If you’re a finance or accounting ops leader trying to separate AI signal from noise, you’re not starting from scratch — I’ve been through this before with RPA. I’m documenting what I’m finding, what I’m getting wrong, and what I wish someone had shared with me the first time around.
What You’ll Find Here
I’m testing AI tools on real finance and accounting work and writing about what happens — the parts that work, the parts that don’t, and the parts where the vendor’s demo and your actual data have nothing in common. If you’ve ever sat through a pitch where everything looked effortless and then spent three weeks trying to replicate it in your own environment, this is for you.
I spent over three years building an RPA function inside a large global finance organization. I watched the same pattern play out that I’m seeing now with AI: big promises up front, messy reality in the middle, and the people doing the actual implementation left to figure it out on their own. The difference is that AI is moving faster and the hype is louder — which makes the gap between what’s promised and what’s operational even harder to navigate alone. I’m using that experience as a lens, not because RPA and AI are the same technology, but because the adoption curve, the organizational challenges, and the human dynamics are remarkably similar.
You’ll find honest tool reviews, lessons from RPA that translate directly to AI rollouts, practical questions to ask before you commit budget or headcount, and observations about what the current adoption cycle looks like from the inside. No vendor partnerships, no affiliate links, no “10 prompts that will change your life.” Just a practitioner working through the same questions you are, sharing what I’m learning as I go.
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Been through the RPA hype. Now navigating AI – together.