What’s Slowing Down AI Adoption?

Daniel Goleman is author of the international best-seller Emotional Intelligence and Optimal: How to Sustain Personal and Organizational Excellence Every Day. He is a regular contributor to Korn Ferry. 

In a recent Fortune roundtable, business leaders gathered to predict what 2026 will look like for tech’s “Magnificent Seven,” the seven most dominant, high-performing tech companies in the US. The conversation centered on which companies will dominate and which AI models will win in the coming year of transformation.

But beneath every optimistic forecast sits a critical, largely unspoken assumption—one that industry experts repeatedly point to, yet organizations consistently underinvest in. This is the assumption that human behavior and culture change can keep pace with where technology is going.

Experts keep saying it: AI adoption isn’t primarily a technology problem, it’s an organizational behavior and culture problem. One consultancy reports that 70% of digital transformations fail due to insufficient aspiration, weak employee engagement, and inadequate support to sustain change. Another finds that 37% of employees don’t use AI not because they lack skill, but because they don’t see their peers using it.

The pattern is consistent and unmistakable: AI can only go as far as humans are willing to take it.

Yet when you examine where organizations actually spend their AI budgets, the imbalance is stark. Most investment flows toward infrastructure, tools, and technical training, while the human dimensions—understanding the psychology of change, examining how AI collides with existing culture, and developing the leadership capabilities required to guide transformation—are treated as afterthoughts.

For the Magnificent Seven to realize their full potential, organizations will need to spend the coming year taking human behavior far more seriously.

Dr. BJ Fogg, founder of Stanford Behavior Design Lab, offers a useful lens. His behavior change model shows that three elements must converge for any behavior to occur: motivation, ability, and a prompt. Most AI initiatives focus heavily on ability and prompts—teaching people how to use new tools and surrounding them with mandates and champions. Far fewer address motivation. In fact, some even create counter-motivation without realizing it.

In Fogg’s model there are three core motivators: sensation (pleasure versus pain), anticipation (hope versus fear), and belonging (acceptance versus rejection). In many organizations, AI activates the fear side of every equation: employees anticipate job loss rather than opportunity; many experience frustration rather than curiosity in the learning curve; and many worry about exclusion— either for moving too fast or for not keeping up at all.

This is where emotional intelligence stops being a “soft skill” and becomes essential infrastructure.

In the AI era, leaders will need to create more and more small islands of coherence: dedicated places where people can surface fears, clarify values, and make intentional choices about how they engage with transformative technology.

Doing this well requires self-awareness to recognize when leaders’ own anxiety about AI is quietly undermining the learning culture they want to build; empathy to understand what adoption actually costs employees psychologically (not just in time and effort, but in identity, security, and sense of purpose); organizational awareness to see the informal networks and hidden dynamics that either accelerate adoption or quietly stall it; and the adaptability to stay present in discomfort long enough for something genuinely new to emerge.

With these capacities, leaders can create environments where hope is grounded in action and where people are supported in navigating the messy middle of change rather than bypassing it.

This may be the hardest part of the work ahead: to stop being wooed by shiny new tools and instead, put more effort towards the people who use them.

These are the quiet variables beneath every prediction about the Magnificent Seven. Their technical capacity is unquestioned. Their access to capital, talent, and intelligence is unmatched. But none of it matters if the motivation required for behavior change at scale isn’t there.

As 2026 begins, the most consequential work leaders can do is to invest as seriously in emotional intelligence, culture, and behavioral change as they have in the technology itself. 

Co-written by Elizabeth Solomon

Click here to learn more about Daniel Goleman's Building Blocks of Emotional Intelligence.