Teams With High-Expectation, High-Empathy Leaders Hit Top Engagement at 3x Rate

Teams led by managers who both expect a lot and care a lot are three times more likely to reach the highest level of engagement — what FranklinCovey Institute research calls "creative excitement" — and six times more likely to produce exceptional results. Those two figures anchor a new leadership argument about why enterprise AI initiatives stall: organizations race to adopt the technology while employees are still working out what it means for their jobs.

The essay, framed around "human factors" in AI adoption, identifies a structural gap in how employers sequence transformation. Once organizations decide to embrace AI — whether for expense reduction or, less often, transformation — they move quickly to execution questions: platforms, token costs, guardrails and training. What they lose sight of, the author argues, is that people must eventually use the new technology in a purposeful way, and that leaders cannot ask employees to fundamentally change how they work while continuing to lead as they always have.

The core question the piece puts to executives is direct: "Are we willing to change how we lead, or are we expecting everyone else to do it?"

What does the research say about leadership behavior?

The FranklinCovey findings suggest the combination of high expectations and high care is measurable in outcomes. A team three times more likely to reach peak engagement, and six times more likely to deliver exceptional results, represents a compounding advantage over teams led by managers who prioritize only one of the two.

The essay positions empathy and action as jointly necessary. Empathy means recognizing that people sit at different points in AI adoption, understanding what they fear losing, and acknowledging those feelings. It explicitly does not mean avoiding the hard work of change. As the author writes: "Caring about people and expecting progress are not opposites."

Leaders must also process their own response to the transition first. Naming their own feelings, the argument goes, better positions managers to lead with the empathy workers need.

Which four behaviors should leaders model?

The piece calls leaders who make participation in change inspiring "positive disruptors" — those who treat disruption as fertile ground for reinvention and build its risks into their vision. Four modeled behaviors stand out:

• Curiosity. No leader knows exactly where AI is heading. Pretending to have all the answers may protect a leader's image but does little to build trust. Asking questions and listening builds more.
• Capability. Adoption starts with applying new tools to existing processes and staying open to radically transforming that work. Beyond training and coaching, it requires learning on the fly, experimentation and cross-team collaboration — and leaders are not exempt.
• Confidence. Capability builds confidence as people experience small wins first-hand. Leaders encourage experimentation, direct it toward consequential opportunities and talk openly about results.
• Consistency. Adoption takes hold when people keep asking questions, keep using tools and keep sharing learnings, with leaders carrying those learnings forward.

How does purpose counter employee fear?

The essay treats mission articulation as a direct counterweight to the loss and fear employees may feel facing a technology it describes as "disorienting and potentially dehumanizing" as AI. Setting ambitious goals — clarifying what the organization is trying to make possible, not just faster or cheaper — replaces uncertainty with clarity.

The psychological mechanics are spelled out: showing individuals how their work fits in reinforces autonomy and status; creating conditions for shared learning strengthens relatedness; transparent decision-making supports a sense of fairness. The same drivers that make change feel threatening can make participation in that change feel inspiring, the author notes.

Can adoption run bottom-up as well as top-down?

The essay's closing argument is that adoption and transformation can happen in both directions. As innovation bubbles up, more team members feel a personal stake in successful AI adoption and advance further. As leaders model curiosity and capability, the workforce returns that trust — a "spiral upward" in which adoption generates its own momentum and employees themselves become leaders in shaping the organization's future.

The final question leaders are left with: "Are we creating the conditions for our people to contribute, or are we simply expecting them to comply?" For HR teams, the actionable implication is that leadership development — not tool selection — may be the gating factor in whether AI investments translate into changed work.