News

June 2026

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

The AI Leadership Gap Nobody Is Talking About

Companies are moving quickly to appoint AI leaders, but many are discovering the market is far more complex than the headlines suggest. Boards want credible AI strategies, investors want evidence of progress, and customers increasingly expect AI-enabled products and services. Yet the real challenge is not finding people who can talk about AI. It is finding leaders who have already built, scaled, commercialized, and operationalized it.

The market is not short on executives who understand AI. It is short on proven AI operators - leaders who have taken AI from experimentation to production, governance, adoption, and measurable business impact.

The Experience Inflation Problem

Over the last two years, AI has become one of the most frequently claimed capabilities on executive profiles. Many leaders have sponsored AI pilots, attended strategy sessions, or experimented with generative AI tools. But when organizations begin hiring for roles such as Chief AI Officer, Head of AI, VP of AI Products, AI Platform Leader, or AI Transformation Executive, the bar rises quickly.

The mandate in these roles is rarely to explore AI. It is to turn AI into products, platforms, operating capabilities, and measurable business results. That requires leaders who can move beyond vision into execution - and that is where the talent pool becomes much smaller.

What Companies Actually Need

The strongest AI leaders are not simply experts in machine learning or large language models. They are enterprise operators who have led the full journey from concept to scale. They know how to move from pilot to production, put governance and risk controls in place, drive adoption across the business, and connect AI investment to commercial outcomes.

They are leaders who have successfully navigated the entire journey from concept to scale.

They have:

• Built AI-powered products that reached production
• Scaled AI capabilities across multiple business units
• Led engineering, product, and data teams through implementation
• Established governance, security, and risk frameworks
• Connected AI initiatives to measurable business outcomes
• Managed organizational change alongside technical transformation

In other words, they have already solved many of the problems organizations are just beginning to encounter.

That distinction matters because the market is still far earlier in enterprise AI execution than the volume of AI rhetoric suggests. McKinsey reports that while AI use is now widespread, nearly two-thirds of organizations still have not begun scaling AI across the enterprise, and only 39% report EBIT impact at the enterprise level. In other words, interest is abundant; scaled impact is not.

Why Experience Matters More Than Ever

One of the biggest misconceptions in today’s market is that AI exposure and AI leadership are interchangeable. They are not. Many executives have participated in strategy discussions, sponsored pilots, or implemented AI-enabled tools within their functions. But building AI into a product, platform, or service at scale requires a different level of judgment and operating experience.

The leaders organizations are seeking today have often navigated challenges around:

• Data readiness and governance
• Productization of AI capabilities
• Infrastructure and compute requirements
• Security and risk management
• Organizational adoption and change management
• Commercialization and revenue impact

These are not capabilities that can be inferred from theory, certifications, or proximity to AI programs. They are learned through execution - through making trade-offs, solving deployment problems, managing risk, and delivering results under real organizational constraints. That is why proven experience is becoming disproportionately valuable as companies move beyond experimentation.
And they are becoming increasingly valuable as organizations move beyond experimentation.

Why Traditional Recruiting Approaches Are Struggling

This is also why traditional recruiting approaches are struggling. AI leadership searches often begin with a job description and quickly produce a large volume of profiles that reference AI. On paper, many candidates appear similar. In practice, they are not.

One executive may have introduced AI tools into an existing function. Another may have sponsored a pilot. A third may have built and scaled AI-powered products serving millions of users while managing infrastructure, governance, adoption, and commercialization. Those are fundamentally different levels of experience, even if all three profiles use similar language.

The challenge is further complicated by the rapid growth of AI-related credentials appearing across the market. As AI has become a board-level priority, candidate profiles increasingly reference AI initiatives, certifications, and strategic exposure. Yet organizations often discover that actual experience leading AI products from concept to commercialization is considerably less common than the volume of AI-branded talent profiles might suggest.
This is one reason organizations are increasingly shifting these searches toward retained executive search partners.

Not because the talent is impossible to find.
Because the assessment process has become significantly more complex.

Why Retained Search Is Becoming More Relevant

The best AI leaders are rarely active job seekers. Most are already leading critical initiatives inside hyperscalers, enterprise software companies, digital platforms, AI-native startups, or organizations that have successfully embedded AI into products and operations.

Many are highly compensated. Many are being approached regularly.
And many are evaluating opportunities based on mandate, leadership alignment, and organizational readiness rather than compensation alone.
As a result, finding these leaders often requires a different approach.

Retained search allows organizations to:

• Access passive AI leadership talent not actively in the market
• Conduct deeper assessments around execution experience
• Validate actual AI outcomes rather than theoretical knowledge
• Evaluate leadership capability alongside technical expertise
• Benchmark talent against competitors and adjacent industries
• Ensure alignment between the role, the mandate, and the organization’s objectives

That is one reason retained search is becoming more relevant in these mandates. The issue is not simply access to talent; it is accurate assessment. Research from IBM and Gartner reinforces the point. IBM highlights a persistent AI skills gap, while Gartner reports that high-maturity organizations are far more likely to sustain AI initiatives over time and that 91% of leaders in those organizations have already appointed dedicated AI leaders. The implication is clear: this is not simply a technology hire. It is a business transformation hire.

The Profile Is Evolving

Perhaps the biggest shift is that AI leadership is becoming less about technical specialization alone and more about enterprise leadership. The strongest leaders can move comfortably between technology and business strategy, product development and commercialization, data architecture and customer outcomes, and innovation and operational execution. They understand how to build AI, but they also understand how to scale organizations around it.

• Technology and business strategy
• Data architecture and customer outcomes
• Product development and commercialization
• Innovation and operational execution

The Real Risk

The real risk is not hiring someone without AI exposure. It is hiring someone whose experience is mostly theoretical when the mandate requires execution. As AI investment rises, the differentiator is no longer whether a leader understands what AI can do. It is whether they know how to build it, govern it, operationalize it, and scale it across the enterprise.

That capability is usually built through direct operating experience rather than education, certifications, or proximity to AI programs. Companies that can distinguish between AI fluency and AI execution will make materially better leadership hires than those still treating them as the same thing.

Final Thought

The conversation around AI talent often focuses on scarcity, but the deeper issue is precision. There is no shortage of executives who can speak credibly about AI. There is a shortage of leaders who have already delivered it at scale.

As organizations move from AI experimentation to enterprise execution, the competitive advantage will come from identifying proven AI operators - leaders who have already translated technical possibility into operational and commercial results.