By Mark Bodger, Director at ICit Business Intelligence
There’s a shift happening in FP&A right now. Not because finance teams suddenly discovered AI, but because the technology has reached a point where it’s beginning to reshape how work actually gets done. Tools like Workday Adaptive Planning are embedding capabilities that move beyond incremental efficiency and start to influence behaviour. Whether that’s how teams spend time, how they interact with data or how they support the business.
The conversation, however, is still catching up. Too much of it is framed around features and terminology, when the more interesting question is structural: what changes inside the FP&A function when a significant portion of manual analysis is no longer required?
For most finance teams, the constraint has never been a lack of insight. It has been the effort required to get there. Hours are still absorbed by pulling data together, reconciling inconsistencies and manually investigating variances. That work has always been accepted as part of the role, even though it sits at the lower end of the value chain. What is changing now is the degree to which that effort can be reduced.
When data exploration becomes conversational and variance analysis is surfaced automatically with clear drivers and commentary, the balance of work begins to shift. The role of FP&A moves further away from being a gateway to information and closer to being a guide for decision-making. That distinction matters. It changes how the function is perceived internally, and it changes the expectations placed on it.
One of the more subtle developments is the removal of friction in accessing insight. Historically, financial models have required a level of technical understanding that limits who can engage with them directly. This has created a natural dependency on the often small number of individuals within the team who know how everything fits together.
As interfaces become more intuitive, that dependency starts to ease. Questions can be asked (and answered) without needing to navigate the underlying complexity. Over time, this has a levelling effect. It broadens access to insight without compromising control and it reduces the constant interruption cycle that many senior analysts experience.
The cumulative impact is often underestimated. It is not just about saving time; it is about creating space for more considered, higher-quality work. Variance analysis is a good example of where this becomes tangible. It has always been one of the most labour-intensive parts of the planning cycle, requiring a methodical breakdown of movements and drivers. It is also one of the areas most suited to standardisation. When that process is automated and when variances are identified, explained and contextualised in near real time, it introduces both speed and consistency.
Consistency, in particular, is often overlooked. It removes the variability that comes from time pressure or manual shortcuts and creates a more reliable foundation for discussion. Finance teams are then able to spend less time assembling the explanation and more time challenging what it means.
Forecasting is evolving in a similar direction. The introduction of predictive models built on a combination of internal and external data changes the nature of the starting point. Instead of building forecasts from the ground up each cycle, teams are presented with a baseline that reflects patterns and correlations across a wider dataset than any individual could reasonably process.
That baseline becomes the beginning of a more informed conversation. It allows finance to focus on judgement, scenario planning and alignment with business reality, rather than the mechanics of constructing the numbers.
The organisations seeing the greatest benefit are not simply enabling new features. They are reconsidering how work is structured. Questions around ownership, workflow design, and the balance between automation and judgement become more prominent. Decisions are made about what should happen by default, what requires intervention, and how outputs are communicated to stakeholders. In that sense, the technology is only part of the story. The more significant factor is how deliberately it is adopted.
Harnessing AI for Enhanced FP&A Report
Our report explores how AI and ML are reshaping the future of FP&A. It draws on recent independent research commissioned by ICit Business Intelligence, based on data and insights from over 300 UK FP&A professionals. Get your copy
