Enterprise AI Maturity Index 2026
New research confirms that many orgs bought AI, but few built for it. Those that did are seeing impressive ROI; the rest are battling chaos.
3 Everyone bought AI. Few built for it
6 Chaos is growing
13 Automating yesterday’s work
22 Reinvention starts here
32 Your AI roadmap
39 Appendix
Table of contents
3
Introduction
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Everyone bought AI. Few built for it
44
Is the world getting better at AI?
Amit Zavery
President, Chief Product Officer,
and Chief Operating Officer
ServiceNow
For three years, we have been asking this simple question.
This year, the answer requires an honest look at what is
actually happening underneath the covers.
AI spending is up 110%. Executive confidence has rebounded.
Organizations have committed. By the surface measures,
enterprise AI is moving fast. But beneath the momentum,
the infrastructure required to execute AI at scale is largely
not there. Data often sits in silos. Governance is frequently
an afterthought. Investments remain fragmented. Point
solutions are solving isolated problems. Pockets of progress
don’t connect.
Buying AI and building for it are not the same thing, and the
gap between the two is where competitive advantage is
won or lost.
What I have learned from working with organizations around
the world is this: Technology matters only when it delivers real
outcomes for real people. Not technology for its own sake.
Outcomes. And the hard truth this research surfaces is that
most organizations are still automating yesterday’s work
instead of reimagining tomorrow’s.
The Pacesetters in this research took a different path.
They unified their data, built governance in from the start,
and invested in their people with ongoing upskilling, not
one-time training. The returns justify every difficult decision
it took to get there.
And here is what Pacesetters prove about what AI can be:
When workers are freed from low-value, repetitive work, they
focus on the problems only humans can solve. They build
the relationships that drive real growth. They do the work
that matters.
What follows is a rigorous look at what separates organizations
being transformed by AI from those still running it on broken
infrastructure. It is a roadmap built from what is working.
The foundation you build today determines the returns you
realize tomorrow. We are just getting started.
Introduction
Introduction Chaos is growing
Chaos is growing Your AI roadmap
Your AI roadmap Contents
Contents Automating yesterday’s work
Automating yesterday’s work Reinvention starts here
Reinvention starts here Appendix
Appendix
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When AI spending more than doubles in a single year, the question stops being whether
organizations are committed. It becomes whether they’re built for what comes next.
To find out, we partnered with ThoughtLab to survey 4,500 executives—and for the first
time, 2,000 employees—across 19 countries and 12 industries.
We measured AI progress across seven pillars: vision and leadership, management
and culture, data modernization, AI governance, talent and skills, AI-enabled workflows,
and driving value from AI. Full methodology details are available in the appendix.
A small group made a different bet. Early.
While most organizations deployed AI into existing infrastructure and moved fast,
a handful paused to ask a harder question: Are we building something that will scale
or something that will break? That distinction is now showing up in the results—and the
gap is widening. The organizations pulling ahead aren’t those with the most advanced
models. They’re the ones with the infrastructure to execute reliably at scale.
We call them Pacesetters, and what they do differently is a roadmap for others.
They represent 21% of this study’s organizations. They are defined not by budget or
industry, but rather by orchestration, connected data, and governance built before
deployment, not after. This report shows how they got there and what others can do
to follow.
The gap between AI ambition and execution
Most are bolting AI onto broken infrastructure
Investment is outpacing infrastructure Only 16% of organizations have replaced fragmented legacy
systems with an integrated platform. The money is moving.
The architecture isn’t.
Agentic AI is everywhere. Autonomous work is not Fifty-nine percent of organizations have moved beyond
piloting agentic AI, but only 9% have made meaningful
progress building autonomous, multistep workflows. Most are
paying for a capability they haven’t unlocked.
Confidence is masking an execution gap Overall, AI maturity rebounded to 51 out of 100. But AI-enabled
workflows score just 40—the lowest of all seven pillars. The
ambition is real. The infrastructure to execute it isn’t.
Introduction
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Underneath the confidence, chaos is growing
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44 2024
35 2025
AVERAGE AI MATURITY
51 2026
100
AI maturity rebounds. Execution at scale lags
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After declining in 2025, when rapidly advancing technology outpaced
organizations’ ability to keep up, AI maturity has made a comeback, climbing
16 points to reach 51 out of 100 in 2026.
Organizations are entering an execution phase with clearer direction, stronger
leadership, and measurable accountability.
Organizations are most advanced in AI vision, strategy, and leadership—
and in establishing the management and culture that will sustain AI growth.
Compared to last year, many have also made some progress on data modernization,
governance, and generating value from AI, suggesting organizations are trying to
close the gap between AI ambition and AI infrastructure.
Two pillars tell a different story, highlighting cracks that weaken execution and
long-term success.
Talent and skills
Although organizations have made the most year-over-year progress here,
few have tailored, ongoing AI training and long-term HR plans to foster human
and AI collaboration.
AI-enabled workflows
Maturity for this pillar is lowest at 40, a reflection of just how much more
challenging orchestration is than automation.
Average pillar scores
100
57
Vision and leadership
54
Management and culture
54
Data modernization
53
AI governance
53
Driving value from AI
49
Talent and skills
40
AI-enabled workflows
0
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Something fundamental shifted this year. AI stopped being a line item that
organizations debate and became one they defend. Investment grew 110% in
a single year. By 2027, AI spend is projected to rise another 81%, representing
more than 20% of an organization’s IT budget.
