It’s clear to everyone in Finance that AI is here to stay. Whilst CFOs, reportedly, have been able to embrace AI less effectively than those in, say, Technology or Marketing, the transformation is coming and Finance teams will look very different within a few years.
In the light of this outlook finance teams will be smaller, they’ll spend less time on tractional work around AR, AP and reconciliations. It does mean conversely they’ll spend more time deploying techniques to forensically review the AIs work. The future rests in oversight and being the ‘human-in-the-loop’ where clearly there is value from human judgement. The primary characteristic of the Finance lead of the future is widely expected to be the person focused on partnering, ensuring finance influences business decisions. And that will be the case even in cases when the primary owner of that decision is in another silo like Sales or Marketing.
In the past any CFO, with a real commitment to building a world class team, always hired with the future in mind anyhow. In these recruitment processes there were always discussions on ‘where would this person be in two years’. With hiring managers asking themselves if a person coming in as Head of Finance, or Finance Director, would realistically be a CFO a few years down the road.
Now the hiring needs to be made in the light of the fact that the rate of change in AI has never been faster. In the past you’d have certain hygiene factors for a good hire in an accountant role or an FC role, double entry, navigating audit, reporting standards, and control processes. Of course you still need the person to have those things, so they can supervise the AI based agents that inevitably will carry out a high proportion of the foundational work in Finance.
You’ll need to ensure that candidates can prove to you that they have a real curiosity about AI, using it every week and month and advancing the uses cases they deploy it to, as the AI itself advances.
Understandably, your talent team will be deploying AI to sift through applications and more quickly recognise the skills and experience present on a CV that makes that person a good match for the role. However that’s really just the start of an AI centred hiring process for a senior person coming into a finance team.
Any finance leader bringing someone into the team right now should be sense checking they are familiar with the kind of questions might you need to include in your interviews and appraisal processes. To get the ball rolling here’s eight examples of questions that you can feed into your interview processes to check your hire has the best possible chance to be on top of what AI will bring for Finance?
- Which AI tools, and what model would you recommend in order to produce a market report that was highly numerical?
- Describe an improvement to a financial process that would combine both automation and a deployment of LLMs?
- Explain why in many cases those working in Finance are drawn towards deterministic models compared to probabilistic models?
- If providing some written content to the CFO, such as a financial commentary or process documentation, what steps would you go through before delivering that content?
- Explain some of the considerations around data privacy and confidentiality, when it comes to things like allowing AI models to summarise the businesses contracts?
- For a business that goes over to an ERP, in what areas do you expect the AI elements to excel? And which areas would be less impacted by AI?
- How did you use AI to prepare for this interview?
- Provide with a definition of business partnering for someone in the Finance team? Give me an example of one of the hardest things that a Finance business partner might have to do?
Above all, set them as tasks to do, and have them set the steps they took down. Ensure they can explain why they selected a particular AI tool, and variation or model of that AI. What inputs they had to give it and what their expectations were. Have them indicate how and why the AI might be able to learn and iterate, delivering a final outcome that is more refined and reliable compared to the first attempt.
The reality is that we have this question hanging in the air ‘What are you left with if AI does a very high percentage of the white collar jobs within a few years?’ Including probably all of the usual accounting, processing and reporting.
You are left with all of the deeply human responsibilities that tend to set side by side with influence and accountability. Persuading other people and taking responsibility for decisions that the Board might seek to scrutinise afterwards. We’re still some way from the AI recommending key decisions to the Board, after all the board seeks interaction in that and absolutely wants to know who the buck stops with. We’re not quite at the point where we can fire the AI, not quite yet.
