The AI talent wars explained: What CIOs need to know

What are the AI talent wars?

The AI talent wars refer to the intense competition among organizations for experienced AI professionals. The surge in demand has been fueled by the growing strategic importance of AI across industries, combined with a relatively small pool of highly skilled practitioners.

In practice, the competition shows up in multiple ways, from extended hiring timelines to higher compensation packages for top candidates.

"At the executive level, the AI talent wars are reflected most clearly in compensation and retention structures," said Ryan Bulkoski, partner and global head of the AI and data practice at executive search firm Heidrick & Struggles.

According to the firm's most recent compensation survey, AI officers in the U.S. earned an average of $380,000 in total cash compensation in 2025, with many also receiving substantial equity or long-term incentives.

"These structures suggest companies are treating AI leaders as mission-critical hires while using long-term incentives to compete in a tight executive talent market," Bulkoski said.

In some technical niches, the supply problem is even more pronounced.

"In areas like robotics or specialized AI infrastructure, the pool of experienced experts is extremely small," said Steve Brotman, founder and managing partner at venture firm Alpha Partners. "You're talking about people who earned doctorates a decade ago. There simply aren't that many of them."

Inside enterprises, that scarcity translates into longer hiring cycles and rising salaries for experienced AI professionals, said Sean Safieh, CIO at Sedgwick.

With only a limited number of qualified candidates available, some organizations struggle to build AI teams as quickly as planned. That lag can introduce delivery risks when projects depend on specialized expertise.

How the AI shortage differs from other IT skills gaps

Technology leaders have faced talent shortages before, from the cloud computing boom to the rapid expansion of cybersecurity roles. But many experts say the AI skills shortage is fundamentally different.

For example, many organizations still struggle to find professionals with advanced AI skills, particularly in emerging areas such as agentic AI frameworks and large-scale model development, Bulksoki said.

Unlike earlier technology waves, where job roles were relatively well-defined, AI work often requires professionals who can bridge multiple disciplines, including data science, ML, generative AI (GenAI) and business integration.

Additionally, the scale of AI disruption differs across industries.

"In earlier tech cycles, companies were competing for people with internet or cloud experience," Brotman said. "Today, AI is affecting almost every sector of the economy at once, which means the competition for talent is much broader."

The lack of mature training pipelines further complicates the problem. While universities have expanded computer science programs, the rapid pace of AI development means educational institutions are often still catching up with industry needs.

What's driving the AI demand?

Several forces are pushing organizations to accelerate AI adoption, which in turn spurs demand for AI talent.

These are the following:

• GenAI adoption across business functions. Organizations are embedding tools, such as GenAI assistants, into workflows across customer service and software development.
• Executive mandates to adopt AI. Many corporate leaders are pressuring IT teams to identify practical AI use cases and deliver results quickly.
• Data modernization initiatives. Companies investing in modern data platforms often pursue AI initiatives in parallel, increasing demand for specialized skills.
• Automation and efficiency pressures. Organizations look to AI to automate processes and reduce operational costs.
• Regulatory and governance requirements. As AI becomes more widely deployed, enterprises must also address issues such as security, compliance and model reliability.

"The move from experimentation to real-world deployment of AI technologies across industries is the number one reason we've seen such a spike in hiring," Safieh said.

Roles such as senior AI researchers, ML engineers and AI architects remain some of the most difficult positions to fill.

How to rethink AI talent

As the demand for AI professionals continues to outpace supply, many CIOs are rethinking how they approach workforce planning. One emerging strategy is balancing external hiring with internal capability development.

"A hybrid approach is going to be the best option," Safieh said. "Combining targeted external hiring for specialized AI roles with internal upskilling gives organizations a stable foundation while creating room for continued growth."

Some companies are already shifting toward this model. Instead of hiring large numbers of specialized AI engineers, they recruit fewer experienced experts while training existing developers and analysts to work with AI tools.

For example, at Sedgwick, the company has hired experienced ML engineers while simultaneously expanding internal training programs to teach employees how to use GenAI tools and development assistants, Safieh said.

Additionally, CIOs can focus hiring efforts on platform engineers and integration specialists rather than purely research-focused AI roles. These professionals can help scale AI capabilities across existing systems and workflows.

Centralized expertise is also becoming more common. Some organizations are establishing AI centers of excellence to concentrate expertise and share best practices across departments.

What CIOs should evaluate now

As the AI talent wars continue, CIOs must make strategic decisions about how to build sustainable capabilities.

Technology leaders should assess their current AI skills inventory and identify where expertise exists internally and where gaps remain. They should also determine which AI capabilities are mission-critical versus experimental, which will help them prioritize hiring and training investments.

CIOs should also determine whether the organization has the governance and data foundations necessary to support AI initiatives.

"The broader organization's foundation should be addressed first," Safieh said. "Without proper governance, data maturity and operational readiness, it will impact how effectively AI programs can scale, even when the right workforce is in place."

Finally, CIOs should consider whether retention may pose a greater risk than hiring. As AI talent becomes more valuable, retaining experienced engineers and leaders can be just as important as recruiting new ones. In many cases, building internal capabilities and fostering a culture of AI literacy may prove to be the most sustainable long-term strategy.

Organizations that invest in both talent and infrastructure will be better positioned to compete as AI becomes a permanent part of the enterprise technology landscape.

Christine Campbell is a freelance writer specializing in business and B2B technology.