UK Businesses Pour More Into AI but Blame Poor Data for Holding Results Back
SAP data suggests average UK AI spending is climbing fast, while many firms say weak data and fast-moving agentic tools are their biggest obstacles.
According to TechRadar, new data from SAP indicates that the average UK business now spends about £20.6 million on artificial intelligence each year. That figure is up 27 percent from the £16.2 million reported for 2025.
TechRadar reports that SAP expects the trend to continue. The company anticipates a further 45 percent rise in average AI spending between 2026 and 2028, which would bring the typical figure close to £30 million a year. SAP also expects return on investment to grow, with an estimated increase of 39 percent cited in the coverage.
The same reporting says that many firms are not satisfied with their progress. Half of the businesses surveyed reportedly worry that agentic AI, systems that take actions with limited human direction, is being rolled out faster than governance can keep up. TechRadar also notes that poor data quality is seen as a drag on results.
Respondents appear to see a remedy in better data infrastructure. According to the report, 67 percent believe that integrated data systems would help them get more from their AI investments.
The findings were published by TechRadar on October 14, 2026, and draw on SAP's figures. The coverage does not describe the survey methodology or sample size in detail, so the exact basis for the numbers should be read with that limit in mind.
Background
Companies have been investing in artificial intelligence for several years, moving from experiments toward production systems that touch customers, finance, supply chains and internal operations. Early pilots often produced impressive demonstrations but struggled to scale, a pattern many executives describe as the gap between proof of concept and measurable business value. Much of that gap is attributed to the condition of underlying information: records scattered across legacy systems, inconsistent definitions, duplicated customer files and missing histories can all degrade the output of machine learning models and generative tools. Enterprise software vendors have responded by promoting data platforms that connect systems and clean information before it reaches AI applications.
Governance has become a parallel concern. As AI tools have grown more capable, the industry has started talking about agents, software that can plan multistep tasks and call other applications on a user's behalf. Such tools raise questions that traditional software rarely did: who approves an action, how errors are traced, and what limits apply to access. Regulators in several regions have also moved toward rules on transparency, risk classification and accountability for automated decisions. For UK organizations, these debates sit alongside broader national interest in becoming a competitive market for AI development and adoption, with public and private investment both playing a role.
Rising budgets are not unusual in technology cycles. Spending often climbs before returns are clear, and firms commonly track adoption, productivity and cost savings over multiple years. Measuring return on AI can be difficult because benefits may be spread across departments and may show up as avoided costs or faster decisions rather than new revenue lines.
Why it matters
The numbers point to a shift in how businesses think about AI. A few years ago, many purchases were framed as trials. Now the figures suggest that AI is being budgeted as a recurring operating expense, closer to cloud computing or cybersecurity than to a one-off experiment. That change affects procurement, staffing and the way boards judge technology leaders.
The tension in the findings is also instructive. Companies are spending more while saying the foundations are weak. If data quality is the bottleneck, then money spent on models alone may underdeliver, and the real work may be in integration, data stewardship and clear ownership of information. That is less glamorous than a new model release, but it is where many projects succeed or fail.
Concern about agentic systems outpacing governance may prove the most consequential signal. Autonomous tools can act across systems at speed, so mistakes can spread quickly. Organizations that move ahead without clear rules for oversight, auditing and escalation risk operational, financial and legal exposure. Governance that arrives late tends to be built in response to incidents, which is an expensive way to learn.
For workers and customers, these trends will show up in everyday decisions: how quickly a claim is processed, how a help request is routed, or how a bank flags a transaction. The quality of those experiences will depend on choices made now about data and oversight.
What to watch
Watch whether the projected growth in spending is matched by disclosed results. Forecasts from vendors are useful indicators of market direction, but independent measurement of productivity, error rates and customer outcomes will say more about value.
Pay attention to how firms define return on investment. Estimates can vary widely depending on what counts as a benefit and over what period. Clearer methods will make comparisons between companies and years more meaningful.
Follow governance developments as well. Watch for internal frameworks that set permissions for AI agents, logging requirements, and named accountability for automated decisions. Also watch for any regulatory guidance that gives UK firms more specific expectations.
Finally, track whether data integration becomes a funded priority rather than a background task. If the 67 percent who favor integrated systems begin to act on that view, it could reshape enterprise technology budgets over the next few years.
Viewpoints
Commentary from our columns: three angles on this story.
The Skeptic open questions and weak points
Big spending projections deserve a second look before anyone treats them as a settled trend. The headline figures, a 27 percent rise this year and a forecast 45 percent jump by 2028, come from a single vendor's data. That does not make them wrong, but a company that sells data and AI platforms has an obvious interest in showing that demand is accelerating. The useful questions are simple. How were the businesses chosen? What counts as AI spending, and does it include staff time and cloud fees? And how is return on investment measured when benefits are hard to isolate?
There is also a gap between what firms say and what they fund. Many respondents reportedly describe poor data as the main brake on results, yet spending forecasts climb as if the brake were not there. Money can buy new models quickly, but it cannot instantly fix years of inconsistent records. Anyone reading these figures should ask whether the budget growth is aimed at the actual bottleneck or at the most visible tools. Growth numbers are easy to publish. Evidence that the money produced working systems is harder to find, and it is the evidence that matters.
The Builder the engineer's view
Anyone who has tried to ship an AI feature knows the data problem is real. A model is only as dependable as the records it reads, and in most large companies those records live in a patchwork of systems built over decades. Fields mean different things in different departments. Customer identities are duplicated. Historical data is incomplete or locked behind old interfaces. Adding a sharper model on top of that mess tends to produce confident answers that are wrong, and the cost of finding them later is high.
The sensible engineering response is to treat data integration and quality as a prerequisite rather than an afterthought. That means clear data contracts, lineage tracking, access controls and evaluation sets that reflect real workloads. It also means being disciplined about agents. An agent that can call tools needs bounded permissions, logging that a human can read, and a kill switch that works. Teams that build those pieces early usually move faster later, because they can change models without rebuilding the trust layer each time. The concern that deployment is outpacing governance is exactly what an engineer would expect when the pressure is to demonstrate progress quickly.
The Rulebook privacy, safety and regulation
When firms say they are worried that agentic AI is moving faster than governance, that is a signal regulators and customers should take seriously. Systems that can take actions across accounts, records and payments raise questions that are not answered by a general promise to be responsible. Who authorizes an action? What happens when an agent makes a mistake that affects a customer or an employee? Can the decision be explained afterward? These are governance questions, and they need written answers before scale arrives rather than after an incident.
Data quality carries a similar legal weight. Personal information held in inconsistent or outdated form can lead to inaccurate decisions, misdirected communications and unfair outcomes. Strong data governance, including clear purposes for use, retention limits and processes for correcting errors, protects people whose information flows through AI systems. Investment figures are a useful measure of ambition, but the more meaningful test is whether organizations can show that their systems are accountable, documented and open to review. Spending more without building that accountability simply makes the eventual reckoning larger.