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The State of Enterprise AI Adoption UK Edition

Research report of 120+ business and IT leaders

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Table of Contents

  1. Foreword
  2. Executive Summary
  3. 01. AI has moved from experimentation to enterprise-scale execution
  4. 02. Scaling AI means rethinking how people and agents will work together
  5. 03. Deeper AI adoption is linked to stronger revenue outcomes
  6. 04. What enterprise AI Adoption Leaders do differently
  7. 05. How organisations are approaching AI governance
  8. 06. The challenges slowing AI adoption
  9. 07. How organisations measure AI value and ROI
  10. Conclusion: The next phase of enterprise AI adoption
  11. About the Survey
  12. About Creatio
Foreword

Competitive advantage will depend on how effectively people and AI agents work together

AI is here and now, and it has real traction in business initiatives across the UK. Our survey of 128 IT and business decision-makers from the UK shows that nearly all organisations have strong AI ambitions, and more than eight in ten have deployed, are scaling, or have AI embedded across the organisation.

As businesses in the UK move further along their AI adoption journeys, achieving the right mix of people, workflows, applications, and AI agents will be increasingly important. Getting this combination right will bring AI technology decisions to the forefront of competitive and go-to-market strategies.

On behalf of everyone at Creatio, I hope the insights in this report help your organisation to adopt AI in a transformational way and propel your operations into the future with unlimited potential for growth.

John Arnold
John Arnold
Head of Product Marketing and Strategic Advisory, Creatio
78%
of organisations are scaling AI agents within twelve months
Don’t wait for the future of AI

If your AI strategy is still focused on pilots, you risk falling behind the market. It's time to move beyond experimentation and develop a plan to scale AI agents across the organisation.

Executive Summary

Seven findings shaping the next phase of enterprise AI

Enterprise AI adoption is becoming an operating model decision.

The opportunity is to embed AI in the workflows that generate revenue, serve customers, and coordinate work across the business. Doing this well requires executives to decide how people and agents share responsibilities, how decisions are governed, and how success is measured.

Creatio’s survey of 128 IT and business decision-makers from the UK shows that AI is already delivering business benefits. It also reveals a gap between organisations using AI and those adopting it broadly enough to qualify as AI Adoption Leaders. Leaders represent 23% of this sample. Their experience offers a useful benchmark for the other 77%.

1. AI moves beyond experimentation – 83% are already putting it to work

More than eight in ten organisations in the UK are already beyond AI experimentation. 28% have deployed AI in selected functions, while another 55% are scaling it across the organisation or have embedded it enterprise-wide.

The next wave is already forming: more than three quarters expect to scale AI agents across departments or use cases within twelve months.

The takeaway

AI is moving from isolated pilots into the operating fabric of the enterprise. The management challenge is no longer whether to adopt AI, but how to coordinate and scale it across the business.

83% are past experimentation
  • Still evaluating 9%
  • Experimenting 9%
  • Deployed in selected functions 28%
  • Scaling across the organisation 41%
  • Embedded across the organisation 14%

2. AI is redefining work, not simply replacing people

Only 13% of organisations in the UK expect significant headcount reduction from AI. 86% of respondents expect AI agents to expand workforce capacity, augment existing teams, or create new roles.

Specifically:

  • 48% expect greater capacity and growth for current staff.
  • 27% expect augmentation without major headcount change.
  • 12% expect new roles.
  • 13% expect job cuts.
  • 2% are unsure.

UK organisations are nearly four times as likely to expect AI agents to create capacity and growth for current staff as to significantly reduce headcount, 48% versus 13%. Almost as many expect job cuts (13%) as expect new roles (12%).

The takeaway

Organisations in the UK need to plan how people and AI agents will work together: which tasks agents handle, where human judgement matters, and who owns the outcomes.

86%
expect AI agents to augment people and create new roles
13%
expect headcount reduction
  • 48% Create capacity and growth for current staff
  • 27% Augment teams without major headcount change
  • 12% Create new roles
  • 13% Reduce headcount
  • 2% Unsure

Note: May not total 100% due to rounding.

3. Companies scaling AI are 3x more likely to report revenue growth from AI

When asked which positive outcomes they had experienced from AI in the past six months, organisations in the UK with deeper adoption were more likely to select revenue gains.

Among organisations automating more than half of their workflows with AI, 38% report increased revenue from AI in the past six months, compared with 13% among those automating less than 10%.

This suggests that as AI becomes more deeply embedded in workflows, revenue gains are more commonly reported among its benefits.

The takeaway

This places enterprise AI adoption firmly on the growth agenda. For executives in the UK, the implication is to look beyond productivity and efficiency alone and measure AI's contribution to revenue as adoption expands.

Reported revenue gains
13%
Under 10% workflows with AI (small base, directional)
38%
Over 50% workflows with AI

4. What AI Adoption Leaders do differently: scaling AI enterprise-wide and measuring impact beyond efficiency

Just under a quarter (23%) of surveyed UK organisations qualify as AI Adoption Leaders, based on the breadth of their AI capabilities and agent adoption across business functions.

The takeaway

Enterprise AI leadership requires adoption depth, broader measures of business value, and the governance to support scale.

