and evidence structures so
they stay testable and traceable. Clear acceptance criteria, consistent
evidence templates and disciplined language reduce ambiguity and
make automation possible. AI supports this work by flagging vague
phrasing, suggesting acceptance criteria and drafting test ideas that
teams can review and refine. When requirements become machine-
readable in practice, engineers spend less time interpreting intent and
more time validating outcomes.
Automation scales when testing and configuration control sit inside
the development pipeline. Teams need to know what changed, what
was tested and what deserves regression attention without manual
reconciliation. That requires rigorous change control tied to the
requirements and architecture each change affects. As verification
crosses domains, test results must remain linked to the requirement or
architecture they prove, so gaps surface before decision gate reviews.
Finally, generate compliance and cybersecurity evidence
continuously. Instead of assembling documentation at the end,
teams should maintain an always-current evidence pack aligned to
the relevant standards, policies and controls. AI can draft evidence
narratives and flags new exposure when changes introduce risk.
When evidence accumulates continuously, sign-off becomes a review
of what already exists, not a last-minute construction project.
Automate verification
and compliance at scale
Five moves to reinvent engineering
processes for growth
50%
of surveyed
C-suite and
engineering
leaders cited
validating
software releases
in regulated or
safety-critical
environments as
a top challenge8
3
Reinventing for Human + AI Engineering
15
The experience of Daiichi Elektronik, a Tier 1 automotive supplier,
shows what it looks like when verification and compliance run
inside the development flow rather than at the end. As automotive
manufacturer expectations moved toward software-defined vehicles,
Daiichi Elektronik had to raise the standard of proof without slowing
development. The automotive electronics supplier responded by
using software provider PTC’s Codebeamer to establish a governed
workflow linking requirements, tests, defects and releases end to end.
It built a compliant framework in under one year and reduced the
core implementation effort from nearly a year to two months. With
traceability and defect tracking integrated from the start, teams found
issues earlier and reduced the rework that often appears with late-stage
evidence
Leads to systemic changes:
• Engineers write requirements that are testable
from the start
• Testing and configuration control sits inside the
development flow
• Multi-domain evidence stays linked to what it proves
• Continuous workflows generate compliance and
cybersecurity evidence in real-time
Five moves to reinvent engineering
processes for growth
Automating verification and compliance at scale
Reinventing for Human + AI Engineering
16
Skill shortages and siloed roles constrain system-level
delivery as much as weak tools. But this moment is also an
opportunity to redesign how engineering work gets done so
people can achieve more, faster, with less friction. Building and
retaining the right capabilities remains difficult, with 59% of
executives finding it challenging to acquire or develop AI talent,
according to our Accenture Pulse of Change
Real value in AI-augmented engineering comes from building a
Human + AI workforce with humans in the lead, where routine work
shrinks and engineers spend more time on judgment, creativity and
problem solving. The opportunity lies in reinventing ways of working
so people and AI continuously learn and adapt together in the flow
of work. Closing the gap between pilots and production starts with
clearer ownership, more fluid teaming and operating models and
explicit human accountability for decisions, trade-offs and risk so
execution speeds up without eroding trust, quality or morale.
Begin by making skill gaps visible and assigning clear owners for
cross-domain decisions. Teams move faster when one person or a
small group can resolve trade-offs across mechanical, electrical,
software and compliance constraints without waiting for serial
signoffs. AI can help identify recurring bottlenecks, show where
decisions stall and tailor learning paths to the tools, models and
artifacts each role actually uses.
As work evolves, organizations also need more hybrid profiles that
carry context across domains. These hybrid engineers, spanning
mechanical and electrical, or software and hardware, don’t replace
deep specialists; they cut coordination overhead by linking
requirements, models, tests and change records so intent and
decisions carry across boundaries.
Redesign the talent model for
AI-augmented engineering
Five moves to reinvent engineering
processes for growth
59%
of executives find
it challenging
to acquire or
develop AI talent10
4
Reinventing for Human + AI Engineering
17
Scale AI automation progressively. Start with understanding work at the
task level so teams can identify repetitive steps such as documentation,
summarizing change requests, classifying issues and generating first-
pass test cases. Then, build an automation roadmap starting with these
tasks under clear AI usage rules and human signoffs. As trust in AI
agents builds, expand toward architecture and decision support, where
AI can surface options, constraints and precedent while engineers retain
accountability for trade-offs, approvals and risk acceptance.
