UK SAP users warn of data gaps slowing AI adoption

Research from UKISUG and Syniti shows that UK organisations face significant gaps in data quality and accessibility. The survey covered 46 SAP user organisations across the UK and Ireland.

The findings point to a disconnect between growing investment in data programmes and declining confidence in the information those programmes rely on. Half of respondents said they now have a comprehensive or near-comprehensive data strategy, up from 40%, while 79% said data transformation is now a business priority.

Confidence in data, however, remains low. Just 4% of organisations said they were very confident in data quality, and only 2% said the same of data accessibility. Almost all respondents, 94%, said data silos were preventing real-time decision-making.

Governance pressure

Data governance and compliance was the most common concern, cited by 72% of respondents. At the same time, only 21% said they use dedicated data quality tools, suggesting many are still trying to address core data issues without specialist systems.

The results suggest organisations are uncovering entrenched weaknesses as they push ahead with broader transformation work. Rather than bringing greater certainty, rising data maturity appears to be exposing longstanding problems with fragmented records, inconsistent standards and poor access to information.

That pattern is also visible in SAP migration work. As the move to SAP S/4HANA becomes a major programme for many large organisations, data management remains one of the main obstacles.

Some 76% said data management is a major migration challenge, while half expect to transform more than half of their data and supporting processes as part of that work.

The survey also identified practical barriers behind those concerns. The most commonly cited migration issue was the availability of business resources for data cleansing, named by 27% of respondents, followed by data quality issues causing delays, cited by 22%.

AI impact

The research also linked weak data foundations to slower adoption of artificial intelligence. A large majority of respondents, 89%, said data challenges would slow AI adoption, while 87% said high-quality data is essential to achieving a return on investment from AI.

These concerns come as many organisations face rising volumes of information, in some cases driven by their own AI initiatives. The data suggests businesses are wrestling not only with legacy issues, but also with the operational strain of managing larger, more complex data sets.

The picture that emerges is not of organisations ignoring data, but of them moving deeper into programmes that reveal hidden structural weaknesses. As data strategies become more formalised, shortcomings in governance, quality and accessibility are becoming harder to overlook.

For SAP user organisations, this matters because migration and transformation work often depend on data that is consistent, available and trusted across business functions. Where that is missing, projects can slow as teams spend more time on cleansing, validation and reconciliation.

The findings also show that business resource constraints remain a major operational issue. Even where organisations understand the need to improve data, access to people who can clean, govern and restructure it is limited, adding pressure to already complex transformation programmes.

Although the survey was limited to 46 organisations, it offers a snapshot of how data ambitions are playing out in practice among SAP users in the UK and Ireland. It suggests many businesses have moved beyond treating data as a secondary issue, only to find the foundations need more work than expected.

Among the starkest figures were the lowest-confidence measures: 4% of respondents were very confident in data quality, and 2% were very confident in data accessibility.