  • Automate more workflows with AI: 45% automate over half their workflows with AI, versus 16%.

  • Scale agents across departments: 45% of Leaders, versus 27% of others.

  • Embed AI across the business: 34% of Leaders report organisation-wide integration, versus 8% of others.

  • Measure success more broadly: Every UK Leader (100%) uses three or more measures of AI success, versus 81% of others, with greater emphasis on revenue, employee satisfaction, and innovation, not just time savings.

  • Establish stronger governance: 45% of Leaders have fully established AI governance, versus 27% of others.

5. Companies with established governance are nearly 5x more likely to feel ready to scale AI

AI adoption is accelerating, but enterprise governance has yet to catch up. 78% of organisations in the UK are already scaling AI agents across departments or use cases, or expect to do so within a year. Yet only 31% report fully established AI governance.

The data reveal why closing this gap matters. Organisations with established governance are nearly five times as likely to feel very confident about scaling AI (83% vs 17% among those still developing a framework). AI Adoption Leaders are also ahead: 45% have fully established governance, compared with 27% of other organisations.

The takeaway

Build governance alongside AI deployment. Clear decision rights, permissions, human oversight, and visibility into agent actions provide a foundation for confident expansion.

Very confident about scaling AI
83%
Established governance
17%
Developing governance framework

6. The barriers to scaling AI vary by adoption stage

The challenges that slow broader AI adoption change as organisations move from early experimentation to enterprise-wide adoption. Among organisations from the UK at the earliest stage of AI adoption, a lack of AI skills is the leading challenge (55%), alongside data quality and integration (49%) and security and compliance (44%).

As adoption matures, skills become far less prominent, declining to 21% among the most advanced adopters. At the same time, enterprise-scale requirements move to the forefront: security and compliance rises to 55%, while commercial models and budgets (52%), change management (38%), and technology capabilities (34%) become increasingly important.

The takeaway

Scaling AI doesn’t eliminate barriers – it changes them. As organisations mature, the challenge shifts from building the skills and readiness to adopt AI towards establishing the technology, security, governance, and operating model required to run it at enterprise scale.

Barriers shift with AI maturity
55% 21%
AI skills
44% 55%
Security
24% 52%
Commercial models and budgets

7. The AI ROI conversation is moving beyond time and cost savings

Organisations in the UK still lead with efficiency (56%) and productivity (42%) when pursuing AI agents, but the value case broadens as adoption matures. Leaders put more emphasis on customer experience, revenue, and innovation – not just cost and time savings.

The measurement model has not fully caught up. Only 56% of organisations pursuing better customer experience measure customer satisfaction, and just 35% of those building for the future measure innovation outcomes.

The takeaway

The next phase of AI ROI is about connecting every strategic reason for adoption to a business outcome that can actually be measured.

What executives should prioritise next

Creatio believes enterprise AI adoption should be treated as an ongoing business capability. Organisations should prioritise workflows that deliver measurable business or customer value and define how people and AI agents will work together to execute them.

Governance and performance measurement should be built into these workflows from the start, allowing organisations to scale AI based on proven results. This approach helps executives make AI adoption a consistent and repeatable part of business operations.

AI has moved from experimentation to enterprise-scale execution

In just one year, enterprise AI has moved from boardroom discussions to production with 83% of organisations now past AI experimentation.

Enterprise AI has moved from a long-term strategic priority to an immediate focus for execution. In 2025, 94% of business and IT leaders from the UK expected AI agents to be critical or important to their organisations’ goals within two to three years. Just one year later, 83% of surveyed organisations have moved beyond AI experimentation.

The next stage is already underway. More than three quarters of organisations say they are scaling agents across departments within twelve months.

This chapter examines how UK organisations are progressing beyond pilots, expanding adoption across functions, and preparing to make AI part of how the enterprise operates.

Most organisations are running AI, not testing it

83% are past experimentation
9% Still evaluating
9% Experimenting
28% Deployed in selected functions
41% Scaling across the organisation
14% Embedded across the organisation

Most surveyed organisations have deployed AI, though adoption ranges from selected functions to integration across the enterprise.

Base: 128 IT and business decision-makers from the UK. No UK respondent reported having no plans to adopt AI.

Enterprise AI has moved past the experimentation stage

More than eight in ten surveyed organisations from the UK have moved beyond AI experimentation. 28% have deployed AI in selected functions, while 55% are already scaling it across the organisation or have embedded it throughout the business.

With no respondents indicating that their organisations have no plans to adopt AI, the findings reflect a broad commitment to AI adoption across the UK market. For the majority already beyond experimentation, the priority is expanding deployment and integrating AI more deeply into business operations.

As adoption grows, enterprise leaders need to coordinate implementation across functions, define how people and AI agents work together, and measure business outcomes. These decisions will shape how effectively organisations translate deployment into enterprise-scale execution.

Expert’s Perspective

AI is moving faster than human behavior can adapt. While 78% of leaders plan to scale AI agents within a year, technology adoption is only half the equation. The real competitive advantage won’t come from deploying agents faster, but from reimagining how humans and AI evolve together.

