A lot has already been said about the impact of artificial intelligence (AI), particularly generative AI (GenAI), on the financial planning and analysis (FP&A) function. It can be hard to decipher the speculative from the tangible, but most commentators accept it will be increasingly beneficial in the future. This, though, leads to an important question for FP&A teams: what is our aspiration for AI?
The FP&A team should not answer this question in isolation. It is important for the whole organisation to debate and agree its aims.
Define a GenAI vision
As Randeep Rathindran, vice president at Gartner Research, recently explained: ‘The key is to think bigger and look at technology as a way to extend FP&A into the wider business rather than as just a way to boost internal capacity… New developments in the finance technology vendor market, and the prevalence of tools with embedded capabilities such as graph analytics, machine learning and generative AI make it easier than ever before for FP&A to transfer expertise to decision makers for complex decisions.’
At first glance, this seems to reinforce the worst fears of FP&A professionals: that GenAI will take their roles. The argument for it, though, is well made. As Rathindran says: ‘FP&A teams are over-extended, always in reactive mode, juggling complex requests that burn out staff, and they are still leaving service gaps because they can’t serve all the decision makers that need support… The typical modernisation approach of automating routine FP&A processes to create capacity for in-person decision support is simply not keeping pace with elevated demand in most organisations, and the current trajectory is unsustainable.’
All about context
Basically, there are not enough FP&A professionals to go around, so using GenAI to provide some sort out of self-service finance planning and analysis would be a way to empower the business in the same way that other digital services have democratised access to products and services; citizen developers and citizen journalists are two examples.
The best FP&A professionals are storytellers, who have a far greater understanding of the individual context
But before FP&A teams become resigned to the idea of ‘citizen FP&A’ colleagues replacing them, fear not. While there are clear opportunities to integrate GenAI into an FP&A environment, there is also significant work required to put the right foundations in place.
The best FP&A professionals are storytellers, who have a far greater understanding of the individual context for an organisation. They can examine deviations between actuals and forecasts to test assumptions and provide the business with a compelling view of financial performance. While GenAI can process data from infinite sources and can spot anomalies it provides quite generic commentary.
Today, an FP&A team is better placed to examine the deviations in a plan because they have the specialised knowledge of their organisation, combined with a historical understanding of business performance and the external factors affecting financial planning.
Encouraging an innovation culture will empower staff to test the technology
Follow the four pillars
In summary, it is positive that FP&A teams are not shying away from the GenAI opportunity. Success will depend on addressing the issues outlined above and structuring the approach around four pillars:
- Vision. Organisations must lay out a clear direction for the adoption of AI. It reassures employees and it provides a strong framework for AI policies and ethics. It is also important that the vision is not rigid as GenAI is still far from mature, and organisations must evolve with it.
- People strategy. Encouraging an innovation culture will empower staff to test the technology. It will help them to overcome uncertainty and understand how it can benefit them. Leadership is key, so FP&A teams have clarity around what the future holds, and avoids misunderstanding, which leads to key talent leaving. Retaining this talent is vital as it holds precious institutional memory, which no GenAI tool can replicate.
If organisations do not get their data strategies right, the adoption of GenAI will fail
- Technology strategy. Focus on the use case first. As with many new technologies there is a danger that people invest in GenAI, then look for a problem to solve; it must be the other way around. For example, when building a forecast in an FP&A solution and soliciting feedback from the business on any anomalies in the actuals compared with the forecast, GenAI could collate the feedback into a summary to identify trends. This is a straightforward use case which offers measurable time-savings for FP&A teams.
- Data strategy. While this falls under technology, it is so important it should have its own focus. If organisations do not get their data strategies right, the adoption of GenAI will fail. This is critical for data accuracy and integrity, also ensuring the right parameters for training AI models. FP&A teams must agree how much data is required, where it is sourced and how is it maintained to ensure accuracy. There is a big difference between results based on three years of daily data inputs rather than three years of monthly data inputs. Equally, it is critical to remove silos between information sources across the organisation, so that the GenAI tool can access a centralised data pool to ensure the accuracy of decision-making.
Organisations that address these pillars will be best placed to cut through the hype around GenAI and develop a strategy for their FP&A teams that is both pragmatic and ambitious.
