I’ve heard this story before.
The excitement. The bold predictions. The C-suite mandates to “implement it — now.” The implementation teams scrambling to turn a PowerPoint vision into something that actually works on Monday morning.
I watched it all unfold with RPA. And now I’m watching it again with AI — except louder, faster, and with a lot more at stake.
My name is Tom Naylor. I’m a CPA and CMA who spent the back half of my career leading a process improvement group in finance and accounting operations. Along the way, I caught the technology bug early and never let go. By the time I retired, my team had researched, piloted, and put real RPA deployments to work — not in a lab, but in the actual messy reality of finance operations.
That experience gave me something I didn’t fully appreciate at the time: a finely tuned ear for hype.
What I Bring to This
When you’ve lived through one automation cycle — the promise, the pilots, the quiet failures, and the genuine wins — you develop a kind of pattern recognition. You learn to read the gap between what a technology can do in a demo and what it will do in your specific environment, with your specific data, run by your specific team.
I’m now a few years into retirement, spending my time following AI closely and learning it seriously. And I keep hearing echoes.
Echoes of the same hype cycle. The same expectation gap between leadership and the people responsible for making it work. The same pressure to move fast on something that still has rough edges. The same mix of genuine capability and genuine limitations that nobody on stage seems eager to discuss.
I’m not here to be cynical about AI. The capability is real — and in many ways more transformative than RPA ever was. But the path from “this is remarkable” to “this is working reliably in our operations” is not the one being described in most of the content out there.
Who This Is For
This site is for the people caught in the middle.
You’re not the executive who just saw a demo and wants it deployed by Q3. And you’re not a researcher building foundation models. You’re the operations leader, the finance practitioner, the process improvement person who has been handed responsibility for AI adoption — and you’re trying to figure out what’s real, what’s ready, and what’s going to quietly create more work than it saves.
I know that person. I was that person for RPA. And I’m trying to be useful to that person now.
Where I see parallels between the RPA era and today’s AI moment, I’ll name them. Where AI genuinely breaks new ground, I’ll say that too. The goal is an honest, experience-grounded take on what it actually means to implement AI in operations and finance — not a press release, and not a doom narrative.
The Question I Keep Coming Back To
How do we get involved in driving the train of AI while attempting not to get run over by it?
That’s what this space is about. Welcome.
Tom Naylor is a retired operations and finance leader with CPA and CMA credentials. He spent the latter part of his career leading process improvement and RPA initiatives in finance operations.