Dr. Ja-Naé Duane
Dr. Ja-Naé Duane
Futurist, Best-Selling Author and AI Researcher, Brown University School of Engineering and MIT CISR Academic Fellow

Most organisations will be scaling AI agents within a year

A year ago, 94% of IT and business leaders from the UK saw AI agents as critical or important to goals two to three years out. The 2026 findings suggest that this shift is happening roughly two years sooner than previously expected.

Nearly a third of organisations (31%) are already scaling AI agents across departments, 18% plan to within six months, and 29% within a year. Together, 78% expect to be scaling agents across the enterprise within twelve months. Only 5% have no timeline.

What it means

For organisations in the UK, scaling AI agents is no longer a distant ambition but an immediate business priority. Leaders need a clear strategy to integrate AI agents across departments, connect them to business workflows, and enable people and AI to work together effectively. Those who act now will be better positioned to realise AI's benefits and stay competitive.

When organisations expect to scale AI agents across departments
78%
31% Already scaling
18% Within six months
29% Within a year
16% One to two years
5% No timeline
2025
94%
of business and IT leaders said agents will be critical or important in two-to-three-years.
2026
78%
of all decision-makers say they are scaling agents across departments within twelve months.

Percentages may not total 100% due to rounding.

Base: 128 IT and business decision-makers from the UK (2026). 2025: Creatio Global AI and No-Code Adoption Survey: UK Edition.

AI adoption is advancing at the same pace across IT and the front office

79%
Exploring or deploying AI
IT and technical
IT operations, software development, R&D
Unified AI strategy and execution required

Disconnected AI strategies across IT and the front office can create significant challenges. Align both teams around a unified strategy and platform to ensure AI agents share data, operate under consistent governance, and contribute to common business goals.

78%
Exploring or deploying AI
Front office
Sales, marketing, service, customer success

Base: 128 IT and business decision-makers from the UK. Share of organisations exploring or deploying AI agents in each group.

AI agents are in IT and the front office at nearly the same rate

In 2026, 79% of UK organisations have AI agents in IT and technical functions (IT operations, software development, R&D) and 78% in the front office (sales, marketing, service, customer success).

The average UK organisation has agents in more than four of nine business functions (4.3), and AI Adoption Leaders in 6.9. The UK runs slightly ahead of the full sample in IT (79% vs 74%) and level in the front office (78% vs 79%).

What it means

IT and the front office are adopting AI agents at nearly the same pace, and in most organisations they are doing it side by side. IT and front office leaders need one aligned AI strategy, and technology that brings their AI initiatives and activities onto a shared platform.

AI agent adoption by function group
IT operations, software development, and R&D and innovation
79%
UK
74%
All organisations
Sales, marketing, service, and customer success
78%
UK
79%
All organisations

Base: 128 IT and business decision-makers from the UK; 608 across all markets. Multiple answers; share with agents in at least one function of the group.

Scaling AI means rethinking how people and agents will work together

AI is changing how work gets done, requiring organisations to rethink how they operate as people and AI agents work together.

Expectations for AI’s role in the workforce are changing. Compared with 2025, more organisations in the UK expect AI to create capacity and support growth for existing staff. The emphasis is expanding beyond helping employees perform existing tasks towards enabling teams to do more.

Realising that potential requires clear decisions about how people and AI agents share responsibilities. Organisations need to identify where human judgement is essential, where agents can act with oversight, and where routine tasks can run independently.

This chapter explores how workforce expectations are evolving and how organisations can design effective collaboration between people and AI agents.

The AI workforce revolution isn’t about replacing people. It’s about people and AI working together

What organisations in the UK expect the role of AI agents to be
86% expect AI agents to augment people
13% expect AI agents to reduce headcount
48% create capacity and growth for current staff
27% augment teams without major headcount change
12% create new roles
13% expect AI agents to reduce headcount
2% unsure

Base: 128 IT and business decision-makers from the UK. Percentages may not total 100% due to rounding.

Workforce expectations in the UK are shifting towards greater capacity and growth

As AI adoption advances, organisations are reconsidering how people and AI agents will work together. In 2025, 44% expected AI agents primarily to create new roles in organisations, while just 11% anticipated creating capacity and growth for existing staff as a result of adopting AI.

A year later, expectations have shifted significantly. The share expecting AI to expand workforce capacity and support growth has more than quadrupled (48%), while expectations for new roles have fallen to 12%. Meanwhile, expectations for significant headcount reduction have slightly increased to 13%.

What it means

While organisations in the UK initially expected AI primarily to create new roles, their focus has moved towards expanding the capacity and growth of existing teams. Expectations for new roles (12%) now sit below the global average of 17%, following a decline in the UK and an increase globally.

The role expected of AI agents: 2025 to 2026
Create new roles
44% 2025 12% 2026
Create capacity and growth
11% 2025 48% 2026
Augment teams, no major headcount change
36% 2025 27% 2026
Reduce headcount significantly
9% 2025 13% 2026

Base: 128 IT and business decision-makers from the UK (2026 Creatio Global Enterprise AI Adoption Survey); 188 business and technology decision-makers from the UK (2025 Creatio Global AI and No-Code Adoption Survey).

Expert’s Perspective

Agentic AI allows for autonomous IT, which means that organizations can now remove human effort from the lion’s share of their workflow. Another way to think about this is that rather than having human work augmented by AI, organizations now have AI work that is moderated by human oversight. The net result is that processes that used to take weeks can now be accomplished in just a few hours.

