Market context

Across industries, organizations are racing to integrate artificial intelligence (AI) into their operations, but few would describe themselves as experts. Nearly every company is experimenting, often through multiple pilot programs or proofs of concept (POC), yet most still consider themselves in early implementation phases. The prevailing sentiment is one of collective learning: progress is real, but maturity remains elusive. In some cases, the drive to move quickly has led to setbacks, including litigation and compliance risks that have made many leaders more cautious about scaling AI too aggressively.

Governance models are evolving. Most organizations manage AI through centralized or hybrid structures where a central body defines guardrails, though regional differences persist. US companies tend to favor hybrid models, while European organizations lean more toward business-unit-led approaches, reflecting their more conservative regulatory environments and lower appetite for rapid upskilling. Data quality, security, and compliance continue to be the most consistent barriers to scale, underscoring that even as AI reshapes business priorities, few organizations have yet figured out how to operationalize it sustainably.

Leadership and compensation structures around AI are evolving unevenly. Many companies have rebranded existing executive roles to include AI oversight, which blurs the definition of true AI leadership and pushes compensation benchmarks upward faster than role clarity or technical scope. At the workforce level, demand is shifting toward change management and cloud engineering, while once-critical skills like prompt engineering are already declining. Notably, research and technical AI skills are now harder to find than strategic ones, suggesting that while the technology is advancing rapidly, organizations are making steady progress embedding AI into their ways of working, resulting in a fast-moving talent market where clarity, skills, and pay remain out of sync.

AI observations 

AI adoption is now widespread, with nearly all organizations actively engaged, but most remaining in an exploratory phase. While governance structures are often in place, ownership models and execution approaches continue to evolve. Organizations are experimenting rapidly and moving quickly from proof of concept to early deployment; however, full operationalization remains limited.

Overall, the landscape reflects strong momentum and broad participation, alongside continued uncertainty as organizations test, learn, and refine how AI can be scaled for sustained business impact. 

This experimentation is already reshaping the workforce. Organizations are investing heavily in upskilling employees while simultaneously reconfiguring roles. Nearly half of organizations have added new AI leadership roles while reducing headcount in automatable jobs, creating a polarized talent landscape marked by strategic investment at the top and efficiency-driven reductions below. For leaders, the challenge is no longer whether to adopt AI, but how to shape the workforce and operating model before experimentation hardens into structure.

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About the author

Ryan Bulkoski (rbulkoski@heidrick.com)  is the global head of the Artificial Intelligence, Data & Analytics Practice; he is based in the San Francisco office.