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Written by

Jodie Gallagher

Jodie Gallagher

Head of UK IFA Product, FE fundinfo

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FE fundinfo’s Jodie Gallagher: AI’s potential in financial services starts with trusted data

Jodie Gallagher, head of UK IFA product at FE fundinfo, sets out how AI is reshaping financial advice while exposing the importance of strong data foundations beneath the technology.

This article by Jodie Gallagher, Head of UK IFA Product at FE fundinfo, was originally published in IFA Magazine in October 2026.

Financial advice is entering a period of significant change. Tasks that once demanded hours of manual work can increasingly be accelerated, while clients themselves have access to tools capable of answering financial questions in seconds.

The FCA’s Mills Review makes clear just how quickly that shift is taking place. One in five UK adults are already using AI to support financial decisions, while recent industry research found that 95% of financial advisers have introduced AI tooling into their operations in the last year.

AI is therefore changing the advice relationship from both directions. Advisers are looking to it to improve efficiency and deliver a more personalised service, while clients increasingly have access to widely available AI tools that can help them explore financial questions independently.

But the Mills Review also points towards the less visible issue underneath that adoption: data. The FCA describes high-quality data as “foundational” to the effective use of AI. For advice firms, that principle extends beyond regulation and supervision. The value firms can derive from AI will increasingly depend on both the quality and connectivity of their own data and whether they can trust the data sources and insights underpinning the AI tools they use.

AI cannot be separated from the information it relies on. If client data is fragmented across systems, repeatedly rekeyed or difficult to reconcile, introducing more sophisticated technology does not make those underlying weaknesses disappear. But connectivity is only one part of the equation. As AI-native products become more prevalent, firms also need confidence in where the data informing their outputs has come from, whether it is reliable and up to date, and whether the appropriate rights and licences are in place for that data to be used.

Adoption is moving faster than infrastructure

AI is already becoming embedded in how financial advice firms operate. Industry analysis has found advisers are saving more than five hours a week, with 51% of firms reporting time gains through automation and improved efficiency. Yet the operational environment underneath that technology has not necessarily evolved at the same pace.

The clearest indication comes from advisers themselves. The same research found that 94% say they have access to accurate data, at the same time, 55% still compile management information manually through spreadsheets.

These findings are not necessarily contradictory. Data can ultimately be accurate while requiring considerable human effort to make it so. Advisers and support teams can still spend time transferring information between systems, reconciling records and checking different versions before that data becomes usable.

That distinction becomes much more important as AI moves further into the advice workflow. AI needs more than accurate information at the point of use. Firms need data that is structured and able to move reliably between the different systems and processes involved in serving a client. They also need confidence in the provenance of the data and insights their AI tools draw on. An output may appear authoritative, but its value in an advice context depends on the quality and reliability of the information underpinning it.

The next stage of AI readiness is about making sure the infrastructure surrounding AI tools can support its use at scale, while giving firms confidence in the information those tools use and produce.

The hidden cost of disconnected data

Much of the discussion around AI in financial services understandably focuses on what the technology itself can do. But the FCA’s emphasis on data quality points to the operational challenge sitting underneath it. The review recommends timely, structured and high-quality data to support cross-firm pattern detection and near real-time monitoring.

For advice firms, the same principle applies to the day-to-day use of technology. The more processes become automated or AI-assisted, the more important it becomes that the information passing between them is structured and trusted.

Yet data fragmentation can be remarkably difficult to see from inside a business because its consequences often appear as everyday administrative tasks rather than a single technology problem.

Only 13% of financial advisers cite data as a key area of concern, but advisers continue to spend significant amounts of time reconciling information between systems. The issue might present itself as another spreadsheet to update or information that has to be re-entered into another application. Collectively, these tasks point to the same underlying challenge: disconnected data and workflows.

AI makes addressing this dependency more important. As AI takes on a greater role across the advice process, firms need information that can move reliably between workflows without repeatedly relying on manual intervention.

That is where the productivity opportunity becomes much bigger than simply making individual tasks faster. Connecting the data and workflows involved in serving a client can reduce the administrative work required to move information from one part of the advice process to another, giving advisers more time to focus on advice itself.

Clients are changing faster than the advice model built for them

The urgency is reinforced by the other side of the Mills Review: consumers themselves are already changing how they engage with financial information.

Clients may increasingly arrive at a financial advice conversation having already consulted an AI tool, while bringing expectations shaped by the fast, personalised digital services they experience elsewhere. This is particularly relevant as wealth begins to pass to younger generations, who may be more accustomed to using AI tools to conduct their own research before seeking professional advice. That changes the role advisers play, and their value increasingly lies both in providing information and applying it to an individual’s circumstances, whilst bringing the judgement that a general-purpose AI tool cannot replicate.

Used effectively, AI can help advisers meet those expectations while preserving the human judgement at the heart of advice. The future of successful use of AI in financial advice will depend on whether firms can connect the trusted data, technology and workflows involved in serving a client.

The Mills Review shows how quickly AI is becoming part of the way consumers make financial decisions, raising the pressure on advice firms to make sure their own technology and operating models can keep pace. Where client information still has to be reconciled, rekeyed and moved manually between systems, the administrative burden on advisers remains.

Connecting those workflows is now an important part of AI readiness. Trusted client data needs to move accurately from meetings through planning, research, reporting and ongoing servicing, reducing the manual intervention required along the way. As AI becomes more embedded across advice, that connectivity will determine how much time the technology can genuinely give back to advisers and, ultimately, how much more of that time can be spent with clients.