Rob Garmaise
Rob Garmaise
VP of AI Research, Info-Tech Research Group

If the data is solid, AI can do the early analysis and our engineers can focus on the real repair. That mix makes the job easier, speeds up the resolution, and removes a lot of the frustration for customers and the team.

Steve Hunter
Steve Hunter
Enterprise Architect, Sureserve

Deeper AI adoption is linked to stronger revenue outcomes

UK organisations that integrate AI into a larger share of their workflows are more likely to report revenue growth.

Companies in the UK are already seeing measurable business outcomes from AI, including greater productivity, faster decision-making, and improved customer experience. As AI becomes more deeply embedded in the business, its impact extends beyond operational improvements towards revenue growth.

The findings suggest that the value organisations gain from AI evolves as adoption matures. Early deployments can make existing work faster and more efficient, while deeper integration across workflows creates more opportunities for AI to contribute directly to business performance.

This chapter examines the business outcomes organisations are achieving with AI and how deeper adoption is linked to stronger revenue results.

Revenue gains grow as AI adoption expands

Share of organisations reporting increased revenue from AI in the past six months
  • UK organisations (n = 125) Share reporting increased revenue, by share of workflows automated with AI
  • All organisations (n = 608) Same measure across the full sample

Base: 128 IT and business decision-makers from the UK, of whom 125 answered the workflow question.

Productivity gains come first; revenue growth follows as AI adoption matures

Organisations in the UK are already seeing measurable benefits from AI. Over the past six months, 63% have reported increased productivity, 53% faster decision-making, and 49% improved customer experience. One in four have also seen revenue growth.

The business impact of AI appears to deepen as adoption matures. Among AI Adoption Leaders, 38% report increased revenue, versus 21% of others.

What it means

The value of AI may evolve as organisations move beyond initial deployment. Early gains often appear in how work gets done, through greater productivity, faster decisions, and better customer experiences. At more advanced stages of adoption, AI is increasingly associated with revenue growth. The opportunity is not limited to making existing operations more efficient, but to use AI to contribute more directly to business performance.

Where AI is delivering business impact
  • Increased productivity 63%
  • Faster decision-making 53%
  • Improved customer experience 49%
  • Reduced operating costs 32%
  • Improved employee experience 31%
  • Improved compliance 26%
  • Increased revenue 25%

Base: 128 IT and business decision-makers from the UK.

Companies scaling AI are nearly 3x more likely to see revenue gains as a positive impact of AI

Revenue results track AI adoption depth in the UK as well. Among organisations automating less than 10% of their workflows with AI, 13% report increased revenue in the past six months. The share rises to 22% at 10%-25% of workflows, 23% at 26%-50%, and to 38% above 50%. The agentic share of AI initiatives follows a similar, slightly flatter curve, from 18% to 32%.

What it means

The difference is significant: organisations automating more than half their workflows are nearly three times as likely to report increased revenue as those automating less than 10%. Organisations weighing whether to press on past selected functions can expect revenue results to follow adoption depth.

Revenue increase by adoption level, indexed (UK)
  • Share of workflows automated with AI
  • Share of AI initiatives that are agentic
  • All markets

Base: UK organisations across all adoption cohorts, grouped by share of workflows automated with AI (8, 32, 56, and 29 respondents) and share of AI initiatives that are agentic (22, 27, 48, and 28 respondents). Index = share reporting increased revenue in each group relative to the first group (1.0x).

What enterprise AI Adoption Leaders do differently

With deeper AI adoption linked to stronger revenue outcomes, the next question is what distinguishes organisations leading that adoption.

AI Adoption Leaders combine extensive AI deployment with more established governance and closer coordination between business and IT teams.

Their approach extends beyond individual deployments. Leaders embed AI across the business, automate a greater share of workflows with AI, and report greater confidence in their ability to scale.

This chapter examines the practices that distinguish AI Adoption Leaders from the rest, offering practical priorities for organisations in the UK seeking to expand AI adoption and strengthen its contribution to business performance.

Meet the AI Leaders

23% AI Leaders
77% Market Mainstream
23% AI Leaders

AI Leaders account for 23% of surveyed organisations. The remaining 77% make up the market mainstream.

Base: 128 IT and business decision-makers from the UK.

How we classified enterprise AI Adoption Leaders

We scored AI adoption maturity on two criteria added together into one AI adoption breadth score from 0 to 16. The two criteria are the number of seven different AI capabilities rated advanced and the number of nine different business functions exploring or deploying AI agents.

AI Adoption Leaders score 10 – 16 points, demonstrating both depth and breadth of adoption. They are advanced across most of the seven AI capabilities assessed and are exploring or deploying AI agents across 6.9 of nine business functions on average, showing that AI is becoming embedded across the business.

