How are UK technology and IT teams using AI?

Technology and IT teams get more from AI than any other function. In aibl’s survey of 755 UK mid-market business leaders, 58% of technology, IT and data leaders report measurable AI ROI, the highest of any function. They also have the most mature governance and the lowest shadow AI. Yet the best-governed IT functions reach 87%, so most still have ground to make up.

The gap closes when governance moves from a written policy to controls built into the deployment pipeline.

How are UK IT and data teams using AI in 2026?

58% of UK mid-market technology and IT leaders can prove a measurable return from AI, against 45% to 46% in workforce and in sales, marketing and CX. 27% have reached the top of the governance ladder, also the highest of any function.

The most common uses in technology, IT and data teams are:

• Code generation and engineering copilots
• Security monitoring and incident detection
• Data quality checks and remediation
• Infrastructure and operations automation
• Model platforms and MLOps for the rest of the business

55% of technology leaders invest in AI mainly for efficiency. Those gains, such as faster code delivery or fewer security incidents, are easier to measure than revenue.

Why do IT teams still struggle to prove their own AI ROI?

40% of technology and IT functions have not yet proved the value of their own AI. 33% see early value they have not measured, and 7% say their AI is still largely experimental. The tools to measure AI exist. They are not always applied to the IT function’s own deployments with the same rigour IT applies to everyone else’s.

What is the governance cliff in IT?

IT teams with only informal guidelines still report 41% measurable ROI, higher than the same level in any other function. But those with a written policy that is not consistently applied drop to 33%. A document that nobody follows creates false assurance while practice drifts away from it.

Once the policy is enforced across the business, measurable ROI rises to 65%. Where governance is mature, it reaches 87% and shadow AI falls to 22%, the lowest figure anywhere in the survey. Our piece on how governance drives AI ROI sets out the full ladder.

Why does shadow AI keep growing across the business?

48% of technology leaders say shadow AI is common, the lowest of any function. But in IT the problem mostly comes from elsewhere: 57% say business units moving faster than IT approval is the main cause. In practice that looks like:

• AI subscriptions charged to department budgets that IT has never reviewed
• Personal API keys for AI services that nobody rotates or audits
• Analysts training models on extracts of production data in local notebooks
• Low-code AI agents connected to internal systems through service accounts IT did not issue

38% of technology leaders have seen AI deployed without an adequate security review. The answer is a faster approved route, not tighter policing. Our piece on why staff use unapproved AI tools explains why speed matters most.

What do the best IT functions do differently?

The IT functions at the top of the ladder share three habits:

• They build security, data residency and governance checks into the deployment pipeline, so engineers have no reason to route around them
• They run a self-service catalogue of approved AI tools that is faster than going around it
• They chair a cross-functional AI governance forum, so the rest of the business adopts the same standard

Where can IT and data teams get help with AI?

Partners listed under Data and Analytics in the aibl AI Enablement Directory work on data quality and platforms. Those under AI Implementation and Deployment build and run AI in production, and AI Governance and Risk partners help turn policy into controls. Our guide on how to choose an AI implementation partner covers the questions to ask before you shortlist.

Go deeper: the free AI Infrastructure 2026 playbook has the governance ladder and a 90-day plan for each level. The aiblLIVE AI Infrastructure track brings CIOs, CTOs and data leaders together to compare what is working.

What this means for the CIO and CTO

Check whether your AI policy is followed by asking three engineering leads to walk you through how they approve a new AI tool. Close the gap on two tools before adding any more. Then pick one approval step that people bypass and turn it into an automated check inside the deployment pipeline.

Frequently asked questions

Read the full research

This is the technology, IT and data cut of State of UK AI Adoption 2026, aibl’s benchmark of 755 UK mid-market business leaders, in partnership with Executive Summary. The full report covers every function, the governance ladder behind these numbers, and the four moves that separate the firms proving a return from the rest.

Read the full State of UK AI Adoption 2026 report →

If you are accountable for AI platforms and data, the AI Enablement Directory lists UK partners by category, including data and analytics, AI implementation and deployment, and AI governance and risk. New findings land each Friday in aiblBRIEF, our free weekly newsletter.

About the research: the State of UK AI Adoption Survey 2026 was run with aibl’s research partner Executive Summary. It covers 755 UK mid-market business leaders in organisations with revenues of £50m to £500m, fielded January to March 2026. Every figure above describes the leaders surveyed.

For more on how UK technology teams are putting AI to work, explore our AI insights for technology and data leaders.