The result is more capacity for higher value work without blurred
responsibility because teams spend less effort on coordination and
more on engineering judgment. ABB provides a concrete example of
that shift at scale. In RobotStudio, its simulation tool used by more
than 60,000 engineers, ABB integrated NVIDIA Omniverse libraries so
teams can design, program, test and validate automation cells before
deployment. This approach delivered 99% simulation-to-real correlation,
cut deployment costs by up to 40%, sped time to market by as much as
50% and reduced setup and commissioning by up to 80%.11
Leads to systemic changes:
• Cross-domain ownership is explicit
• Hybrid roles carry context across domains
• AI handles repetitive, low-value tasks
• Human judgment is installed as final decision gate
Five moves to reinvent engineering
processes for growth
Redesigning the talent model for AI-augmented engineering
Reinventing for Human + AI Engineering
18
Partners now influence performance, compliance and update
speed directly, yet many programs still manage them through email,
inconsistent data and late integration tests. That approach fails
when suppliers design major subsystems and when evidence must
stay intact across organizational boundaries. One senior aerospace
engineer manager put it bluntly, “Seventy percent of an airplane
is built by the supply chain. So, if I don’t have an integrated supply
chain in lockstep with what I’m developing and manufacturing, I am
doomed for failure.”
Start by defining core versus partner boundaries early, then set
data-sharing rules that match the risk profile of the program. Clear
baselines, explicit access controls and a shared definition of approved
artifacts reduce intellectual property anxiety and version churn. AI
can support governance by classifying datasets, recommending
access controls and flagging oversharing before sensitive material
leaves the organization.
This governance only works if partners collaborate in controlled
digital environments linked to the single source of access. The
goal is one working baseline, not weekly reconciliation of files and
interpretations. When teams share the same requirements, interfaces
and change records, integration problems surface earlier and
engineering time shifts from chasing status to resolving issues.
Standardize supplier onboarding and resilience planning so a partner
change does not force a rebuild of the product story. Common
checklists, system interface definitions, evidence expectations and
escalation paths help partners plug into the operating model quickly.
Make partner collaboration
structured, not scrambled
Five moves to reinvent engineering
processes for growth
5
Reinventing for Human + AI Engineering
19
With these foundations in place, co-development and co-validation can
move faster without losing control. Shared environments allow teams
to test interfaces, run integration checks and validate evidence against
agreed baselines long before mismatches become physical problems.
AI adds speed by detecting interface conflicts, spotting anomalous test
results and accelerating review cycles by routing the right evidence to
the right owners.
Volkswagen’s expanded engineering hub in Hefei, China shows
what structured collaboration can achieve. It supported faster co-
development and co-validation across a local ecosystem including in-
depth technology collaboration with XPeng, a leading Chinese electric
vehicle manufacturer. By combining software, hardware and full-vehicle
validation under one roof and coordinating work with joint-venture R&D
teams, the company anticipates reducing vehicle development cycles
by 30%. The setup also supports earlier supplier integration in the
concept phase, shortening decision loops, adapting features faster to
Chinese customers and surfacing integration issues before they become
late-stage
Leads to systemic changes:
• Leadership defines core and partner boundaries early
• Partners work in controlled digital environments linked to
the same product story
• Supplier onboarding becomes structured and standardized
• Co-development and co-validation run against
shared baselines
Five moves to reinvent engineering
processes for growth
Making partner collaboration structured, not scrambled
Reinventing for Human + AI Engineering
20
The five reinvention moves
described above speed
engineering processes
inside the function, as we
heard in our interviews.
But cycle time still breaks
when work crosses the value
chain and waits for answers,
approvals, parts or fixes.
Engineering has to further
orchestrate decisions and
evidence across the network,
not just optimize its own
lane (Figure 4).
Figure 4: Engineering at the center of the value network
Engineering
Aftersales
Procurement
Customers
Sales &
Marketing
Manufacturing
Suppliers
Source: Accenture
Reinventing for Human + AI Engineering
21
Engineering Procurement:
If you can’t source it, you can’t ship it
Supply risk and part substitution can rewrite a program overnight. If
sourcing enters too late, engineering redesigns under pressure. Bring
procurement into architecture early so designs reflect supplier capacity,
alternatives and cost targets.
