Midmarket finance leaders in the UK consider their departments ahead of others in adopting artificial intelligence, but new research uncovers a wide gap between perceptions of senior executives and operational staff.
The survey, involving 250 finance professionals from midmarket firms, points to significant differences in how adoption is measured and experienced within organisations.
Perception gap
According to the findings, 83% of surveyed finance functions claim some level of AI adoption. More than half of Chief Financial Officers (CFOs) believe their finance teams have fully adopted AI. In contrast, fewer than one in five Financial Controllers (FCs) agree. This 32-point gap highlights a disconnect between leadership's strategic perspective and operational realities on the ground.
Senior leaders' confidence in AI adoption also influences how they compare their progress with other departments. Large proportions of CFOs and Finance Directors believe the finance function is ahead of HR, sales, marketing, engineering, and IT in integrating AI. However, FCs are far more reserved in such assessments.
Partial uptake
While the outward appearance may suggest that AI is firmly embedded in finance functions, the underlying picture is far from uniform. Only 35% of finance leaders believe their teams have fully adopted AI, with nearly half reporting only partial adoption.
"CFOs are measuring AI adoption by whether tools are available and being used. FCs are measuring it by whether their day-to-day operational work has fundamentally changed. That 32-percentage-point difference is telling us that deployment and meaningful adoption are two very different things," said Rob Steele, CFO, iplicit.
The research finds that the majority of operational staff are still reliant on traditional processes. Data is often manually prepared and exported before AI can be leveraged for analysis and reporting. This difference in what counts as true adoption is also reflected in how senior leaders and controllers view integration with other departments and the role of AI in strategic decision-making.
Operational realities
Many finance teams report spending significant time on data preparation and validation prior to using AI tools for analysis or board reporting.
"CFOs see AI generating board commentary and dashboards, so the strategic view looks highly automated. But from an operational perspective, many FCs are still spending days producing the underlying numbers before AI can even begin its analysis. Currently, AI is excellent at the presentation layer, but the foundational finance work still requires significant manual input," said Andy Jackson, Financial Controller, iplicit.
Mark Pullen, CEO of SoMax Finance, commented on the industry-wide nature of this misalignment between leadership and staff. "We're seeing this perception gap every day in the midmarket. Senior leadership teams are convinced they've 'done AI' because dashboards and commentary look more automated. But when you get into the engine room with Financial Controllers and operational finance staff, the reality is very different.
"They're still exporting spreadsheets, fixing data manually and stitching together numbers before the AI tools can even begin their analysis. The message we give our customers is simple: if the underlying processes aren't automated, then AI isn't genuinely live in your finance function - it's just presenting the numbers faster."
Structural hurdles
The research indicates that foundational issues, such as fragmented systems and manual processes, remain widespread. Many finance departments have yet to reach a point where AI reshapes daily operations.
"In truth, many finance teams haven't adopted AI in any meaningful sense yet. What we often see is experimentation rather than transformation. And that's understandable - most SMEs are still working through foundational issues such as fragmented systems, inconsistent data, or manual processes that haven't been revisited in years. Until those basics are addressed, AI can only sit on the surface rather than reshape how finance actually works," said Chris Harman, Director, Candura.
Training shortfall
Despite high self-reported confidence in AI among leadership, formal training remains rare. Only 32% of survey respondents say they have received any structured guidance from their employer. Most are relying on self-directed learning or informal experimentation to understand and implement AI solutions.
"There's understandable pressure on senior finance leaders to lead the charge on AI adoption. However, with formal AI training lacking and most implementations being partial, many teams may be rushing transformation without ensuring the necessary foundations are in place first," said Steele.
Channel opportunity
Industry voices suggest this gap between strategic ambition and operational readiness represents a chance for specialist advisers and technology partners to redirect priorities.
"Channel partners have a critical role to play in slowing organisations down just enough to get the foundations right. We're having more conversations now about data structure, governance and workflow automation than about the AI tools themselves - and that's a positive shift. If the basics aren't in place, companies end up with AI generating commentary on data they can't fully trust," said Pullen.
"AI provides an amazing opportunity for organisations that are willing to put the basic building blocks in place. This means harnessing modern technology to support their tech savvy finance teams. The opportunity to exploit AI even at a rudimentary level will give finance teams a significant advantage over their peers and competitors at this point in time," said Harman.
"Finance leaders should feel energised about AI - not panicked. But what's needed now is cutting through the hype and adopting the technology responsibly," said Steele.
