Beyond the hype, AI is already changing how finance functions plan, close, and report. A grounded view of where it delivers value — and where it does not yet.
Every finance leader is being asked the same question by their board: what are we doing about AI? The honest answer, for most organizations, is that the technology is moving faster than the operating model can absorb. The opportunity is real, but capturing it requires discipline about where AI genuinely pays and where it is still a distraction.
This article offers a grounded view, based on what is actually working in finance functions today.
Start with the work, not the technology
The organizations getting value from AI in finance did not start by buying a tool. They started by mapping where their finance function spends effort and where that effort is low-value — reconciliations, manual data gathering, first-draft commentary, routine queries. AI is applied to those points deliberately, not sprayed across the function in the hope that something sticks.
Where AI is already paying
Several applications have moved beyond pilots and into genuine, repeatable value:
- The close — anomaly detection, automated reconciliations, and intelligent matching shorten the close and free skilled people for analysis rather than data wrangling
- Planning and forecasting — machine-learning models improve forecast accuracy and let teams run more scenarios, more often, with less manual rebuild
- Reporting and commentary — generative models produce strong first drafts of management commentary, which finance professionals then review and sharpen rather than write from scratch
- Controls and assurance — continuous monitoring across full populations of transactions, rather than samples, catches issues earlier
- Service and queries — intelligent assistants handle routine finance queries, reducing load on shared-service teams
Where it does not yet pay
It is just as important to be clear about where AI is not yet a reliable answer:
- Judgement-heavy decisions where accountability and explainability matter — capital allocation, material estimates, contentious accounting positions
- Situations where the underlying data is poor; AI amplifies data-quality problems rather than solving them
- Anywhere the cost of an unexplained error is high and the model cannot show its reasoning
The discipline is to use AI to do the heavy lifting and to keep human judgement firmly where judgement belongs.
The prerequisites
The finance functions getting value from AI share a few prerequisites:
- Clean, well-governed data — without it, every downstream application underperforms
- Clear ownership of models, including who validates them and who is accountable for their outputs
- A control framework that treats AI outputs as inputs to be reviewed, not answers to be trusted blindly
- People who are equipped to work alongside the tools — which is as much about capability and mindset as about technology
The operating-model shift
The deepest change is not technological but organizational. As routine work is automated, the finance function's centre of gravity shifts toward analysis, business partnering, and judgement. Roles change, skills change, and the shape of the team changes. Leaders who plan for that shift capture the value. Those who bolt AI onto an unchanged operating model rarely do.
A practical starting point
The most effective starting point is a focused diagnostic: where does effort go, where is value lost, and which two or three applications would pay quickly and build confidence? Done well, those early wins fund and de-risk the broader transformation.