"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.

Every accountant I talk to is excited about AI doing the work. Not one of them has asked the harder question: can you prove the AI got it right?

Agents categorize transactions now.

They draft journal entries. They reconcile accounts. They make changes faster than any human could.

But how do you know it's accurate?

How do you prove to a client that every number was reviewed and compliant? How do you show an auditor that an AI-generated entry followed the same logic a senior accountant would use?

"The AI did it."
This is not an answer in accounting.

Every number needs a trail.

Every change needs a reason.

We built Puzzle around this problem.

Not just AI that does the work. A system that proves the work was done right.
Immutable audit trails. Full traceability from GL line to raw transaction. Every agent action logged. You can review it. You can reverse it.

The AI does the work.

The proof of accuracy is the product.

When every platform has AI, the one that can prove accuracy wins the accountant. And the accountant wins the client.

Speed was the old differentiator.

Proof is the new one.

P.S. Excited to share we just launched AI Close for Accountants. The first agent builder built into the general ledger for accounting firms: https://lnkd.in/gv6KXbP9