What AI in invoice automation actually delivers – one year in
An honest look at what's working, what isn't, and what to look for if you're evaluating AI for your finance function.
Overview
If you’re weighing up AI for your accounts payable function, you’ve probably seen the demos. They’re impressive, sure. What’s much harder to find is a straight answer to the question that actually matters: once it’s live, what does AI in invoice automation really deliver?
That’s the conversation we want to have.
Join Emma Blackmore (Chief Marketing Officer, Snowfox) and Tome Haapsaari (Chief Growth Officer, Snowfox), together with a Snowfox client who has been running AI in their AP function for the past year.
What this is: This is a candid, practical look at the state of AI in AP in 2026 and what it looks like once it is part of everyday operations.
What this isn’t:This isn’t a product walkthrough or a polished success story. We’ll talk honestly about where AI is actually delivering real value and ROI, where it’s not, and what separates automation that sticks from automation that gets shelved six months after launch.
Our guest will share their experience in their own words: what drove the decision, what surprised them along the way, and what they’d tell another finance leader evaluating AI today.
What you'll take away
- A clear-eyed view of where AI in AP genuinely delivers, and where it doesn't
- A practical framework for evaluating AI invoice automation, whoever you're considering
- An honest, first-hand account of life with AI in a finance function, a year on
- Five things to test and to ask before you commit
Who it’s for
Finance leaders, AP teams, and anyone responsible for evaluating automation in their finance function.
- Industry expertise from Snowfox
Your hosts
Tuomas Haapsaari
Chief Growth Officer, Snowfox
Emma Blackmore
Chief Marketing Officer, Snowfox
- How Our AI Works
Self-learning AI that gets smarter with every invoice
Snowfox doesn’t rely on static rules or templates. Instead, it creates a continuous feedback loop where every approval, correction and exception makes the AI more accurate. The result? Predictions that improve automatically, without manual intervention.