The sharp rise is driven by new AI solutions, the need to update underlying
IT infrastructure, and the pressure to deliver results: profits, growth, and
competitiveness. This kind of acceleration doesn’t happen unless leadership
is convinced AI is existential, not experimental.
The pattern holds across every sector. Government organizations increased
AI spend by 140% year over year, more than any other industry surveyed. This
signals that even the most process-bound institutions are moving with urgency.
In manufacturing, spend rose 126%, driven by pressure to modernize operations
built on decades of legacy infrastructure. Different industries. Different drivers.
The same fundamental commitment.
Survey-reported projection
Revenue spent on IT by 2027
Up from 4.6% in 2025 8.2%
IT budget allocated to AI by 2027
Up from 13.1% in 2025 20.4%
YoY increase in AI as % of revenue
2025 110%2026
AI spend surges. So does the pressure to deliver
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The foundation has cracks
Most executives still wrestle with the prerequisites for AI at scale: data
modernization, solid governance, seamless integration across systems, and
the IT infrastructure to support autonomous execution. A brilliant AI agent can
become a dangerous liability if it isn’t built on the right foundation.
The most important part of that foundation is data modernization—which
executives see as the No. 1 challenge to AI adoption. Poor accuracy, access,
and management set organizations up for failure.
Other top AI challenges include lack of transparency and potential for
misinformation—cited by six out of 10 executives—with regulatory and
compliance complexity close behind. And only 20% have implemented AI
testing, auditing, and risk assessment processes. As AI takes on more
decision-making, the absence of clear governance frameworks amplifies the
risk of deploying AI agents.
Legacy system integration and inadequate IT infrastructure are hurdles for many executives, showing that their organizations are trying to scale AI without the connected, unified foundation good execution requires. As a result, early wins remain fragile and difficult to replicate beyond isolated use cases.
The pattern is consistent: Organizations that bolt AI onto fragmented systems get fragmented outcomes. The investment grows. The results don’t.
As AI scales, cracks become chasms
WHERE MOST ORGANIZATIONS GET STUCKWHERE MOST ORGANIZATIONS GET STUCK
Inadequate data accuracy, access, and management
Concerns over integration with legacy systems
Lack of IT infrastructure to facilitate AI
71%
47%
45%
Data and infrastructure
Lack of transparency and potential for misinformation
Regulatory and compliance complexity and constraints
Data privacy and security concerns
59%
56%
49%
Governance and risk
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While all organizations grapple with common barriers of data, governance, and infrastructure,
local challenges around culture, leadership practices, and regulatory concerns shape the path
to AI maturity differently around the globe.
Global barriers. Local pressure points
In Canada, potential job loss from AI is top of
mind. This also makes it harder to select the right
AI use cases that will drive value with minimal
workplace disruption.
In the United States, leadership commitment to AI
strategy is a clear gap. In fact, 57% of employees think
that their leadership teams are not staying on top of
fast-changing market trends.
Because of structural, regulatory, and investment
constraints, organizations in Mexico have greater
concerns about legacy systems than most
other countries.
Customers in France are resisting AI use more than
those in other countries. Only 42% of execs there say
that clients are happy with AI-enabled experiences,
compared, for example, with 63% in the US.
In Germany, legal liability and intellectual property
risk looms larger because of the country’s strict
regulatory environment and claimant-friendly courts.
In the United Kingdom, concern about job loss
runs deep, creating tension between the urgency to
move forward and the need to bring people along.
In Saudi Arabia, employee resistance to AI is
higher due to more rigid, risk-averse workplaces,
compounded by siloed data and overlapping tools.
In Australia, trust in AI-generated outputs and
accountability is the defining tension for leaders.
Without confidence in what AI produces, progress
stalls before it starts.
In India, legacy infrastructure is a bigger barrier
than in many other countries. That is because many
critical services were digitized late on low-cost stacks,
which are now tightly bound to national platforms.
In Japan, leadership challenges are a greater drag
on AI progress. By the time consensus-driven
decisions are reached, the pace of AI has moved
on. Further, six out of 10 employees think their
leadership teams are not up to the task.
AMERICAS EUROPE AND MIDDLE EAST ASIA-PACIFIC
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Intention over speed
Holly Briedis
SVP, Global Industries and Solutions
ServiceNow
Across banking, healthcare, manufacturing, government—you
name the industry or the market—I see the same pattern
playing out. The organizations pulling ahead with AI are not
the ones that moved first. They are the ones that asked a
better question.
They didn’t ask: how do we use AI to do this faster? Instead,
these leaders asked a more foundational question: Should
this work exist at all?
Automating a broken process doesn’t create value. It simply
makes the problem happen faster.
I’ve seen organizations invest heavily in AI to accelerate
workflows that should have been redesigned or retired. The
technology worked exactly as expected, but the outcome
delivered very little. When you contemplate that much of this
work is still happening in disconnected siloes with fragmented
tech, the organizational impact was stunted before it began.
However, the many (but still not majority of) leaders getting
this right take a step back before deploying a single agent.
They ask whether the process reflects the organization they
are building toward or the one they inherited. They treat AI
as a design decision, not a technology deployment. And they
have the leadership clarity and courage to ask: what are we
trying to become?
That discipline is what separates organizations that are
experimenting with AI from the ones that are truly
transforming with it.
In the end, the advantage isn’t speed or volume or
technical sophistication.
It’s intention.
