A pattern keeps coming up in conversations with finance leaders in Ireland right now. They know AI is changing how finance functions work. They are watching their peers in larger organisations deploy tools that automate reconciliations, accelerate month-end close, and generate first-draft commentary on management accounts. And when it comes to their next hire, they are still writing job descriptions that could have been posted in 2019.
That gap is worth taking seriously.
PwC’s research on Irish business painted a striking picture: only 17% of Irish CEOs reported meaningful AI-driven revenue gains, while 51% named keeping pace with AI as their single biggest concern. Those two numbers together tell you something important. The anxiety is real. The results are not there yet. And for finance leaders, that tension lands squarely on the question of people — who you have in your team, what they can do, and who you are trying to hire next.
The finance function is changing faster than hiring has caught up
Automation has been rewriting the edges of finance work for years — accounts payable, bank reconciliations, basic reporting. That is not new. What is shifting now is that AI tools are moving into territory that used to require qualified judgement: variance analysis, forecasting narratives, even elements of financial modelling. The tools are imperfect. They require oversight, challenge, and interpretation. But they are capable enough that a finance professional who knows how to use them well produces materially better output, faster, than one who does not.
That changes what you need from a hire. Not less technical rigour — more. But alongside it, an ability to work with AI tools critically rather than either dismissing them or accepting their output unchecked. That combination is not common yet, and it is already becoming a differentiator.
What “AI-literate” actually means in a finance context
It is worth being clear about this, because the phrase gets used loosely. An AI-literate finance professional is not someone who has done an online course in prompt engineering. It is someone who understands what the tools can and cannot do, applies them to real finance tasks with appropriate scepticism, and can explain their outputs to a board or an audit committee without losing credibility.
In practice, this tends to show up in candidates who have worked in environments where they were actively encouraged to experiment — forward-thinking finance teams, scale-ups, or firms that have invested in their technology stack. It also shows up in candidates who have gone looking for it themselves, who have applied tools to their own work without being asked to.
When we are speaking with candidates at Financial Controller or senior FP&A level in 2026, this is increasingly a real differentiator. Not a box-tick, a genuine capability gap between candidates who are similar on paper.
What this means for how you hire
If you are hiring a Financial Controller, an FP&A Manager, or a Head of Finance in the next six months, it is worth asking yourself honestly: are you interviewing for this? Not asking “are you familiar with AI tools” and moving on, but genuinely probing how a candidate has applied them, what they got wrong, what they would not use them for.
The other thing worth examining is your job description. If it reads like a list of technical requirements and ERP experience, you may be filtering out exactly the kind of candidate you need — the one who has spent time building new ways of doing things alongside managing the traditional ones. Flexibility of thinking is harder to screen for than years of experience in a specific system, but it matters more now.
The upskilling question
Hiring is only part of the answer. Most Irish finance functions in 2026 have capable people who have not yet had the time, resource, or permission to engage seriously with AI tools. That is a leadership question as much as a training one. If your team believes that experimenting with new tools is not really sanctioned — that the priority is accuracy and throughput and anything else is a distraction — they will not develop the capability you need.
The CFOs who are ahead of this are not necessarily running formal AI training programmes. They are creating space for their finance teams to try things, tolerating a degree of productive imperfection, and building AI experimentation into how they think about development conversations. That is a cultural shift as much as a technological one.
Where to start
The most useful thing you can do this month is look at your next planned hire and ask whether your interview process would actually identify an AI-literate finance professional if one came through the door. If the answer is probably not, that is the place to start. Write one or two questions that would genuinely surface this capability. Test them in your next conversation.
The organisations that will be ahead in two years are not the ones who made the biggest investment in AI tools. They are the ones who built finance teams capable of using them well. That starts with knowing what you are hiring for.