Engineering Manufacturing:
Design it for the line, from day one
Manufacturing now influences speed as much as design. If plant
constraints appear after design freeze, late fixes hit cost and schedule.
Bring manufacturing in early and tie changes to one shared record.
Engineering Sales and Marketing:
Turn demand into clean choices early
Market demand only creates value when it translates into clean choices
early. Last-minute feature requests and variant proliferation create noise,
not growth. Translate demand into clear, testable requirements, but
also use the process to say no. Great product management is portfolio
gatekeeping: disciplined decisions on features and variants allow only
those changes to enter engineering that create real customer and
economic value.
Engineering Aftersales and Service:
Make uptime a design input, not a repair bill
Uptime is now a design outcome. If service only sees the product at the
end, engineering learns too late and repeats failures. Treat service as a
lifetime value co-owner so field issues shape future requirements.
Engineering Suppliers:
Co-engineer with rules, speed and trust
Design and launch roadmaps depend on partner capability, but ad
hoc collaboration creates IP anxiety and version churn. Structured
co-development with clear data boundaries, shared evidence and
disciplined change control speeds integration and lowers risk.
Engineering Customers:
Let real use steer what you build next
Customer reality changes faster than annual planning cycles. If
feedback arrives late, teams build for yesterday. Create a continuous
channel for customer and fleet signals, then feed them into engineering
as priorities teams can validate fast.
Integrate engineering across the value chain
Reinventing for Human + AI Engineering
22
By 2030, the best engineering organizations will win on speed
and economics, defined by how quickly and cost-effectively
they can launch new products and keep improving them without
compromising safety, quality, cost discipline or compliance.
Based on our interviews with engineering leaders and hands-on
engineers, along with cross-industry analysis, we see a practical
path to this Human + AI engineering advantage. It requires building
a modern, cloud-based digital core that enables a single source
of access; using AI to convert legacy knowledge into connected,
decision-ready evidence; and redesigning how work flows through
engineering and across the value chain.
Companies making these moves are building engineering
organizations designed for both speed and control, and for
continuous product improvement after launch. But that future won’t
arrive on its own. The work must start now.
Reinventing for Human + AI Engineering
23
To get started, CEOs and other C-suite executives, along with
engineering leaders, should pick one product line where delays hurt
most and where they can prove value quickly. Stand up the cloud-based
digital core, then use AI to locate the critical data, standardize the
minimum set of data basics and surface broken trace links before they
become late surprises. Put the reinvention moves into daily practice
with clear decision gates, named owners and metrics that track cycle
time, launch performance and lifecycle impact. Then extend the same
discipline into manufacturing, procurement, service, suppliers and
customers so speed holds when work crosses boundaries.
Done well, this approach does more than streamline engineering; it
elevates engineering’s role in the business. Rework declines, launches
become more predictable and update cycles accelerate. The strategic
payoff is larger. Engineering shifts from a cost center to a growth
driver. Products reach the market sooner and at lower cost, freed from
the inefficiencies and delays that erode margins. Over time, the value
compounds through higher uptime, new service revenues and the
reuse of platforms and components, so each innovation becomes a
foundation for the next.
Engineering as a growth engine
Industrial decision maker:
"If you don't have someone at
the top who says, ‘I’m here, I’m
supporting this,’ you will not get
the organization to do this."
Reinventing for Human + AI Engineering
24
How Accenture can help
Accenture helps Industrials, including aerospace and defense,
automotive and industrial equipment companies, turn Human + AI
engineering from ambition into operating reality. We combine deep
engineering and industry expertise with the ability to design, modernize
and run engineering environments end to end.
Our capabilities span engineering process and methods advisory,
technology modernization, talent transformation and hands on
engineering execution. Because our teams work in real engineering
contexts, we help clients build transformations that are practical, scalable
and adopted in daily work.
We modernize the engineering stack by connecting PLM, ERP, MES and
data platforms into a governed digital backbone. That backbone links
legacy environments into a single product story across requirements,
design, software, testing and field data, without disruptive rip-and-
replace programs. Strong data standards and governance make
engineering information traceable and ready for AI.
