Before You Hire an AI Executive or Director, Answer These Questions (TPL Insights #321)
- Jun 19
- 7 min read
There is a conversation happening in boardrooms and C-suites across the country right now. It sounds something like this: "We need to do something with AI." Someone nods. A few people look at their phones. The CEO says he wants an AI person on the board or wants to hire a Chief AI Officer. The meeting moves on.
The instinct is right. In most cases, the execution is not.
I work with CEOs and boards on leadership, culture, and organizational health. Over the past several months, I have had the privilege of talking with some of the most forward-thinking AI practitioners I have ever encountered. People who have been building machine learning systems since the early 2000s.
People who have stood up digital infrastructure for global industrial companies. People who have built and sold AI-native businesses. What I have learned from those conversations has fundamentally changed how I advise clients who are trying to figure out where to start.
Here is what I know: most companies are asking the wrong questions.
The Question Is Not "Who." It Is "What."
When a CEO tells me he wants an AI expert on the board or an AI executive on the leadership team, my first question back is always the same: What do you want that person to do?
It sounds obvious. It rarely gets answered well.
There is a meaningful difference between a board director who provides strategic oversight and a fractional Chief Digital Officer who rolls up her sleeves and leads implementation. There is a difference between a Chief AI Officer who builds strategy from the ground up and someone who comes in to govern an existing roadmap. There is also a difference between someone who helps you understand what AI can do for your industry and someone who helps you build the data infrastructure you need before AI can do anything meaningful at all.
Most companies I work with are not ready for the deeper kind of help. They think they are. They are not. That gap between aspiration and readiness is where most AI initiatives stall out.
You Cannot Run Agents on Dirty Data
The most consistent theme across every conversation I have had with serious AI practitioners is this: the technology is not the hard part. The data is.
Before any meaningful AI implementation can happen, a company must be able to answer a basic set of questions. Where does your data live? Is it clean, structured, and accessible, or is it scattered across spreadsheets, shared drives, and people's heads? Have you digitized your historical records, or are decades of institutional knowledge still sitting in paper files? What does your cybersecurity posture look like, and does it meet the requirements of your most demanding customers?
These are not glamorous questions. Nobody gets excited about data hygiene the way they get excited about autonomous agents or generative AI. But skipping this step is like deciding to build a second floor before you have poured the foundation. You will spend a lot of money, make a lot of noise, and end up with something that does not stand.
One practitioner I spoke with recently described walking a company through a carve-out and building its entire digital platform from scratch. ERP, cybersecurity architecture, data governance, all of it. The PE board did not want to spend the money. They learned quickly that they did not have a choice. Their largest customers were already requiring certain levels of digital maturity as a condition of doing business. That is not a future problem. It is happening right now, in every major industry.
The CEO Has to Go First
Every transformation I have ever been part of, and I have been doing this work for nearly three decades, comes down to one variable: does the leader walk the talk?
AI is no different. If a CEO wants his organization to become genuinely AI-fluent, he must be visibly and demonstrably using these tools himself. Not just talking about them in all-hands meetings. Actually using them. Showing his team what it looks like to build a prompt, review an output, push back on a hallucination, and use the result to make a better decision faster.
The organizations I see making real progress on AI are the ones where the CEO has gone on his own learning journey and is not afraid to show it. He is not performing the expertise he lacks. He is modeling what it looks like to be a serious learner in a fast-moving environment. That signal, more than any strategy document or board resolution, is what gives an organization permission to change.
Ask Better Questions Before You Make Big Decisions
When I work with clients on AI readiness, I start by helping them develop a clear-eyed picture of where they actually are before we talk about where they want to go. That means asking hard questions across six areas: leadership alignment, data infrastructure, technology capability, use case prioritization, cultural readiness, and governance structure.
Some of the most important questions have nothing to do with technology. Does the senior team share a common definition of what AI even means for the company? Who owns AI strategy today, and does that person have the authority and budget to act on it? What is driving resistance in the organization? Is it fear of job displacement, data security concerns, or skepticism about reliability? Has anyone done even an informal inventory of where AI could add the most value, and in what order?
A thoughtful assessment of these questions, before a board seat is filled, before an executive is hired, before a vendor is engaged, and before a dollar is spent, will save enormous time and money. I have seen companies invest heavily in AI initiatives that stalled because nobody asked what the employees were afraid of. I have seen others spend months searching for the right external leader when the real gap was internal data governance.
Keep the Humans in the Loop
There is one more question I now put to every CEO and board before they go down this road, and I put it with more urgency than any other. How will you keep human beings in the loop? AI is a powerful tool. It is not a substitute for human judgment, and it cannot be allowed to become one. The moment an organization lets the machine make decisions, exercise discernment, set strategy, or define who the company is, it has handed over the very things that make it a company worth protecting.
I am an enthusiastic user of these tools. I also have a healthy respect for their dark side. AI will tell you what you want to hear. It will produce a confident answer that is wrong. It will optimize for whatever you point it at, including the wrong things, and it has no conscience to tell it when to stop. Left unchecked, it can erode the judgment of the very people who are supposed to be exercising it. That is why discernment, final decision rights, and accountability have to stay with humans. A person has to own the call. A person has to be answerable for it.
The guardrails that keep AI in its proper place do not come from the technology. They come from clarity about who the company is and where it is going. An organization with a clear purpose, mission, vision, and values has a fixed reference point against which every AI output can be tested. Does this recommendation serve our purpose? Does it honor our values? Does it move us toward our vision, or just toward a number? Strategy and PMVV are the guardrails. AI serves them. They do not serve AI. A company that knows what it stands for can use these tools aggressively and still protect what matters. A company that does not will be quietly steered by whatever the model optimizes for, and it may not notice until the damage is done.
So when you fill that board seat or hire that executive, do not ask only what they can build. Ask how they think about restraint. The best AI leaders I have met are not the ones most eager to automate everything. They are the ones who understand where the human has to stay in the chair, and who design their systems to keep it that way.
What Good Looks Like
If a company decides that AI leadership belongs at the executive level, the right construct depends entirely on where the company is in its journey. Early-stage readiness calls for someone who can assess the landscape, set priorities, and build foundational infrastructure. A more mature organization may need someone to govern strategy, manage vendors, and drive adoption across business units. Those are very different profiles.
If AI oversight belongs at the board level instead, the question is not just who has the credentials. It is those who have the time, the interest, and the judgment to serve as a genuine thought partner. Someone who will engage between quarterly meetings, challenge assumptions, make useful introductions, and help leadership think through decisions they have never faced before.
That person exists in both cases. Finding them takes the same rigor as any senior leadership search. The position specification must be honest about what the company actually needs, not just what sounds impressive in a press release.
The companies that will win in this environment are not the ones that move fastest. They are the ones that build on a solid foundation, lead from the top, and ask better questions than their competitors. That has always been true. AI just makes the stakes higher and the timeline shorter.
Rob Andrews is Chairman and CEO of Allen Austin, a retained executive search and organizational health consulting firm. He is the author of Organizational Health: The Ultimate Competitive Advantage, an Executive Ed.D. candidate at the University of Texas at Austin, and Affiliate Faculty in Leadership and Ethics at the McCombs School of Business. Allen Austin advises clients on retained search, AI governance, executive and board composition, and leadership strategy.
Rob Andrews
Chairman & Chief Executive Officer
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