Seven possible AI capabilities
0-7 points

one for each capability the organisation rates as advanced on the four-step scale

  1. Using AI in daily work
  2. Automating core processes with AI
  3. AI agents executing tasks
  4. AI in standard operations
  5. Measuring AI outcomes
  6. Building and configuring AI agents
  7. Overall breadth of AI adoption across the business
Nine possible business functions
0-9 points

one for each business function that is exploring or deploying AI agents today

  1. Sales
  2. Marketing
  3. Service
  4. Customer success
  5. Software development and DevOps
  6. IT operations
  7. Finance and procurement
  8. HR and recruiting
  9. R&D and innovation
16 possible points

Base: 128 IT and business decision-makers from the UK.

Three profiles of AI adoption maturity

AI Adoption Leaders make up 23% of UK organisations exploring or deploying AI across their organisations. Deployers make up 34% of organisations, and Explorers make up the remaining 43%.

Leaders

Breadth score 10 to 16

Advanced in most of the 7 AI capabilities, exploring or deploying agents in 6.9 of 9 business functions on average.

  • 45% automate over half their workflows with AI
  • 31% make over half their AI initiatives agentic
  • 45% already scaling agents across departments
  • 90% very confident about scaling AI

Deployers

Breadth score 6 to 9

Advanced in a few capabilities, with agents in about 4 functions (4.6 on average). AI is in production, not as broadly as the Leaders.

  • 25% automate over half their workflows with AI
  • 30% make over half their AI initiatives agentic
  • 36% already scaling agents across departments
  • 52% very confident about scaling AI

Explorers

Breadth score 0 to 5

Advanced in 1 or 2 capabilities at most, with agents in two or three functions (2.7 on average). Most AI is still in a pilot stage.

  • 9% automate over half their workflows with AI
  • 11% make over half their AI initiatives agentic
  • 20% already scaling agents across departments
  • 27% very confident about scaling AI

Base: 128 IT and business decision-makers from the UK. Breadth score = capabilities rated Advanced (0-7) + functions with AI agents (0-9). Leaders 10-16, Deployers 6-9, Explorers 0-5.

Five things the AI Adoption Leaders do differently

4x

Embed AI across the business

34%

of Leaders describe AI as embedded across the organisation, against 8% of others (14% of Deployers and 4% of Explorers). And 45% are already scaling agents across departments, against 36% and 20%.

Nearly
3x

Deploy AI in more than half of their workflows

45%

of Leaders automate more than half of their workflows with AI, against 16% of others (25% of Deployers and 9% of Explorers). And 31% make more than half of their AI initiatives agentic, against 30% and 11%.

1.6x

Govern AI before they scale adoption

45%

of Leaders run fully established AI governance, against 27% of others (34% of Deployers and 22% of Explorers). And 41% review AI as a formal board agenda item, against 30% and 20%.

1.4x

Adopt for customers and measure people outcomes

38%

of Leaders measure employee satisfaction, against 26% of others (23% of Deployers and 29% of Explorers). And 34% name customer experience as a reason to pursue agents, against 31% of others (39% and 25%).

2.3x

Scale AI with confidence

90%

of Leaders are very confident they can scale AI, against 38% of others (52% of Deployers and 27% of Explorers). And 52% see the pace of AI as a balanced risk, against 25% and 38%.

Base: UK Leaders 29, Deployers 44, Explorers 55 (Leader figures are directional).

Expert’s Perspective

Some of the most useful AI work in any company is already happening, built quietly by individual teams to make their own jobs easier. The job of leadership now is to find those improvements and put them to work across the business, rather than waiting for a top-down strategy. In our own development team, that shift took us from asking what we could do with AI to asking what we can't.

Barney Holmes
Barney Holmes
Senior Vice President, CRM, Bits in Glass

AI transformation isn't a single moonshot. It's a Mars Landing, requiring multiple interconnected missions to succeed. The 17%* of organizations leading in AI understand that real transformation isn't about deploying more technology. It's about orchestrating people, governance, and AI as one interconnected system.

Dr. Ja-Naé Duane
Dr. Ja-Naé Duane
Futurist, Best-Selling Author and AI Researcher, Brown University School of Engineering and MIT CISR Academic Fellow

* 17% across all markets surveyed; 23% in the UK.

How organisations are approaching AI governance

Established governance is linked to nearly 5x greater confidence in scaling AI.

Organisations must develop trust between people and AI agents, and much of that trust rests on governance.

More than four in five UK organisations (82%) have a fully or partially established framework, but only 31% call it fully established, and only one governance practice (security reviews) is rated mature by more than half.

Organisations with a framework are more likely to see the risks of moving too fast and too slow as balanced (39%) than those still developing or planning one (24%), and fully governed organisations are nearly 5x as likely to feel very confident about scaling AI.

Governance is often assumed to slow innovation. This section shows the reverse: it comes before the confidence to scale.

Governance is becoming the foundation for scaling AI with confidence

Adoption of AI governance frameworks
82% have some form of AI governance in place
31% Fully established
51% Partially established
14% Currently developing
2% Planning to create one
2% No plans

Governance is shifting from guardrail to growth enabler.

Base: 128 IT and business decision-makers from the UK.

Strong governance lays the foundation for AI at scale

Most organisations have begun establishing AI governance, but relatively few have completed the process. Only 31% report having a fully established governance framework, while 51% have a partially established framework and 14% are still developing one. Despite this, the UK is ahead of the global average, with 82% of organisations having some form of AI governance in place, compared with 73% globally.