What distinguishes Accenture is execution depth. Thousands of our
practitioners work directly in engineering roles, helping clients embed
AI, automation and model-based approaches into workflows with clear
human accountability. Our talent and organization expertise ensures
roles, skills and collaboration models evolve with technology.
With deep supply chain and engineering expertise and responsible
AI built in, Accenture helps shift engineering from a cost center to a
resilient, AI-enabled growth engine.
Reinventing for Human + AI Engineering
25
About the research
Accenture Research conducted 36 expert interviews with senior
engineering leaders and former board members at leading aerospace
and defense, automotive and industrial equipment companies across
Asia-Pacific, Europe and North America in January and February 2026.
These interviews explored current engineering challenges related to
processes and tools, the use of AI and collaboration between engineering
and other business functions as well as external stakeholders. We also
examined potential challenges and solutions involved in reinventing
engineering more broadly.
In addition, we conducted 100 AI-moderated online interviews with hands-
on engineers in the aerospace and defense, automotive and industrial
equipment sectors across Asia-Pacific, Europe and North America between
January and April 2026. The questionnaire sought to understand engineers’
day-to-day challenges. This helped us assess how engineers currently
allocate their time between core engineering and administrative tasks,
identify the day-to-day challenges of using AI tools and examine the
gap between the strategic goals of senior engineering leaders and the
daily working reality of hands-on engineers. Unless otherwise stated, all
insights, findings and quotes in this report are derived from the interviews.
These qualitative findings are supplemented by Accenture’s proprietary
quantitative research.
The main quantitative research survey leveraged is the Buyer Behavior
in Product Engineering—Voice of the Customer Survey (2026). This joint
Accenture and Everest Group report surveyed 234 senior engineering
leaders across eight industry sectors to understand how global disruptions
are reshaping enterprise priorities, redefining engineering focus areas and
influencing the evolving role of engineering service providers in the market.
We use generative AI in our research production process. Our research
experts review and validate generative AI outputs using traditional
research methods where possible and they apply Accenture’s
Responsible AI standards.
Reinventing for Human + AI Engineering
26
References
Acknowledgements
The authors thank the following individuals for their contributions to this report:
Research & Editorial:
Rohan Amrute, Aditi Bhatnagar, Abhishek Mishra, Piotr Pietruszynski, Iys Suyambulingam,
Rebecca Tan, Emily Thornton, Meredith Trimble, Matthias Wahrendorff and Jeff Wheless
Marketing + Communications:
Eva Erichsen and Kati Haarla
The authors extend special thanks to subject matter experts Alexander Albers, Tracey Countryman,
Dirk Molitor, Jeff Pitcock and John Warlick for their insights and contributions to this research.
1. This estimate is based on the interviews conducted for this study and official statistical data covering both the
number of software and hardware engineers employed by aerospace and defense, automotive, and industrial
equipment companies in the United States, the European Union, China and Japan, and their hourly and annual
wages by engineering discipline, industry, and location.
2. Siemens, Eigen Engineering Agent | Siemens, April 24, 2026
3. Accenture in cooperation with Everest Group, Buyer Behavior in Product Engineering—Voice of the Customer
Survey, January-March 2026. N=234
4. aPriori, Design To Cost Helps CNH Accelerate Product Development, March 10, 2026.
5. Microsoft, BMW Group innovates with Azure for 10 times more efficient data delivery | Microsoft Customer
Stories, January 28, 2025.
6. Accenture in cooperation with Everest Group, Buyer Behavior in Product Engineering—Voice of the Customer
Survey, January-March 2026. N=234
7. Siemens, The digital advantage for Siemens Energy - Building a sustainable future - NX Manufacturing,
August 25, 2025.
8. Accenture in cooperation with Everest Group, Buyer Behavior in Product Engineering—Voice of the Customer
Survey, January-March 2026. N=234
9. PTC, Daiichi Elektronik Turns Compliance into a Growth Engine with Codebeamer, January 26, 2026.
10. Accenture, Accenture Pulse of Change, November and December 2025. N=391
11. Nvidia, ABB Robotics Taps NVIDIA Omniverse to Deliver Industrial Grade Physical AI at Scale | NVIDIA Blog,
March 9, 2026.
12. Volkswagen, Test Center Complete: Volkswagen Group Now Able to Fully Develop and Validate Products in China
for China | Volkswagen Group, November 25, 2025.
Reinventing for Human + AI Engineering
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