The AI Leadership Paradox (Part 2): Hiring Without a Playbook

In part 1 of this series, we laid out the AI talent paradox: the need to hire some form of AI leadership has never been greater, but the profile of this leader is still taking shape. It’s a question that dominates conversations among CEOs and investors right now: who has a strong leader in place (and where did they come from), who’s still looking, and what is the right profile? Most organizations are figuring it out in real time.

So how do you approach this challenge with intention?

Based on our work across AI leadership searches, here’s what we’ve learned about hiring for a role that’s still being defined.

Hire the Person, Not the Résumé

At the 2026 Montgomery Summit our team attended, leaders from various AI-native, tech-enabled service platforms described their hiring philosophy in striking terms: they hire smart, creative problem solvers who know how to use these tools, not just vertical or functional experts.

That won’t apply to every context, but the underlying principle matters enormously for hiring AI leadership: when the role itself is evolving this fast, the person matters more than the pedigree.

The strongest AI leaders we’ve encountered share a common set of traits that go well beyond technical credentials:

Curiosity: The drive to understand not just the technology, but the business problems it can solve.

Comfort with ambiguity: The ability to move forward without a perfect plan, to experiment, and to learn from failure without getting paralyzed.

Learning agility: The capacity to absorb new information quickly, update their thinking, and apply insights across unfamiliar domains.

Problem-solving instinct: A bias toward action and creative approaches rather than waiting for the playbook to be written.

These aren’t just indicators of whether someone can do the job today. They determine whether a leader can grow with the role as it evolves.

Identifying a candidate with these traits is a long-term investment, and evaluating for them means going deeper in conversation: asking individuals to walk through a deployment that failed, a stakeholder they couldn’t convince, a moment when the technology didn’t do what the business needed. How they answer tells you far more than their résumé.

Separating Fluency from Fluff

When assessing candidates for an AI leadership role, the key is knowing enough to understand what you’re hearing. We’re not talking about the ability to build new LLMs from scratch, but rather a working level of knowledge in the tools that create measurable impact.

In conversations with candidates who are truly embedded in this work, the difference is unmistakable. They’ll walk through their stack, the specific technologies in use, how those tools are being leveraged across the business, and what they’ve learned from the things that didn’t work the first time. They can articulate how they arrived at the result when it was positive and when it wasn’t.

Then there are candidates who can speak the language at a headline level but haven’t lived it. They know the buzzwords and they’ve read the trend pieces. But when the conversation pushes past the surface, there’s no operational depth.

Many enterprises have moved past the experimentation phase. They’ve operationalized AI, they’re deploying it across functions, and they’re iterating on what works. The talent you’re looking for should be at that stage or ahead of it.

Where the Candidates Actually Are

Based on our recent searches, strong AI leadership candidates tend to cluster in a few areas, each with distinct strengths depending on what your organization needs in year one:

• AI and data operating partners from PE portfolios: Strongest for firms needing cross-portfolio deployment and ROI accountability.
• AI and data engineering leaders from infrastructure, software, or data-intensive companies: Best suited for organizations building technical product/platform foundations.
• Transformation leaders most recently from major consulting firms with hands-on AI implementation experience: Ideal where change management is the primary challenge.
• Chief Data Officers and Chief AI Officers from healthcare and financial services: Strong where governance, risk, and scale matter.
• Enterprise executives who’ve driven AI adoption inside large, complex organizations: Best for highly matrixed environments.

Each of these backgrounds brings different skills, and no single profile maps perfectly to every organization’s needs. That’s precisely why defining the scope of the role clearly, before going to market, matters so much. Understanding what your organization genuinely needs in its first year of AI leadership makes it possible to identify which backgrounds are most relevant and where to focus.

The Strand Won’t Hold Forever

As I illustrated in part 1, the current AI talent market is like two balls of rubber bands connected by a thin strand: the pioneers out ahead and the broader talent pool being pulled forward behind them. That strand is still holding, for now, and the pool of people who’ve truly operationalized AI at scale is growing (slowly, but meaningfully). The organizations moving with intention are the ones best positioned to capture that talent.

The organizations that navigate this well tend to do a few things differently:

• They define the role before they start the search. Rather than going to market with a wish list, they get honest about what the first year actually requires and build the spec around that.
• They rethink what “qualified” means. They prioritize curiosity, learning agility, and problem-solving instinct over a perfect résumé match. The perfect profile for this role doesn’t exist yet, but these traits allow a leader to evolve with the role as the organization’s needs change.
• They move with urgency. In a market shifting this fast, indecision is its own risk. The best candidates won’t wait, and the landscape won’t hold still.
• They bring in expertise early. Not just a partner to run a search, but one to help shape the role, benchmark it against what the market can actually deliver, and evaluate candidates at a level of depth that traditional processes can’t reach.

The talent is out there. Finding the ideal individual requires knowing exactly what you’re looking for, crafting a role that makes sense for your business and the candidate marketplace, and being willing to move quickly when the right person surfaces.