Introduction
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Appendix
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Automating yesterday’s work vs. reinventing tomorrow’s
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From unfamiliar to almost everywhere in 12 months
A year ago, 71% of executives said they were not very familiar with agentic AI,
and only one-third of organizations were piloting or operating an agentic AI use
case. This year, the percentage of organizations using agentic AI has jumped to
59%, and another 30% are piloting it.
That is one of the fastest adoption curves in the study’s three-year history.
For many, autonomous work is still out of reach
Most organizations are using AI agents to assist individuals, which is useful,
but a long way from autonomous. Only 5% are redesigning work, as opposed
to using AI agents to autonomize existing workflows. Zero percent of
organizations surveyed have built a cross-functional, self-improving agentic
operating system. Most organizations still use agentic AI as a set of
disconnected assistants, not as a coherent operating layer that maximizes
end-to-end performance and scales pilots to production.
One reason organizations are moving slowly on autonomous execution is risk
avoidance. When guardrails aren’t in place, agents can take unintended actions
across interconnected workflows faster than anyone can catch them, amplifying
data bias, operational errors, and compliance breaches. Moving from assisted
to autonomous isn’t just a technology decision. It’s a governance one.
Agentic AI is everywhere. Autonomous work is not
Few are progressing on agentic AI initiatives
P e
rc e
n ta
g e
o f
o rg
a n
iz a
tio n
s
No progress
Exploring/ Piloting
Assisting individuals,
no autonomous action
Redesigning work
Creating cross-
functional systems
Scaling existing
agentic AI
0
20
30
40
50
11%
30%
41%
13%
5%
0%
10
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59% of organizations are using agentic AI
have made significant progress
using agentic AI to create
autonomous, multistep workflows
9% ONLYMost are paying
for a capability they haven’t yet unlocked
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Where agentic AI is already delivering
BANKING
Customer onboarding
We used AI agents to guide the masses of customers about a loan application guide from start to the end.
CTO, UK
“
RETAIL
Point of sale or e-commerce
We leveraged commerce tools to build an AI-enabled agentic commerce channel.
SVP, Mexico
“
HEALTHCARE PROVIDER
Medical coding and documentation
AI automatically summarizes patient visits and creates structured clinical notes, reducing physician documentation time.
Director, India
“
GOVERNMENT
Public engagement
[We are] deploying conversational AI agents that handle thousands of routine citizen inquiries 24/7 with empathy and speed.
SVP, Belgium
“
TELECOMMUNICATIONS
Service assurance
We deployed AI agents that automatically optimize routing, bandwidth, and security across our network.
Sr. AI engineer, US
“
TECHNOLOGY
Maintenance
Agentic AI automates routine infrastructure tasks, ensuring outcome-driven service delivery.
Director, France
“
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Fragmented systems, disjointed outcomes
Most organizations are scaling AI on top of infrastructure that was never built for it. Only
16% of survey respondents have widely or fully replaced fragmented legacy systems
with an integrated platform. The rest are bolting AI onto disconnected architectures
and expecting end-to-end results. In that situation, AI fails to get the complete, real-time
view it needs to act reliably. Without integrated foundations, use cases stay isolated
and hard to scale.
Data silos put blinders on AI
Not only are systems fragmented—so is data. Forty-one percent of employees rank data
silos among their organization’s biggest AI mistakes. Similarly, 41% of organizations cite
siloed data as a major challenge in AI adoption.
When information is scattered across business units, it lacks the context required for
accurate, trustworthy AI outputs. AI returns partial answers, struggles with complex
queries, and cannot consistently support the decisions that matter.
AI can’t reinvent work it can’t connect
of employees say their organization is not doing a good job connecting AI-enabled workflows across the enterprise
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Employees are willing. Organizations are not prepared
Employee perspectiveExecutive perspective
23%
37%
Improves morale, job satisfaction
Enables focus on high-value work
Reduces skills due to AI dependency
Raises moral and ethical concerns
18%
34%
46%
67%
45%
71%
Employee-employer disconnect gets in the way of aligning
investment to impact
Personal use of generative AI tools has accelerated employee
enthusiasm faster than most leadership teams have recognized.
Employees are significantly more likely than executives to believe
AI will improve their job satisfaction, enable higher-value work, and
strengthen collaboration. They are also far less worried about the
downsides. Executives are overestimating the cultural resistance
and underestimating the readiness.
Organizations that stay in touch with employee sentiment can
move faster, with more confidence and greater workforce
alignment behind their AI investments.
The workforce is enthusiastic. Leadership hasn’t caught up
What impact will AI have on employees?
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AI is moving faster than workforce readiness
Employees might be feeling good about the
impact of AI on their tasks, but nearly half
believe their jobs will become less necessary
as AI agents evolve. Many believe their
organization is not doing a good job training
them. Long-term HR plans and redesigned
operating models for AI-human collaboration
are largely neglected.
The risk is losing the engagement and trust that
AI transformation requires.
Leaders should be skilled in guiding this change, assisting employees in viewing AI as a tool for empowerment rather than a threat.
Employee, UK
“
59% of organizations don’t have
long-term HR plans to support
the future of work
42% of employees say they aren’t getting enough AI training
21% of organizations have assessed
AI skills across the enterprise
ONLY
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2020
Build for people, not systems
Bhavin Shah
Senior Vice President and
General Manager of Moveworks and AI
ServiceNow
Most organizations have not failed at AI because they lacked
ambition or the budget. They failed because they bolted AI
onto broken, fragmented infrastructure and then expected
employees to figure it out.
That’s backwards.
