A new study from FloQast, released August 11, 2026, has put a number on what many finance leaders already feel: accounting teams are enthusiastic about AI in theory and hesitant about it in practice.

The research, conducted across accounting and finance professionals in the US and UK, found that 85% of accounting teams have made AI a strategic priority. Just 10% are using it extensively.

That gap is not a technology problem. It is a governance, training, and trust problem — and it has a measurable cost.

The Gap in Numbers

FloQast’s research mapped organizations into AI maturity levels and compared their operational performance. The contrast is stark.

The most AI-mature finance teams spend just 34% of their time on manual work. The least mature spend 63% on the same tasks. Nearly double the manual burden, with the same accounting software and the same underlying data available to both.

The closing performance gap tells a similar story. AI-mature organizations complete the monthly close in an average of 6.7 days. Those at the earliest stage of AI adoption average 8.7 days — two full additional days locked in reconciliation, review, and sign-off cycles every single month.

Across a year, that adds up to 24 days of extra close time, and all the downstream pressure that creates on reporting timelines, board readiness, and finance team bandwidth.

Why Most Teams Are Stuck

The study is clear that the barriers are not technical. Finance teams are not struggling to access AI tools. They are struggling to trust them, train with them, and govern them properly.

The findings reinforce what practitioners have been saying for the past two years: AI adoption in accounting is not blocked by the absence of good software. It is blocked by the absence of the organizational scaffolding required to use that software responsibly.

The teams that are pulling ahead shared a common pattern. Leadership made a deliberate decision to build controls infrastructure and governance structure before scaling their AI investment — not after. They treated AI readiness as an organizational capability problem, not a procurement decision.

What This Means for Business Leaders

If your finance team is in the 85% who have AI as a strategic priority but the 90% who are not using it extensively, the path forward is probably not another software evaluation.

The more productive questions are:

Do your people trust the outputs? AI-assisted reconciliations and close processes only accelerate the close if the team is willing to act on the outputs without manually re-verifying everything. That trust comes from seeing the system perform reliably over time, in your specific environment, with your specific data.

Do you have governance in place? For finance teams operating in regulated industries or with audit requirements, AI-generated outputs need a clear ownership and review trail. The organizations succeeding here built that framework first, then expanded AI usage into it.

Is training continuous or a one-time event? Accounting AI tools change quickly. A training session from 12 months ago may not reflect what the current system can and cannot do. Teams that are outperforming their peers tend to treat AI capability development as an ongoing function, not a launch event.

The Strategic Window

The FloQast data suggests the accounting profession is at an early but accelerating inflection point. The gap between AI-mature and AI-laggard teams is measurable now and will likely widen over the next 12 to 24 months as the leading organizations build institutional knowledge that compounds.

For CFOs and finance directors, this is not a comfortable position to be in. The competitive advantage in financial close speed, reporting accuracy, and team capacity is beginning to accrue to a small group of early movers — and it is driven less by which tools they bought than by how intentionally they built the conditions for those tools to work.

The 10% using AI extensively are not necessarily the best resourced or the most technically sophisticated. They are the ones that treated adoption as a leadership challenge rather than a software challenge.

Where EDNA Fits

Enterprise DNA works with finance and accounting teams that are sitting in exactly this gap — clear on the potential, uncertain about the path. Through EDNA Learn, teams can build the practical data and AI skills that underpin confident adoption. And through Omni Advisory, leaders can work directly with advisors who have helped organizations move from AI aspiration to AI execution.

The FloQast study confirms what the data has been showing for several years now: the bottleneck in AI adoption for accounting is human, not technical. That is actually good news, because human problems have human solutions.