"I've already automated a lot of my manual tasks, why invest time, and money, into AI right now?"


Every day I speak to finance professionals at very different points on the AI journey. Some are still figuring out where to begin. Some are using it for everything.

But the group I find most interesting - and probably most underserved by the current conversation - are the leaders who genuinely get it, have already built something good, and are now asking: does AI actually add value when things are already working well?

That question deserves a proper answer. Here's how I'm currently thinking about it.

Using AI for automation gets you from 100 to 50. AI as a tech stack does something different.

In an already-automated finance function, the process is still disjointed. You automate the flag, but someone still reviews, interprets, and acts on it. AI can close that loop - using your business context to not just flag a mismatch, but understand it and commit to an output. Input > insight > action, without the manual stitching in between.

Three areas where I'm seeing this matter most:

1) Workflows - A CFO already running a tight, automated function is well placed to go further. Experimenting with AI is a natural starting point, but the real step-change is when it's genuinely embedded into your workflows rather than bolted on top. That's where you move from "AI saves me time" to "AI runs this process."

2) Finance Systems - A dedicated Finance Systems or FinOps lead who's AI-literate can be transformational here. Not just maintaining your stack, but elevating it - building something that's not just automated but self-auditing and genuinely scalable as the business grows.

3) FP&A and Commercial - A Finance Data Analyst, or equivalent, as the connective tissue between data, finance, and commercial teams. Done well, this isn't about better dashboards - it's about surfacing commercial insight that wouldn't otherwise exist, and adding outsized value at a business level.

Right now (almost definitely) probably isn't the moment to tear up what's working. But it is the training phase, and the teams building these capabilities now - through tooling, through the right hires, or both - will have real options when the technology matures further.

Always interested to hear what people think of this.. feel free to share your thoughts below.