A few weeks ago I was reviewing a financial model that AI (with my help) had built for a client. It was well-structured, logically layered, and used Excel functions I had not thought to apply. The model was, by most measures, better than what I would have built myself.
My reaction was not anxiety. It was closer to recognition. This is the actual value of AI: it expands what you can deliver, often beyond what your own skills and time would allow. For CFOs working independently or in lean teams, that expansion is significant.
But then I tried to validate it. And that is where things got harder. The model was using formulas I could not fully navigate. I could see it was sophisticated. I could not always confirm it was right.
This edition is about that gap: what to do when AI produces output that goes beyond your ability to check it, and how to make sure it does not become a liability.
The Validation Problem
Standard validation works when you understand the territory well enough to spot what is wrong. You check that a balance ties. You confirm a formula references the right cell. You catch a figure that contradicts what you know. These are checks you can run because you have enough domain knowledge to recognize the gap.
AI breaks that in two ways.
First, it can produce outputs more technically sophisticated than what you would have built, using methods you might not fully follow.
Second, it operates confidently across domains outside your expertise: legal language, tax structures, jurisdiction-specific accounting.
In both cases, the output arrives polished and professional regardless of whether the underlying reasoning is sound.
Two paths forward
When AI produces output you cannot fully evaluate, you have two paths. Which one applies depends on the stakes and on how far outside your expertise the territory is.
Path 1: Bring in a human expert.
When the stakes are high and the domain is genuinely outside your expertise, a human validator is a must. But here is the part most people miss: bringing in an expert does not mean starting from scratch. Use AI to build the strongest possible starting point, then bring the expert in to challenge, validate, and refine it. The expert is not doing the heavy lifting. AI already did that. The expert is applying the judgment that AI cannot replicate. That combination produces better output than either alone, and it is faster than a traditional engagement.
When you bring the expert in, brief them efficiently. Cover four things before they look at anything:
What the AI was asked to do and what it produced.
What decision or action this output is meant to support.
What specifically you are not able to evaluate yourself.
What a wrong answer would cost: financially, legally, or reputationally.
This model is underused because most experts are not yet thinking about their role this way. They are accustomed to being brought in at the beginning, not asked to validate AI output at the end. That will change.
The experts who will be most valuable going forward are the ones who develop the fluency to do this well.
I have recently received the first explicit requests not to build a model from scratch but to validate and refine an AI-generated one. I will share how it goes in the next editions. I think we will all be getting more of these requests, explicitly or not.
Path 2: Raise the quality of the AI output itself.
When the stakes do not justify bringing in an expert, or when you are navigating territory where you are not fully confident but still want to push forward, there are two things you can do to significantly increase your chances of getting a reliable result:
Prompting: Invest serious time in building your prompt before you ask for any output. The more context, constraints, and specificity you provide upfront, the smaller the surface area for error. This is not about finding the perfect phrasing. It is about giving AI everything it needs to reason well in your specific situation.
Stress testing: Once you have the output, run a structured challenge against it before acting. Ask AI to review its own work as a critical reviewer, not a validator. The goal is to surface assumptions, gaps, and failure points you might not have spotted yourself. The paid section this week gives you a specific framework and a reusable skill for doing this.
Most of your AI work will not justify bringing in an expert. That does not mean you are on your own.
In the subscriber section: a five-element prompt framework that reduces error before the output exists, and a devil's advocate stress-test you can run against any output you cannot fully evaluate. Both are practical, both are reusable, and one ships as a downloadable skill file.
Closing Thoughts
AI is genuinely expanding what CFOs can do. The Excel moment I described at the start of this edition was real, and I expect more of them. The skill is not learning to be skeptical of that expansion. It is learning to navigate it, knowing when to trust it, when to test it, and when to bring in someone who can see what you cannot.
The CFOs who get the most out of AI will not be the most technically sophisticated. They will be the ones with the clearest judgment about where AI ends and where human expertise begins.
I am curious where you are landing on this. Hit reply and tell me.
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Until next Tuesday, keep balancing!
Anna Tiomina
AI-Powered CFO