Adoption of AI governance frameworks across organisations
82%
have some form of AI governance in place
  • 31% Fully established
  • 51% Partially established
  • 14% Currently developing
  • 4% Planning or no plans

Expert’s Perspective

AI governance isn't just about guardrails. It's about designing how humans and agents make decisions together. Organizations with established governance are four times* as confident about scaling AI, yet only 36% have human review in place. The real challenge isn't how much autonomy we give AI, but how we ensure human judgment remains meaningful.

Dr. Ja-Naé Duane
Dr. Ja-Naé Duane
Futurist, Best-Selling Author and AI Researcher, Brown University School of Engineering and MIT CISR Academic Fellow

* four times across all markets surveyed; five times in the UK.

Base: 128 IT and business decision-makers from the UK.

AI governance is advancing, but many organisations have yet to reach maturity

Governance maturity is uneven across practices. Organisations in the UK are furthest along with more established risk and security controls, like security reviews (51%) and risk assessments (45%). Practices needed to govern AI as its use expands are less mature: only 36% have established human review of AI decisions, 28% have mature AI ethics guidelines, and just 10% have mature vendor governance standards.

What it means

As agents take on more responsibilities and AI becomes embedded across more workflows, organisations in the UK need governance that covers the full AI lifecycle – from how AI is selected and used to how its decisions are reviewed, monitored, and held accountable. This means establishing clear usage policies, defining where human oversight is required, and regularly reviewing AI risks and performance. Building these practices early can help governance keep pace with adoption and give organisations greater confidence as they scale AI across the enterprise.

AI governance practices by maturity
  • Security reviews 51%
  • Risk assessments 45%
  • AI usage policies 44%
  • Employee AI training 42%
  • Human review of AI decisions 36%
  • Compliance reviews 33%
  • AI ethics guidelines 28%
  • Vendor governance standards 10%

Base: 128 IT and business decision-makers from the UK. Practices question allowed up to three answers.

Governance and confidence scale AI together

The maturity of AI governance is closely associated with how confident organisations feel about scaling AI. Among organisations in the UK with fully established AI governance, 83% feel very confident about scaling AI. Among those still developing their governance framework, just 17% say the same.

Mature governance is also more common among AI Adoption Leaders: 33% of organisations with fully established governance qualify as Leaders, compared with 17% of those still developing their framework.

What it means

Governance is often assumed to slow innovation, but our findings challenge that idea: organisations in the UK with more established AI governance are more confident about scaling AI across the enterprise.

At scale, clear policies, controls, and oversight can give organisations the confidence to expand AI into more workflows and grant AI agents more autonomy. The goal, therefore, is not simply to control AI but to create the right conditions to scale it responsibly.

Run a fully established AI governance framework
45%
Leaders
34%
Deployers
22%
Explorers
How confident companies are about scaling AI, by governance stage
  • Fully established 83%
  • Partially established 42%
  • Currently developing 17%

Base: 128 IT and business decision-makers from the UK. Governance status n = 40 / 65 / 18; planning (n = 3) and no plans (n = 2) omitted. Leaders 29, Deployers 44, Explorers 55.

Expert’s Perspective

The most important thing to remember about governance is that it needs to be right-sized based upon organizational needs. All too often, we see IT teams that have to peel back previous governance efforts for fear that they have unintentionally become the department of “No.”

Rob Garmaise
Rob Garmaise
VP of AI Research, Info-Tech Research Group

The challenges slowing AI adoption

The challenges that can slow broader AI adoption evolve as organisations move from experimentation to enterprise scale.

Among organisations at the earliest stage of AI adoption, a lack of AI skills, alongside data quality and security and compliance, are the significant challenges.

As adoption matures, skills become far less prominent. At the same time, enterprise-scale requirements move to the forefront: security and compliance rises even further, and commercial models and budgets, change management, and technology capabilities become increasingly important.

This chapter examines how these barriers evolve and what organisations in the UK need to prioritise to build a stronger foundation for scaling AI securely, responsibly, and effectively.

AI maturity doesn’t eliminate barriers, it raises the enterprise bar

Barriers that slow broader adoption by maturity
  • Security and compliance 44% → 55%
  • Budget for licences 24% → 52%
  • Technology limitations 25% → 34%
  • Lack of AI skills 55% → 21%

As adoption matures, the challenge shifts from learning how to use AI to building the enterprise foundation required to scale it.

Base: UK Explorers 55, Deployers 44, Leaders 29.

The barriers to broader AI adoption shift as organisations mature

When asked about the challenges that are most likely to slow broader adoption, organisations at the earliest stage of AI adoption highlighted a lack of AI skills (55%), alongside data quality and integration (49%) and security and compliance (44%).

Among the most advanced adopters, AI skills decline to 21%. At the same time, security and compliance rises to 55%, while budget for licences (52%), change management (38%), and technology limitations (34%) become increasingly important.

What it means

Scaling AI doesn’t eliminate barriers – it changes them. The challenge shifts from building skills towards establishing the technology, security, governance, and operating model required to run AI at enterprise scale.