The friction your employees face every day—navigating
multiple portals, hunting for answers in data silos, and learning
five different interfaces—isn’t just a tech hurdle. It’s a design
failure. When people have to work for the system rather
than the system working for them, they don’t use the AI.
They work around it. And the moment they work around it,
ROI plummets.
The leaders winning right now share one obsession: They
make AI intuitive and almost invisible. It shows up where
employees already work. It speaks natural language, and it
doesn’t give more soul-crushing homework; it finishes the job.
“Invisible AI” sounds like a paradox, but it’s the highest
standard. When people stop thinking about the tool and start
focusing on the outcomes, adoption takes care of itself.
You can choose to add more noise, or you can choose to
build a foundation where AI and people thrive.
Every leader can make that choice right now. Go be one
of them and equip your employees for success.
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Contents Reinvention starts here
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Appendix Automating yesterday’s work
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AI is being applied to existing workflows, not better ones Most organizations are using AI to do the same work slightly faster. The more transformative uses of agentic AI—creating autonomous
multistep workflows or entirely new workflows that were previously impossible—remain out of reach for the vast majority.
Working across business functions, a key component of enterprisewide AI, is even harder this year than it was last year. In 2025, 30% of organizations had streamlined and integrated
workflows across business functions with AI. In 2026, that dropped 14 points to 16%. This is happening because organizations still have fragmented platforms with a new wave of agent sprawl sitting on
top of them.
The two-year outlook doesn’t close the gap
Even looking two years ahead, ambitions remain modest. Only 20% of organizations expect to be using agentic AI to create autonomous multistep workflows. As AI capabilities accelerate, organizations still
running on outdated workflows will find themselves falling behind on productivity and unable to deliver the new products and services AI makes possible.
While ambitions remain modest, orgs plan to double progress
Two yearsNow
22%
9%
Automate and optimize workflows with AI
Streamline and integrate workflows across business functions with AI
Use agentic AI to create autonomous multistep workflows
Use agentic AI to create completely new workflows that were not possible before
20%
9%
37%
16%
42%
27%
Efforts to modernize workflows
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Without control, chaos compounds
Vijay Kotu
Chief Analytics Officer
ServiceNow
The pattern I keep seeing across industries isn’t a technology
gap; it’s a deployment gap. Organizations are moving agents
into production faster than they’re building the capabilities to
manage agents, both individually and collectively.
The core risk isn’t an individual agent failing. It’s that
connected agents amplify each other’s errors as readily
as they amplify each other’s value. Complexity compounds
faster than most organizations anticipate, and by the time it
becomes visible, the cost to correct is significant.
This isn’t new territory. Organizations that invested early in
cloud and API governance paid a one-time architectural cost.
Those that didn’t, pay repeatedly—at increasing scale, and
always at worst possible moment. End-to-end orchestration
is that same inflection point. Most enterprises are
underestimating how early they need to make the call.
What end-to-end orchestration requires is a unified layer
that routes, governs, and audits agent decisions across
functions, not just within them. Siloed orchestration is still
fragmented execution.
Human judgment in this model isn’t a bottleneck. It’s the
architecture itself. Leaders define the boundaries within which
agents operate and the outcomes they’re accountable for.
That distinction shapes everything about how the system
gets designed.
Organizations that move first on orchestration infrastructure
aren’t just managing risk better. They’re building the
foundation that turns autonomous AI from an operational
liability into a durable competitive advantage, one that
compounds over time.
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Every year, a group pulls ahead in the study. We call them Pacesetters
Pacesetters represent approximately 21% of organizations surveyed, united by a maturity
score above 60 and an average score of 74, well above the average for others of 45 and
global average of 51. What defines them is not budget or industry. It is a fundamental
decision about how AI fits into the enterprise. While most organizations deploy AI into
existing structures, Pacesetters build their organizations around AI. The difference
between those two approaches is compounding with every AI deployment that follows.
End-to-end orchestration drives business reinvention
Pacesetters treat orchestration as the foundation, not the goal. They connect data, workflows, and governance across the entire enterprise so that AI doesn’t just assist
people; it completes work end to end. That structural choice is what separates an organization with a lot of AI tools from one that is genuinely AI first. And for
Pacesetters, that structure includes their people. The organizations pulling ahead are redesigning how humans and AI work together, not just how
technology is deployed.
Average maturity score
45
74 PACESETTERS
OTHERS
Pacesetters aren’t just AI enabled. They’re AI first
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160% AVERAGE ROI TODAY
What Pacesetter status delivers Exponential impact compared to others
Pacesetters achieve higher ROI
AVERAGE ROI IN TWO YEARS
194% More likely to use AI to improve or create
new products, services, and revenue channels
6.5x
Higher productivity
5.6x
Greater ability to scale
2.7x
Better at reducing risks
2.6x
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Five strategies separating Pacesetters from the rest The gap between where most organizations are and where Pacesetters
operate is wide, but it is closable. The research reveals key strategies
that define how leading organizations are pulling ahead.
1 Set the vision. Bring everyone with you
2 Control your data. Unleash your AI
3 Move beyond automation. Start orchestrating
4 Build the workforce your AI strategy demands
5 Accelerate governance. Close the chaos gap
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1 Communicate AI vision widely across the organization
71% of Pacesetters vs. 29% of others
2 Create an implementation plan with timelines and defined
metrics for AI transformation
70% of Pacesetters vs. 17% of others
3 Build an AI mindset that reimagines how work is done
72% of Pacesetters vs. 34% of others
4 Define AI responsibilities across C-suite and staff
58% of Pacesetters vs. 12% of others
Pacesetters don’t hand AI strategy down from the top. They spread it across the entire organization.
