News

By Tammy Hawkins, Client Partner, Head of Western United States, and Head of Technology & Digital – North America

March 2026

AI has moved rapidly from experimentation to execution. As organizations accelerate their ambitions, many are making the same critical misstep - not in who they hire, but in how prepared they are for that hire to succeed.

What I am seeing in real time

AI is no longer a side conversation. It’s front and center in boardrooms. Across the companies I’m working with - whether enterprise platforms, private equity-backed businesses, or data center and AI infrastructure operators - there’s a very real push to move from “AI strategy” to actual execution. And almost always, that leads to the same next step: “We need to hire a Head of AI.”

On the surface, that makes sense. But here’s the pattern I’m seeing play out again and again:

The hire isn’t the problem. The environment they’re walking into is. The same mistake, just in a new form.

In the data center market, we’ve already seen what happens when leadership is hired too late or without clarity - projects fall behind, decision-making slows, and execution becomes reactive instead of strategic. AI is following a very similar path.

Companies often wait until:

• there’s pressure from the board
• competitors are announcing AI initiatives
• customers are asking about capabilities

…and then they hire. But by that point, expectations are already high and the foundation often isn’t there.

So instead of building, AI leaders spend their first 6–12 months untangling.

Why this is especially true in AI infrastructure and data centers

This dynamic is even more pronounced in AI infrastructure environments.

Why? Because AI isn’t just software - it’s deeply tied to compute, power, data, and physical infrastructure.

In data center and hyperscale environments, AI leaders are stepping into ecosystems where:

• capacity is already constrained
• power strategy is a board-level issue
• timelines are driven by customer commitments
• infrastructure decisions have long-term implications

Now layer AI on top of that. You’re asking one leader to navigate:

• data readiness, model strategy, and infrastructure constraints
• commercial expectations and, often, a still-evolving operating model

That’s a big ask - especially if the organization hasn’t aligned around what AI actually means for them.

What happens next (and it’s predictable)

When the organization isn’t ready, the symptoms look very familiar.

You’ll hear things like:

• “We’re not getting traction”
• “The roadmap keeps shifting”
• “We have pilots, but nothing is scaling”

Behind the scenes, it usually comes down to a few core issues:

• Decision-making isn’t clearly owned
• Data is harder to access than expected
• Business leaders aren’t fully aligned on priorities
• Expectations for ROI are immediate, but the work is foundational

None of this shows up in the job description. But it defines whether the role succeeds or struggles.

This is not a talent problem

Most of the AI leaders being hired right now are strong. Many are coming out of hyperscalers or leading technology platforms. They’ve seen scale, they understand the art of the possible, and they’re looking for environments where they can have real impact.

Which is exactly why this is so important.

When they step into organizations that aren’t ready, two things happen:

• Progress slows

• Frustration builds - on both sides

And over time, you either lose the leader… or you reset the mandate.

The role has changed (more than most realize)

AI leadership today is not just about models or algorithms. It’s about connecting the dots across the business.

The strongest leaders I’m seeing are able to move between:

• technical depth and business outcomes
• infrastructure realities and product strategy
• long-term platform thinking and near-term delivery

In AI infrastructure and data centers, that also means understanding:

• how power and compute constraints impact AI strategy
• how customer demand is evolving (especially with AI workloads)
• how to build something that scales — not just something that works

That’s a very different profile than what many organizations initially set out to hire.

So where does timing come in?

The companies getting this right are asking a slightly different question.

Not just: Who should we hire?”
But: Are we actually ready for this person to succeed?”

They’ve usually done some of the upfront work:

• aligned leadership on priorities
• clarified ownership across teams
• defined what success looks like in year one vs. year three

It doesn’t have to be perfect. But it has to be intentional.

Because in AI, just like in data center growth, timing and clarity are what create momentum.

Where retained search fits

In this kind of environment, hiring isn’t just about finding someone impressive on paper.

It’s about aligning:

• the role
• the mandate
• the organization’s readiness

That’s where a more structured, retained approach becomes valuable - not because the talent isn’t out there, but because the context has to be right.

The bottom line

AI transformation is going to create real winners and losers.

But it won’t come down to who hired the most AI talent. It will come down to:

• who built the right foundation
• who aligned their leadership teams early
• who brought in the right leaders at the right moment

AI leaders don’t fail because they aren’t capable.
They fail because the organization wasn’t ready for them to win.