Barriers to broader AI adoption by maturity
  • Security and compliance 44% → 55%
  • Technology limitations 25% → 34%
  • Budget 24% → 52%
  • Change management 35% → 38%
  • Lack of AI skills 55% → 21%
  • Organisational readiness 22% → 21%

Base: UK Explorers 55, Deployers 44, Leaders 29.

Scaling AI across an organisation requires an enterprise-grade foundation

As AI expands across the enterprise, organisations in the UK need more than strong AI capabilities. They need an enterprise-grade foundation that can support AI across data, security, people, technology, and operations.

When asked, “Which of the following is your biggest barrier to scaling AI across your organisation?” respondents identified data quality and integration (54%) and security concerns (53%) as nearly equal challenges, followed by skills and expertise (44%).

Change management (34%) and technology limitations (27%) also present significant barriers, while organisational structure (25%), governance (24%), and unclear ROI (14%) are cited less frequently.

The pattern is clear: scaling AI successfully depends on the strength of the broader enterprise environment around it – not on AI capabilities alone.

What enterprises need to address to scale AI
  • Data quality / system integration 54%
  • Security concerns 53%
  • Skills and expertise 44%
  • Change management 34%
  • Technology limitations 27%
  • Organisational structure 25%
  • Governance 24%
  • Unclear ROI 14%

Base: 128 IT and business decision-makers from the UK. Multiple responses allowed.

Expert’s Perspective

AI is no longer just a feature layer we bolt onto legacy workflows – it is the new operating system for enterprise. The companies scaling AI successfully aren't just deploying more models; they are completely re-architecting their business from the ground up. AI is shifting from a personal productivity hack into the core engine driving our decisions, operations, and customer value. Ultimately, winning in this era means building an organization that is AI-first by design.

Anatoliy Troshyn
Anatoliy Troshyn
CEO, Banza
What it means

Enterprises should choose AI platforms based on where they want to scale, not just where they plan to start. Integration, security, and workflow flexibility should be priorities from the outset. The right enterprise-ready platform can turn these potential barriers into a foundation for scaling AI across the organisation.

The AI era is changing what buyers expect from enterprise software

When asked about their expectations of enterprise software, 96% prioritised the ability to expand AI, workflows, or workforce capacity without creating new commercial barriers as adoption grows.

This signals a broader shift in enterprise software expectations. AI changes the economics of scale: growth is no longer driven primarily by the number of users or licences, but increasingly by AI consumption, tokens, agent activity, workflows, and applications.

As adoption expands, organisations in the UK need platforms that provide the capacity to scale these dimensions without unpredictable costs or repeated charges that can constrain growth.

96%
expect software to scale freely to support AI
  • 35% Deploy AI without constantly purchasing additional licences
  • 45% Scale freely across users, AI agents, workflows, and applications
  • 16% Scale as the workforce grows without buying seats
  • 4% No strong preference

Base: 128 IT and business decision-makers from the UK.

Expert’s Perspective

Favor outcome-based or flat pricing or consumption caps, so vendor inefficiency doesn't become your cost. If the metrics you measure haven't moved after a set period, treat the AI as a cost rather than an investment, and don't scale it. This doesn't mean not to use AI at all, just that it was applied to the wrong problem – or in a wrong way. Analyze and find a better suited problem to solve using AI.

Thomas Wieberneit
Thomas Wieberneit
CEO and Principal Analyst, aheadCRM
What it means

Enterprise buyers should evaluate software not only by its initial price, but by the economics of scaling AI.

The critical question is whether the platform can support more AI consumption, agents, workflows, applications, and business functions while keeping costs predictable and removing commercial friction as adoption grows.

How organisations measure AI value and ROI

AI measurement starts with operational gains, but mature programmes broaden the scorecard to business outcomes.

Organisations most often measure AI through productivity gains and time savings. Yet the data shows a measurement gap: the outcomes that motivate adoption are not always the ones organisations track.

As adoption matures, measurement broadens. Leaders are more likely to track revenue impact and employee satisfaction.

This chapter follows that progression: what organisations expect from AI, what they measure, where measurement falls short, and where measurable returns are already appearing.

AI agent adoption is driven by immediate gains and long-term ambitions

  • 56% AI agents to ensure increased efficiency
  • 42% AI agents to drive productivity gains
  • 40% AI agents to help build for the future

Base: 128 IT and business decision-makers from the UK. Share of organisations reporting each result as main reasons why their organisation is pursuing an AI agent strategy.

UK organisations initially pursue AI for efficiency, but the value broadens with maturity

Increased efficiency (56%) and productivity gains (42%) are the leading reasons organisations pursue AI agents, followed by building for the future (40%), operational excellence (35%), and cost savings (34%).

The mix changes with maturity. Productivity is cited by 52% of Leaders versus 35% of Explorers, while building the future rises from 36% among Explorers to 41% among Leaders. Cost savings, by contrast, falls to 28% among Leaders.

What it means

The value case for AI broadens as adoption matures. Efficiency can justify the initial investment, but enterprise AI programmes increasingly need to demonstrate productivity, customer, and longer-term business impact. That makes the choice of success metrics critical from the start.