Vision is communicated widely, backed by implementation plans and tied to defined outcomes.
Strategy, for Pacesetters, is not a document. It is a shared direction.
But strategy alone is not enough. Culture is what makes it stick. Pacesetters build AI mindsets before
they build deployments. They define AI responsibilities at every level, encourage experimentation,
and create the conditions for people to reimagine how work gets done. They understand that AI
transformation doesn’t happen to an organization. It happens inside one.
Set the vision. Bring everyone with you
Pacesetters commit at every level
LESSON 1
of Pacesetters set a shared strategic vision for
AI beyond efficiency gains, compared to just
21% of others
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1 Set up policies for data ownership and control
71% of Pacesetters vs. 22% of others
2 Implement tools to collect and track data in real time
67% of Pacesetters vs. 12% of others
3 Use AI to improve data migration, creation,
and management
66% of Pacesetters vs. 14% of others
4 Create processes to clean, tag, and standardize data
64% of Pacesetters vs. 13% of others
Control your data. Unleash your AI
LESSON 2
Data and AI are only as powerful as connectivity
The most sophisticated AI agents still can’t reason, act, or reliably execute across an enterprise if the
data supporting them is fragmented, siloed, or out of date. Clean, connected, real-time data isn’t a
technical nice-to-have. It is what makes AI useful at scale.
Pacesetters treat data modernization as a strategic prerequisite, not an IT project. They replace legacy
systems with integrated platforms, establish clear policies for data ownership and control, and use AI to
improve how data is migrated, created, and managed. The result is an enterprise where AI doesn’t just
have access to data. It has the right data, in context, at the moment it needs to act.
For Pacesetters, every interaction, every workflow, every data point adds enterprise context that makes
their AI more precise and the organization’s decisions more reliable over time.
Pacesetters have AI-ready data
of Pacesetters use digital technologies to
integrate and optimize data, compared to 14%
of others
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Embedding AI into core workflows resulted in performance gains, rather than introducing standalone tools.
CIO, Pacesetter insurance company, France
“
Move beyond automation. Start orchestrating
LESSON 3
Pacesetters close the workflow gap
Most organizations automate the way they built their technology stacks: one problem at a time, one
tool at a time, one department at a time. Individual tasks get faster. The enterprise stays fragmented.
Pacesetters reject that model. They embed AI across departments, connect it across functions, and
keep going until routine work runs itself—not as a set of disconnected tools, but as a governed layer
that coordinates AI agents, data, and decisions across the entire enterprise. That is not automation.
That is orchestration.
The result is an enterprise where AI and people operate together at scale. Routine coordination,
routing, and execution are handled by AI. Human judgment, creativity, and decision-making move
to the center.
1 Implement AI initiatives across a wide range of
departmental workflows
61% of Pacesetters vs. 5% of others
2 Streamline and integrate workflows across business
functions with AI
58% of Pacesetters vs. 5% of others
3 Use agentic AI to create autonomous multistep workflows
36% of Pacesetters vs. 2% of others
4 Use agentic AI to create completely new workflows
not possible before
36% of Pacesetters vs. 1% of others
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Build the workforce your AI strategy demands
LESSON 4
of Pacesetters launch change management
programs, compared to 7% of others
Business reinvention doesn’t happen without people. Pacesetters know this, which is why they build
deliberate strategies to attract, develop, and retain AI talent—not as a one-time initiative, but as an
ongoing organizational discipline. They think long term, building HR plans that target the roles and skills
needed to become AI-first organizations.
What separates them most is how seriously they take the human side of AI. They establish dedicated
teams to experiment with new ideas, launch change management programs to eliminate outdated ways
of working, and build new structures for managing AI agents and humans together. Tailored training,
external partnerships, and systematic skills assessments ensure their people are ready for what’s next.
Pacesetters build organizations where people and AI are ready to work together at scale—and the
results show it: Pacesetters are 2.6 times better at employee engagement and retention than others.
Pacesetters invest in their people
1 Develop strategies to attract, hire, and retain AI talent
68% of Pacesetters vs. 10% of others
2 Build long-term HR plans to support AI strategy
58% of Pacesetters vs. 5% of others
3 Provide tailored, ongoing AI training and
upskilling programs
57% of Pacesetters vs. 4% of others
4 Assess AI skills across the organization
54% of Pacesetters vs. 12% of others
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Accelerate governance. Close the chaos gap
LESSON 5
Pacesetters treat AI governance as a competitive advantage, not a compliance burden. Instead of a
one-time policy document, they build living processes: continuous regulatory scanning, ongoing risk
assessment, and systems to track compliance and accountability across their enterprise.
As agents assume greater decision-making, Pacesetters establish cross-functional oversight that defines
standards and intervenes when risks emerge. Every autonomous decision is grounded in enterprise rules,
auditable from end to end, and aligned with organizational policy.
The result is an organization that moves faster because the foundations are right. Governance is what lets
Pacesetters accelerate without losing control. Autonomous doesn’t mean unattended. The organizations
that move fastest on AI are the ones that have defined, in advance, exactly where human judgment begins.
of Pacesetters say organizations need a security-
first approach when developing AI solutions
Organizations that embed security from the start build the
audit trails that let them move fast without losing control.
