Government needs to ‘fix the plumbing’ before AI can deliver, says SAS

Government must address longstanding problems with fragmented and poor-quality data if it wants to move artificial intelligence (AI) projects successfully from experimentation into production, according to SAS.

The company’s VP for EMEA public sector, Nicola Furlong, said attention around public sector AI continues to focus on models while the quality and management of the underlying data can receive less attention.

“You can bolt the smartest model in the world onto a mess – all you get is a faster, more confident mess,” she told Think Digital Partners.

“I’ve sat in enough procurement conversations now to know the pattern: it’s the model that gets the attention, but the data foundation can be an afterthought, and then six months later someone’s asking why the ‘AI project’ produced a beautifully-formatted wrong answer.”

Furlong said government faces a particular challenge because data is distributed across legacy systems and organisational boundaries that have developed over decades.

“Government departments need to fix the plumbing first. It’s less glamorous than an AI project, but it’s the bit that actually determines whether any of it will work.”

Moving beyond the pilot

Furlong said public sector AI deployments are beginning to move beyond experimentation, but the number reaching production is much smaller – “though there’s genuinely more happening now than a year ago,” she said.

2025 OECD analysis of almost 1,500 AI use cases collected by the European Commission’s Public Sector Tech Watch found that 58 percent were still planned, in pilot or in development.

“Starting a project and running it in production are very different things,” said Furlong.

Furlong said there are “plenty of proofs of concept that impressed everyone in the room, and a much smaller number that survived contact with real procurement cycles, real security review, and real budget scrutiny the following year.”

Governance needs to reach the frontline

Governance presents another barrier, according to Furlong, particularly where high-level policies have not translated into practical guidance for the people expected to use AI.

“The struggle is in the gap between ‘we’ve written a policy’ and ‘someone in the building actually knows what it means for their day to day’,” she said.

“A lot of governments now have AI strategies and governance frameworks on paper, but very few have built the mechanisms to actually measure whether their deployments are delivering public value.”

The OECD’s 2026 outlook found that 30 of 36 countries surveyed had at least one institution responsible for governing public sector AI, while measuring AI’s impact as an ongoing weakness.

Furlong said data sovereignty adds another layer to the challenge.

“Who owns this, where does it sit, who can see it under what legal basis – and you’ve got the two hardest governance conversations happening at once, usually without anyone senior enough in the room to actually decide,” she said.

Synthetic data and fraud

Furlong also sees a practical role for synthetic data, particularly where government organisations need to work with sensitive information.

Governments hold tax, benefits and health records that cannot be made available for training AI systems, she said. Synthetic data can provide statistically representative information without exposing the individuals contained in the original datasets.

It can also create examples of unusual events such as particular types of fraud.

“Fraudsters are already using AI to generate synthetic identities and forge documents at scale,” said Furlong.

SAS research published in December found responsible use of AI and analytics, integration with existing systems, and privacy and security among the main barriers reported by UK public sector fraud professionals. Forty percent said their departments already used AI, while nearly all expected to adopt AI or generative AI within two years if they weren’t already using it.

Furlong said fraud, waste and abuse teams are moving fastest with the technology, while applications affecting individual entitlements require greater caution.

“Anything touching individual entitlement decisions – benefits eligibility, immigration, anything where a wrong call has a face attached to it – moves much more slowly, and rightly so,” she said.

Trust as a measure of success

Furlong argued that government should avoid judging AI deployments primarily through productivity improvements.

“You can save a caseworker two hours a week and still lose if citizens don’t believe the system is fair,” she said.

SAS research found 96 percent of UK public sector fraud professionals surveyed believed fraud, waste and error had negatively affected citizen trust.

Furlong said measures of successful AI should include whether deployments reduce errors affecting citizens, allow employees to concentrate on work requiring human judgement, and make public services more explainable.

“Productivity is easy to put in a slide. Trust is the actual score to keep.”

Looking ahead, Furlong expects some of the most important uses of AI in government to become less visible.

“I think the honest answer is a lot less ‘AI does the thing’ and a lot more ‘AI quietly catches the thing before it becomes a problem’,” she said.

She also predicted that local authorities will gain greater access to sovereign and governed AI infrastructure that is currently more accessible to central government.

“That gap closing is overdue and, I suspect, closer than people think,” she said.