Main reasons for pursuing AI agents, by profile
AllExplorersDeployersLeaders
Increased efficiency 56% 55% 55% 62%
Productivity gains 42% 35% 45% 52%
Building for the future 40% 36% 43% 41%
Operational excellence 35% 40% 32% 31%
Cost savings 34% 38% 34% 28%
Improved customer experience 32% 25% 39% 34%
Risk reduction 18% 16% 23% 14%
Employee enablement 11% 13% 9% 10%

Base: UK Explorers 55, Deployers 44, Leaders 29. Multiple answers allowed.

Expert’s Perspective

The question probably needs to be, should we do that with AI rather than can we do that with AI? Because you can probably do most things with AI, but it’s finding the right use case and then really getting that buy-in, realizing the value. I think that builds momentum within an organization and then you let it scale from there.

Christopher Burrows
Christopher Burrows
Director of Operational Systems and Digital Transformation, Veolia

UK organisations don’t always measure the outcomes they pursue with AI

Even when organisations name a clear reason for adopting AI, they do not always measure the corresponding outcome. Among those pursuing productivity gains, 72% track productivity metrics, while only 52% of those seeking cost savings track the corresponding results.

The gap is larger for strategic goals. Only 56% of organisations pursuing better customer experience measure customer satisfaction, and 35% building for the future measure innovation outcomes.

What it means

These gaps suggest that organisations' AI ambitions are advancing faster than their ability to measure business impact. Evaluating success requires organisations to look beyond operational efficiency and understand how AI contributes to customer experience, innovation, and long-term growth. Without clear metrics aligned with their objectives, organisations risk scaling AI investments without a complete picture of the value they deliver.

Who measures AI outcomes that match their reasons
Reason for adoptingMatching metricShare that measure it
Productivity gains Productivity gains
Employee enablement Employee satisfaction
Increased efficiency Productivity gains
Improved customer experience Customer satisfaction
Cost savings Cost savings
Time savings Time savings
Building for the future Innovation outcomes

Base: organisations in the UK naming each reason (n shown in order: 54, 72, 41, 44, 72, 14, 51). Reading: of the 54 organisations adopting AI for productivity gains, 72% measure productivity gains.

Expert’s Perspective

As AI matures and scales, measuring AI quality and cost across CX, AI compute, and human effort isn't an audit burden, it's a massive strategic opportunity. It exposes where AI is creating real breakthrough value versus where false resolutions are silently shifting work back onto humans.

A company celebrating a low AI token cost on a customer interaction is blind to the human cost, orders of magnitude greater, generated when a failed fix forces a second call, requiring a human agent to untangle what went wrong, calm an emotional customer, and rebuild lost trust.

Michael Partridge
Michael Partridge
Partner Service Excellence, Deloitte

The next phase of enterprise AI adoption

The report points to a clear shift. Enterprise AI is moving beyond experimentation and becoming part of how organisations operate. As adoption deepens, organisations need to think differently about how work is designed, how AI is governed, and whether their technology can support AI at enterprise scale.

At Creatio, we believe the next phase of AI adoption will be defined by these core principles:

AI should be embedded into workflows, not added as another disconnected tool. People and AI agents need to work across the same end-to-end processes, with the right level of autonomy and human involvement.

AI needs a unified foundation. As AI spreads across functions, organisations need a common environment to build, manage, and use AI agents. This gives business teams room to innovate while IT maintains enterprise-wide visibility and control.

Governance needs to scale with AI. Usage policies, human oversight, security, auditability, and monitoring should be built into how agents operate so organisations can scale AI with confidence.

Enterprise software needs to be built for AI at scale. Organisations need the freedom to expand agents, workflows, applications, integrations, and AI usage without technology or economics becoming a barrier every time adoption grows.

Enterprise AI is entering a new phase. The question is no longer whether organizations will adopt AI, but how they will scale it across the business. That requires a new foundation – one where people and AI agents work together at scale, governance is built in, and technology gives organizations the freedom to expand AI without adding complexity. This is the future of enterprise software we are building at Creatio.

Burley Kawasaki
Burley Kawasaki
SVP Industries, Creatio

About Creatio’s Enterprise AI Adoption Survey

Creatio’s State of Enterprise AI Adoption Survey was fielded between August and September 2026 and gathered 608 completed responses from IT and business decision-makers, director level and above, across sales, marketing, customer success, operations, IT, and executive roles in the United States, the United Kingdom, Mexico, and Colombia. Participants received no incentives for participating in the survey.

128

Business and technology decision-makers director level and above from the UK

10+ industries
  • 32% Manufacturing and Distribution
  • 32% Financial Services
  • 14% Technology
  • 8% Professional Services
  • 14% Other
Company size
  • 18% 10,000+ employees
  • 26% 2,500 – 9,999 employees
  • 40% 250 – 2,499 employees
  • 12% 50 - 249 employees
  • 4% <50 employees
The State of Enterprise AI Adoption: UK Edition
The full report, PDF, 50 pages
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About Creatio

Creatio is an AI CRM and workflow platform where people and AI agents work together – with no limits on users, agents, or scale. We help midsize and large organisations run customer workflows in the AI era. Headquartered in Boston, MA, with a global team and a large ecosystem of partners, Creatio serves thousands of customers in over 100 countries and automates tens of millions of workflows daily. Genuine care for our clients and partners is at the heart of our DNA.

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