· Saul Mateos · AI-Native CFO · 6 min read
The AI Readiness Finance Teams Actually Have
My cleaning lady killed my AI agent. What that says about the gap between AI excitement and the AI readiness finance teams actually have today.
My cleaning lady killed my AI agent. This is a story about AI excitement and the AI readiness finance teams actually have.
Not metaphorically. She closed the laptop lid on the second MacBook running my automation, and the agent just… died. Three hours of processing. And since it was running on my machine, I couldn’t use it again until I got home. Gone.
I laughed about it for a week. Then I realized it was the most honest thing that’s happened in AI this year.
Here’s what my actual week looks like
I’m roughly 80% vibe working with Claude Code. Forecasts, data analysis, decks, legal documents, policies, marketing workflows, HR documentation. I talk, it executes. Not perfectly. Not every time. But enough that my operational rhythm has fundamentally changed.
As I’m writing this, I have multiple Claude Code sessions running in parallel. One building a presentation. Another updating a project plan. A third pulling data for a dashboard.
Earlier in the week, I asked it to prepare a project plan for a critical project. It loaded it to Notion and assigned tasks. After our conversation with the team, it updated all the tasks and timelines. In parallel, I was creating multiple dashboards to track the performance of a couple of our critical operations.
That’s a week of work. Done by Friday.
This is real. This is happening. And if you haven’t experienced it, I get why it sounds like hype.
But here’s the part nobody posts about
I’ve been using Open Claw as a personal assistant. It has access to my emails, messages, meeting transcripts. It gives me a daily digest that’s actually useful. It even has its own mailbox where I can send it instructions and emails. Can’t receive external emails yet (by choice), but still. It works about 80% of the time.
It feels like what Siri was supposed to be. And honestly, now I get why Apple hasn’t shipped that yet. Same thing with the new Amazon Alexa. It understands what I’m asking 80% of the time. Which means it’s… not that different from the old one.
I’ll give you an example. Technology is one of my passions. And honestly, I love gaming. Western RPG games. I stopped playing for a long time, but now I restarted playing on the Go with my Legion Go. Some people doom scroll instagram, I play BG3. So I asked my Open Claw assistant to prepare a guide for me. A few iterations. Did a great job. But then I got the brilliant idea to ask for a powerpoint presentation instead of my previous pdf guide. Well, it had the brilliant idea to create the new presentation and delete my previous guide. The pptx sucked. But it forgot to tell me it had deleted the previous guide. So yeah. All work lost.
That was obviously a very low-stakes situation.
But this matters a lot when stakes are higher. And in finance, the stakes are very high.
Here’s why that matters for us. 80% doesn’t work in finance. Not when the other 20% can cost you real money. A forecast that’s 80% right is a forecast you can’t trust. A reconciliation that misses one in five entries is a reconciliation you’re redoing anyway.
But. 80% is still a massive leap when your baseline was doing everything manually. The math changes when you compare it to zero automation, not to perfection.
My finance team still lives in Excel. Power BI is their ceiling. And honestly? That’s fine. For now. Because the gap between me experimenting with AI and my team adopting it isn’t a training problem. It’s an infrastructure, trust, and culture problem. That’s a multiple-year shift minimum.
Both things are true: the AI readiness finance teams don’t have yet
I built a forecast in Claude that would’ve taken two days. My team still reconciles in spreadsheets. I’m 80% vibe working. Most finance teams haven’t started. The transformation is real and it hasn’t happened yet. Both/And.
This is the messy middle that nobody wants to talk about. Because admitting you’re deep into AI while also admitting most of your organization isn’t there yet doesn’t make for a great LinkedIn post. There’s only so much you can change yourself from one day to the next. And then there’s the legacy processes, the technical debt, the systems that weren’t built to change.
But it’s the truth.
So who actually owns this?
Every AI conversation I have with colleagues, founders, other CFOs ends the same way. “Who’s supposed to own AI at our company?” And the room goes quiet.
IBM found that 26% of organizations now have a Chief AI Officer. Up from 11% a year ago. Companies with a CAIO report 10% higher ROI on their AI investments.
Most people default to someone in Technology. And that’s when something dies in me.
I interviewed dozens of candidates. I kid you not. Most of them hadn’t vibe coded a single thing in their lives. Their use of AI was constrained to paying $20 for ChatGPT.
Makes sense. Someone has to bridge the gap between the excitement and the execution. Someone who understands the tech enough to evaluate it and the business enough to implement it.
Here’s what I keep coming back to: the best candidate for that role isn’t a technologist. It’s someone who’s lived in both worlds. CFOs and COOs who already operate across multiple functions. People who manage budgets, teams, technology, and operations simultaneously. People who can look at an AI tool and ask both “does this work?” and “what does this cost us if it doesn’t?”
We’re quietly the best candidates nobody’s considering.
The real threat
The threat isn’t AI replacing you. It’s the person who figured out vibe working while you were still waiting for the ROI study. The person who spent six months failing with AI agents and now has workflows that actually run. The person whose cleaning lady can kill their AI because they have AI running in the first place.
First-mover advantage in AI isn’t about being the smartest. It’s about starting. Failing. Starting again. Building the muscle memory that no certification can give you.
The gap between AI excitement and AI readiness is where the next generation of operators will be defined. The question is which side you’re on.