Ten Questions Your Board Will Ask About AI. Most CEOs Can Answer Two.

CEO AI Pain Series, Introduction: How This List Got Built

🌟 Editor's Note - Hi, I am an executive with decades in the trenches, summarizing proven techniques that are working today to help you succeed. Connect with me here: https://www.linkedin.com/in/stevegrady/

This CEO AI Pains Series insight introduces the entire series and provides context for the subsequent individual “Pains” playbook posts.

Question - Answer?

I started this project because I got tired of hearing the same four sentences that come up in almost every AI conversation I have with a mid-market CEO right now.

1. We're eighteen months in…
2. We've got things running…
3. I couldn't tell you what any of it returned…
4. And my board is starting to ask.

That's not a technology problem. It's a management problem disguised in technology's clothing. And the reason it persists is that nobody has laid out the actual list of things going wrong in a form a CEO can act on.

So I built the 10 CEO AI Pains list.

How I Built It

I used Claude for the research, and I want to be specific about what that means, because "AI-assisted research" has become a way of saying nothing.

I ran a deep structured sweep of public material including:

• Analyst coverage
• Academic studies
• Published survey data from CIO and CFO panels
• Earnings call language from mid-market public companies
• Vendor marketing claims
• Compliance and insurance requirements that are starting to appear in customer contracts.

Claude did the fetching and the reading. That part is straightforward, and it saved me weeks.

The part that mattered more was what came next.

I had Claude separate what the sources actually demonstrated from what they asserted. Vendor material is full of confident claims with no method behind them, and a model will happily blend those into a summary that reads as evidence.

So I made it tag every claim by source type and flag anything it could not trace. Then I had it cluster the traceable material into recurring pains, rank them, and argue against its own ranking.

That last “argue” step is the one I'd recommend to anyone doing this work.

The first list ranking Claude produced put security and governance near the top. That's a defensible read of the published material, because much of the existing AI-related material over-indexes on risk (the fear factor for eyeballs).

Claude is also wrong about what mid-market CEOs actually fund first, and I know that from sitting in discovery meetings. So I moved the GRC issues down and pushed business measurement and strategy up.

The model was reasoning from what has been written about. I was reasoning from what gets approved and purchased.

Then I worked to monetize the outcome of solving each pain point. Every pain was modeled against a single reference company so the ten are comparable to each other: $250M revenue, 400 people, four to seven core enterprise systems, no Chief AI Officer, twelve to eighteen months of pilot activity, fifteen or more scattered initiatives.

If that profile sounds like a description of your company, that isn't a coincidence. It's the shape of the market we work in.

I'll be straight about the results. This is deep AI research combined with my own field experience, not a primary study using a survey instrument. I'd rather be transparent with you than dress it up.

The Ten CEO AI Pains

Each of these Pains will have its own newsletter edition, with a Claude-based playbook you can run yourself before any AI agent exists. Here are the Pains in priority order, with one reason each earned its place

1. 1. No measurable ROI. Your pilots that worked and your pilots that did nothing look identical on paper. There's one missing field that explains why, and almost nobody fills it in before the money leaves.
2. Data not AI-ready. This failure has a signature timing. It shows up around 90 days into the project, after you've spent the budget and the calendar, and it gets blamed on the wrong thing every time.
3. Talent and expertise gap. You've probably been told to hire a Chief AI Officer. I'm going to argue against it, and tell you what smart execs do with that money instead.
4. No coherent AI strategy. There's a query you can run this week that will tell you how many AI initiatives your company actually has. It typically comes back well above what your CIO believes. I'll tell you where to run it.
5. Operating model not built for AI. Here's the question I've started asking clients with something AI-based in production. When your AI produces a customer-facing recommendation, and it's wrong, who is accountable? The pause before the answer is the whole post.
6. Adoption resistance. One variable predicts whether a person uses the tool you bought them. It isn't age, it isn't training, and it isn't the interface. It's something you can change in a quarterly objective.
7. Vendor selection paralysis. Nine demos, four months, no decision. The comparison isn't decidable as your team has constructed it, and there's one option almost nobody prices honestly.
8. Shadow AI and security. Search your expense reports for anything under twenty dollars a month on a corporate card. Then compare that to what your CISO believes is in use. The gap between those numbers is the post.
9. Trust and hallucinations. One wrong number in a customer proposal will cost you more than six months of earned trust. I'll show you where verification silently breaks down, and it isn't where you'd guess.
10. Governance and compliance. What you told a customer in March and what you told your insurer in September are both in writing. If they don't match, there is a bigger exposure than a missing policy.

What You'll Get in Each “Pains” Newsletter Edition

I will use the same structure in this series every time. The AI pain, stated honestly, including why it's harder than it looks. A numbered solution of four to six steps you can run with Claude using files you already have, no AI agent required.

I will detail the outcomes you should expect and the specific metrics to track success. And a paragraph describing the issues that will actually be difficult, because the organizational change work is always the part that kills these efforts, not the technology.

I'm also going to tell you what we're building as part of our TPC AI Orchestrate Solutions.

We're prioritizing our development plan for a set of C-level agents around these ten pain points, and I'll name the one that matches each post. None of them are finished. That's deliberate. I'd rather show you the thinking while it's still open to your input than announce a product and hope it fits.

What to Do Next

Read CEO AI Pain #1 when it shows up in your email, even if AI ROI measurement isn't your worst pain. Everything else in the series depends on proving real value, and it's also the cheapest thing on the list to fix.

Then here's what I'd like from you. When you get through the ten CEO AI Pains, tell me which one is costing you the most right now. My working assumption is that it's measurement and strategy for most CEOs, and governance for anyone heading toward a transaction in the next two years.

I've been surprised by actual user assumptions before, and the feedback changes what we build next and in what order.

CEO AI Pains post #1 lands next. It opens in the last ten minutes of a board meeting, when this whole problem tends to surface.

Now, if you want to jumpstart conversations in these CEO AI Pains, Solutions, and Outcomes, email me: sgrady@tpcon.com

Till next time…