Pacesetters prioritize governance
1 Embed trust and transparency into AI processes
69% of Pacesetters vs. 16% of others
2 Communicate regularly with regulators to stay informed
on evolving AI standards
64% of Pacesetters vs. 13% of others
3 Install a system to manage and track governance
and compliance initiatives
61% of Pacesetters vs. 17% of others
4 Implement AI testing, auditing, and risk assessment
processes
61% of Pacesetters vs. 10% of others
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The leadership path to business reinvention
Brian Solis
Head of Global Innovation
ServiceNow
For decades, organizations have been built around one assumption: that getting the right person to act on the right problem was the hardest thing to do. The org chart, the approval process, the meeting, the escalation path—all were built to allocate scarce human judgment to the decisions that needed it the most.
AI dissolves that constraint by democratizing levels of intelligence. And most organizations have not yet reckoned with what that means for how they are built.
Executives are already living with the early consequences. They are individually collaborating with AI models. Some have deployed agents. Many have automated workflows. Some have begun redesigning how work flows. But even AI Pacesetters have largely treated AI as something deployed into an existing organizational structure and management system.
The next phase demands something more challenging. It requires redesigning the structure, reimagining work, and rethinking the value model.
When agents become an integral part of the workforce—not as a tool,
but as a participant—the questions change. Where can an agent add the greatest value when you break roles into tasks and separate creative and critical knowledge work from knowledge work? How do you onboard an agent? How do you measure its performance? Who is accountable when it acts outside the boundaries you set?
How do you build a team of humans and agents that improve together over time? How do humans and agents unlock net new value?
These are not technology questions. They are organizational design questions that no enterprise has fully answered. The leaders who move earliest to answer them will shape what the next-generation, AI-first organization looks like. And this creates an exponential advantage. In a world where everyone else is automating and scaling yesterday’s models, an AI-powered status quo is permeating.
Here is the call to action I want to leave you with: Every leader reading this report has an org chart. Very few have a work chart, a living map of how outcomes flow and could flow through the enterprise, showing where humans add irreplaceable imagination and judgment and where agents can own execution, securely, end to end. You need that map. You need that vision. The future of work isn’t about yesterday’s work. It’s about imagining a new model for improving outcomes and inventing new ways to deliver value.
Until you create one, you are managing AI as a capability. The leaders who pull furthest ahead will manage it as an augmented workforce.
That is the work still ahead. And it is the most important organizational design challenge of the next decade.
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AI maturity is a journey that is not strictly linear. Organizations move forward as
their foundations strengthen and sometimes step back as technology, regulation,
and business priorities shift. What matters is knowing where you are, because
your next move depends on it.
Build your roadmap. Accelerate AI maturity
Experimenter Testing AI approaches and running
pilots in individual departments.
Building the strategy and plans
needed to scale.
21% Surveyed
57% Surveyed
Scaler Moving from plans to implementation.
Building IT foundations and cross-
functional collaboration to deploy
AI across the organization. Most are
still working through siloed systems
and fragmented workflows.
Advancer Strong foundations in place. Scaling
AI enterprisewide and beginning to
optimize with agentic AI, with full
business reinvention still ahead.
PACESETTER
17% Surveyed
PACESETTER
4% Surveyed
Transformer Reinventing business models, products,
and services through widescale AI
execution. AI is central to strategy,
operations, and value creation.
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With Experimenters, the key words are “thinking about it.”
These organizations are still developing their plans for AI.
Most are just starting to automate and optimize workflows
with AI—but not yet with agentic. Very few have replaced
fragmented legacy systems, let alone developed
business cases for AI initiatives.
Key challenges
• Choosing the right vendors for their individual needs
• Integration with their legacy systems
• Handling AI risks and ethical concerns
$7.3B Average size
125% Average AI ROI
Typical industries
• Nonprofit
• Government
• Healthcare provider
Roadmap from Experimenter Scaler
Leadership
Define selection criteria for AI pilots before choosing
them. Prioritize projects with measurable outcomes and
assign executive ownership to each.
Culture
Build a change management program and an AI partner
ecosystem. Treat every pilot as a learning asset: document
what works, what doesn’t, and why.
Governance
Establish initial policies for data privacy, ethical AI use,
and risk management before pilots expand. Governance
built at the start is an accelerator. Governance installed
later is a brake.
Data modernization
Develop data governance policies covering ownership,
quality, and access. Clean, tagged data is the prerequisite
for everything that follows.
Workflows
Identify the fragmented, manual processes consuming
the most time and resources. Develop a phased plan to
automate key workflows and start building the business
case to retire legacy systems. Task-level automation is the
entry point, not the endpoint.
Talent and skills
Identify internal AI champions and pair them with external
expertise through universities, industry associations,
and technology partners. Start building the talent pipeline
before the skills gap becomes a chasm.
Generating value
Define success metrics before each pilot launches, not
after. Use early proof points to build the internal
business case for broader AI investment.
Experimenter
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$9.1B Average size
145% Average AI ROI
Standout region
EMEA
Leadership
Formalize an AI strategy with defined objectives,
priorities, and accountability structures.
Culture
Establish a cross-functional AI center of excellence to
guide and align AI efforts across the organization. Use your
change management program to shift the culture from
adopting AI to building with it.
Governance
Build a governance team with defined roles, policies, and
oversight procedures. Introduce formal audit practices
and embed transparency into autonomous workflows.
Data modernization
Move beyond data collection to data orchestration.
Standardize processes for cleaning, tagging, and
integrating diverse data formats in real time. Reliable data
infrastructure is what separates scalable programs from
isolated pilots.
