AI is rewriting the criteria for hiring product and engineering leadership

Eighteen months ago, some of the practices that define today’s best AI-era leaders might have looked like red flags. Small teams with no structured roadmap. Leaders openly saying, “I don’t know yet.” CTOs redesigning the entire org rather than managing it. Practices that were anti-patterns are becoming normalized and codified in a new executive hiring playbook.

Insight’s Onsite executive talent team has supported more than 400 executive searches and met over 1,400 candidates over the past year. What we’re seeing in product and engineering is a distinct set of qualities that can predict candidate success as this new leadership playbook evolves, and knowing what these qualities look like will change how you evaluate the people in your most critical roles.

This is the first in a series of articles about changing trends for leadership hiring during this wave of AI transformation.

The expectations have already changed for product and engineering leaders

CTO and CPO roles have changed considerably in the past two years. Engineering teams are shrinking from eight or ten people to five or fewer. Cycle times that used to take weeks now take days or hours. Engineers have shifted from writing code to designing systems that produce code. Operational product management is being automated. What remains, and can’t be automated, is harder: judgment, strategy, and customer insight.

Meanwhile, the cost of building has collapsed. That sounds like good news (and it is), but it also shifts the bottleneck. Commercialization has become the challenge — pricing, packaging, selling, and enterprise adoption are where the leverage lives now.

The leaders who understand these shifts are redesigning their organization’s operating system. Those who don’t are optimizing a model that is already outdated.

Six qualities of leaders meeting this moment

Across hundreds of searches and portfolio engagements, our executive talent team has identified six qualities that consistently show up in leaders who are navigating this moment well:

• Intellectual curiosity
• Willingness to unlearn
• Commercial instinct
• Comfort with ambiguity
• Change-management muscle
• Operational rigor with speed

These are not equally weighted: intellectual curiosity is the bellwether.

You can develop commercial instinct, coach someone through ambiguity, and help a leader build change-management muscle over time. Yet, intellectual curiosity — the drive to get into the tools, build, experiment, and not delegate that exploration to someone else — can’t be manufactured.

When thinking through a candidate’s intellectual curiosity, the signal isn’t whether someone is aware of AI (that’s a given at this point). Instead, ask: Are they experimenting with AI personally? Are they sharing what they’re learning? If you’ve given them the tools, created the conditions, made it safe, and they still aren’t engaging, that’s a mindset signal, not a skill gap. And mindset gaps don’t respond to coaching.

What each quality looks like

Intellectual curiosity shows up as an active behavior. For example, the leader is experimenting and building themselves, not just sponsoring experiments.

Willingness to unlearn is harder to spot because the leaders who lack it rarely know it. Watch for the ones whose response to every new idea is “we tried that” or “that won’t work here.” Ask them directly: When did you last change how your team works? If they can’t answer quickly, you may have your signal.

Commercial instinct is actually the most coachable of the six traits. The exception is the leader who insists it isn’t their job. A CPO who won’t engage in a pricing conversation, or a CTO who doesn’t think about the go-to-market implications of architectural decisions, is operating in the wrong decade.

Comfort with ambiguity sounds like this: “I don’t know yet, but here’s what we’re trying.” Leaders who need a clear answer before they act will be stuck for a while.

Change management muscle means driving adoption top-down while also cultivating it from the bottom up and bringing the team along on the journey. One without the other stalls.

Operational rigor with speed is the tension that the best leaders hold. Experiment constantly, but don’t let experiments bring the business down. Both things must happen at once.

What this means for your team right now

The headlines can tell conflicting stories. Engineering and product roles are in flux — open roles across the market are up 78% since 2023, while software has seen over 120,000 YTD layoffs. Talent is available, but searches are taking longer. Identifying, attracting, and evaluating AI talent is complex. The median time to fill an executive search is 106 days, and AI-ready leaders command a 10 to 15% compensation premium across levels. The market is moving fast, and knowing what you’re looking for before you start a search matters more than it did two years ago.

Once you’ve evaluated your leaders against these qualities, ask yourself: Do you have the right people, or do you need to make a change?

Three questions can help you decide where to focus:

1. Is the intellectual curiosity there? If yes, you can build the rest. If no, that’s your answer.
2. Is this a skill gap or a mindset gap? Skill gaps respond to investment. Mindset gaps don’t.
3. What’s the opportunity cost of waiting six more months to find out?

Changes beyond product and engineering

Product and engineering are where the transformation in AI-era leadership is most pronounced right now, which is why we’re starting here. But AI is reshaping what effective leadership looks like across every function. In articles to follow, we’ll move beyond product and engineering to look at the full exec team.

The playbook isn’t written yet. We’ll keep reporting back as it takes shape. Get the latest by following Insight Partners on X and LinkedIn.