Workflows
Prioritize retiring legacy systems that prevent integration
across functions. Build automated workflows with
repeatable processes that can be replicated at scale.
Fragmentation that felt manageable in earlier stages
becomes a strategic liability if not addressed.
Talent and skills
Launch tailored, ongoing AI training and begin building
long-term workforce plans that support AI-human
collaboration. Define the roles your organization will
need as AI matures and start recruiting for them now.
Generating value
Build a portfolio of AI use cases with measurable KPIs.
Replicate what is working across functions, and drive
greater value by delivering consistent results at scale.
Roadmap from Scaler Advancer
Scaler Scalers put their AI vision into action, adapting and updating
their organizations to embrace AI with a value-driven
approach. They have taken steps to address data,
governance, and skills gaps. Yet most still have siloed
legacy systems and traditional workflows. The most
important moves at this stage are foundational: data
modernization and governance. Organizations that resolve
these two tracks to set themselves up for greater success.
Key challenges
• Unclear AI ROI
• Siloed data and traditional workflows
• Insufficient human oversight on AI
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$13.1B Average size
159% Average AI ROI
Leadership
Develop an AI value framework that connects every major AI
initiative to a specific business growth objective. Leaders must
move the conversation from efficiency gains to competitive
differentiation. Cost reduction is a byproduct, not the goal.
Culture
Build cross-functional teams that actively reimagine
how work gets done, not just how it gets optimized.
Innovation at this stage means redesigning processes
from first principles, not improving the ones inherited
from the pre-AI era. Governance
Evolve governance from project oversight to enterprise-wide
accountability for AI outcomes. Establish KPIs that measure
strategic business impact, not just operational efficiency.
Governance should enable ambition, not constrain it.
Data modernization
Assess data requirements for complex, multi-step
workflows and update ownership and privacy policies
accordingly. At this stage, data architecture decisions
directly determine which autonomous capabilities
your organization can deploy.
Workflows
Use agentic AI to orchestrate workflows across
functions, not just within them. The shift from
departmental automation to enterprise-wide
orchestration is the defining transition of this stage.
Talent and skills
Create targeted strategies to attract, develop, and retain
AI talent. The skills gap at this stage is specific: Advancers
need people who can design orchestrated workflows,
interpret AI-driven decisions, and manage AI-human
collaboration at scale.
Generating value
Prioritize AI use cases that reshape business models
and open new markets, not just those that reduce cost or
improve speed.
Advancers have succeeded in developing a strong
foundation for AI transformation. Modernized data systems
are largely in place along with needed governance and clear
AI vision. While in the process of optimizing workflows with
agentic AI, these organizations still have some ways to go for
complete business reinvention.
Key challenges
• Potential for job loss
• Employee resistance
• AI skills
Typical industries
• Technology
• Telecommunications
• Life sciences
• Manufacturing
• Banking
Roadmap from Advancer Transformer
PACESETTER
Advancer
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$10.3B Average size
210% Average AI ROI
Leadership
Use AI to surface emerging opportunities before the
market signals them. The question is no longer how to
apply AI to the business. It is how AI reshapes what the
business can become.
Culture
Embed regular cycles of AI capability review into how
the organization operates. Organizations that hold this
position don’t wait for new capabilities to find them.
They are piloting what comes next before competitive
pressure makes it urgent.
Governance
Your responsible AI frameworks and ethical AI policies
should now be sophisticated enough that others benchmark
against them. Governance at this stage is not a constraint on
innovation. It is what makes bold innovation sustainable.
Data modernization
Move from managing data to using it predictively:
surfacing signals and positioning the organization to act
on opportunities before they become visible to others.
Workflows
Evaluate autonomous workflows not just for efficiency
but for strategic reach. The benchmark is no longer
whether a workflow is automated but whether it is
self-improving and generating intelligence that feeds
better decisions.
Talent and skills
Build continuous learning systems that evolve alongside AI
capabilities. Prioritize developing the human judgment and
oversight capabilities that make autonomous AI trustworthy
and effective at scale.
Generating value
The value question is no longer how much ROI AI is
generating. It is what new markets and competitive positions
AI is making possible that didn’t exist before.
Transformers are rare. They represent a small share of
organizations globally, but they are setting the terms for what
AI-first means in practice. They have moved decisively past
efficiency and automation into new territory: fostering growth
through new markets, customer segments, and business
models, while driving a step change in competitive position
and shareholder value. Transformers use AI not just to assist
people, but to orchestrate work across the enterprise. The
results reflect that ambition.
Key achievements
• 88% increased customer engagement and retention
• 87% accelerated time to value
• 84% reduced risks
• 70% improved staff engagement and retention
Typical industries
• Life sciences
• Insurance
• Banking
• Retail
Transformer sustaining priorities
PACESETTER
Transformer
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Appendix
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Authors and contributors
Contributors
Authors
René Stranghoner
Head of Thought
Leadership Research
ServiceNow
Acknowledgements
A special thanks to Jessica Buckley, head of brand content; Sheila
Dowd, director of thought leadership editorial; Marissa Amendolia
Finn, director of brand content experience; and Emma Kieckhafer,
director of social media.
Thank you to all the ServiceNow colleagues who helped bring this
research to life, especially Tim Catts, Ryan Breen, Laura LeBleu,
Abi Hobson, Angela Chen, Ina Chu, Sheryl Domingue, Rich Cohen,
Zoe Gaylard, Mary Silverstein, and Jingsi Chen.
Research and economic analysis were
conducted independently by ThoughtLab, a
global research and thought leadership firm.
Lou Celi
CEO, ThoughtLab
Daniel Miles, Ph.D.
Chief Economist, ThoughtLab
Janet Lewis
Senior Editor, ThoughtLab
Brian Solis
Head of Global
Innovation
ServiceNow
Evan Volness
Research Analyst,
Thought Leadership Research
ServiceNow
Holly Briedis
SVP, Global Industries
and Solutions
ServiceNow
Sarah Struble
AI Index Strategy
and Research Lead
ServiceNow
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Regions and countries
Survey demographics
Americas
Brazil
Canada
Mexico
U.S.
825
75
150
75
525
1,400
200
200
150
850
APAC
Australia
India
Japan
Singapore
South Korea
530
85
250
100
60
35
1,400
350
350
300
200
200
EMEA
Belgium
France
Germany
Italy
Netherlands
Saudi Arabia
Spain
Switzerland
UAE
UK
625
35
85
85
70
35
15
85
35
15
185
1,700
100
250
250
150
150
150
150
150
100
250
Automotive
Banking
Consumer goods
Government
Healthcare provider
Heavy manufacturing
Insurance
Life sciences
Nonprofit
Retail
Technology
Telecommunications
74
318
100
118
152
86
107
166
202
199
317
161
325
450
325
425
400
200
400
400
350
400
425
400
Employees (2,000)Executives (4,500)Respondents
Industry
Role
Frontline manager 33%
Middle manager 44%
Individual contributor 23%
SVP/VP/Sr. Dir./Dir. 34%
C-suite 66%
Less than $1B
37% 43%
12% 8%
$1B to $9.9B
$10B to $24.9B
Over $25B
Revenue
Age
35%33% 30%
3%
Generation Z Millennial/Generation Y Generation X Baby boomers and beyond
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Determining overall index score
All pillars were weighted equally. The overall score was calculated by averaging the
pillar scores, dividing by 5, and multiplying by 100 to produce a 0 to 100 scale.
Respondents were assigned to one of five categories based on overall score:
Evaluator: 1 to 19
Experimenter: 20 to 39
Scaler: 40 to 59
Advancer: 60 to 79
Transformer: 80 to 100
Less than 1% were Evaluators, so it was not included in the AI roadmap section.
AI index methodology Updated AI Maturity framework
For 2026, we expanded the framework from five to seven pillars and refocused existing pillars to reflect
new strategies and solutions.
1 AI vision, strategy, and leadership: Expanded to include setting a shared vision for AI transformation
beyond efficiency gains and selecting AI projects based on ability to drive strategic outcomes.
2 Management and culture: New pillar measuring progress in establishing AI leadership teams, partner
ecosystems, innovation centers, and enterprise-wide culture.
3 AI governance: Refocused to cover responsible AI use, oversight teams, regulatory compliance,
and embedding trust and transparency into AI processes.
4 Data modernization: New pillar gauging progress on data quality, security, privacy, and compliance;
data integration and standardization; and real-time data capture.
5 AI-enabled workflows: Refreshed to include agentic AI creating autonomous, multistep workflows
and entirely new workflows not possible before.
6 Talent and skills: Updated to address AI’s impact on the future of work, including long-term HR
planning and managing AI agents and humans together.
7 Driving value from AI: Refocused from investment to value creation: new products, services,
and business models, with sharper focus on performance metrics.
Calculating pillar scores
Each of the pillars was supported by a question asking respondents to gauge
progress across sub-pillars, scored as follows:
0 = No progress
1 = Evaluating and building support
2 = Testing and developing plans
3 = Finalizing plans and starting implementation
4 = Scaling widely across the organization
5 = Fully implemented with significant results
Each pillar score was calculated by averaging its sub-pillar scores (range: 0 to 5).
Identifying Transformers
Respondents scoring 80 or higher were classified as Transformers only if they
agreed with two or more statements about how their organization has driven
strategic transformation through AI. Those scoring 80 or higher but agreeing with
fewer than two statements were classified as Advancers.
PACESETTERS
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Enterprise AI Maturity Index 2026 Table of contents Everyone bought AI. Few built for it Is the world getting better at AI? The gap betweenAI ambition and execution
Underneath the confidence, chaos is growing AI maturity rebounds. Execution at scale lags AI spend surges. So does the pressure to deliver As AI scales, cracks become chasms Global barriers. Local pressure points Intention over speed
Automating yesterday’s work vs. reinventing tomorrow’s Agentic AI is everywhere. Autonomous work is not Most are paying for a capability they haven’t yet unlocked Where agentic AI is already delivering AI can’t reinventwork it can’t connect Employees are willing. Organizations are not prepared Build for people, not systems While ambitions remain modest, orgs plan to double progress
Reinvention starts here Without control, chaos compounds Pacesetters aren’t just AI enabled. They’re AI first What Pacesetter status delivers Five strategies separating Pacesetters from the rest LESSON 1 Set the vision. Bring everyone with you LESSON 2 Control your data. Unleash your AI LESSON 3 Move beyond automation. Start orchestrating LESSON 4 Build the workforce your AI strategy demands LESSON 5 Accelerate governance. Close the chaos gap
Your AI roadmap The leadership path to business reinvention Build your roadmap. Accelerate AI maturity Experimenter Scaler PACESETTER Advancer PACESETTER Transformer
Appendix Authors and contributors Survey demographics AI index methodology