The higher you climb, the more disconnected you become—and that disconnection is fueling the gap between AI’s potential and its actual impact.
New research BCG and Columbia Business School shared at an event last week reveals a troubling pattern: executive leaders are 51 percentage points more likely than individual contributors to think employees are well-informed about AI strategy (80% vs 29%). They’re 45 points more optimistic about employee enthusiasm (76% vs 31%). Those gaps create real challenges—especially with AI adoption, where fear competes with opportunity.
Organizations are seeing wide variation in adoption, and technology companies and engineering functions are often heavier adopters, but by no means universally. The same research showed the main driver isn’t industry or function: employee centricity explained 36% of variance in AI maturity—more than industry (14%), department (12%), or company size (5%).
This isn’t soft-skills sentiment, it’s hard business reality. Companies that listen to feedback, provide clear advancement paths, and hold people accountable to outcomes—not activity—don’t just create better workplaces. They create the trust and safety required for people to embrace transformative change.
If your organization struggles with trust or engagement, your AI transformation will struggle too.
Why employee centricity drives AI success
AI adoption requires employees to fundamentally rethink how they work. They need to experiment with new tools, admit when they’re struggling, collaborate across boundaries, and continuously learn. None of that happens in low-trust environments where people feel expendable.
The data bear this out. Compared to organizations with low employee centricity, employee-centric organizations have people who are:
- 70% more likely to feel enthusiastic about AI adoption
- 92% more likely to feel well-informed about AI strategy
- 57% more likely to rate their organization’s speed of technology adoption faster than competitors
As MIT’s Zeynep Ton explained at the conference publicizing the research, “The status quo mindset in leaders is to see labor as a cost to be minimized. Exemplary companies think of employees as drivers of customer satisfaction, profitability and growth.” When you view employees as assets to invest in rather than costs to minimize, they become your competitive advantage in technological transformation.
The flexibility-AI connection
This dynamic plays out clearly in workplace flexibility. Dan Spaulding from Zillow noted that its distributed teams and flexible work model directly contribute to higher AI adoption rates. More engaged employees are more willing to embrace new technologies.
Organizations clinging to the belief that forcing people back to offices will magically improve collaboration are missing the point. As CBRE’s Annie Dean observed, “AI can’t pick up insights and context from watercooler conversations.”
What drives successful AI adoption—and successful flexible work—is intentional design. Clear goals that align teams around outcomes. Sufficient context so people understand the “why” behind changes. Trust built through consistent actions, not mandates.
The organizations succeeding at both flexibility and AI adoption aren’t relying on proximity or surveillance. They’re investing in the leadership practices that create psychological safety, encourage experimentation, and build genuine engagement.
The real barriers to AI adoption
Organizations approach AI adoption as a technology problem. They buy tools, mandate usage, and wonder why adoption stalls. But the emotional barriers rooted in organizational culture are what’s actually blocking progress.
One event attendee told me his CEO is taking a fear-based approach: raise your productivity, or you might get laid off. That’s taking a toll on morale, enthusiasm, and adoption.
Anne Arlinghaus from KKR captured the opposite approach: “Leaders who score higher on empathy have teams with higher performance. Not just basic needs met and a stake in the outcome, it’s do people care about me and is my work valued?”
When people don’t feel valued, they won’t take the risks that AI adoption requires. They’ll quietly resist, do enough to keep their jobs, generate voluminous output (sometimes “AI slop”), or simply leave for organizations that treat them better.
What employee centricity actually looks like
Employee centricity is about fundamental operating principles that build trust and psychological safety. The BCG and Columbia research framework focused on three core areas:
Feedback systems and responsiveness: Do you listen to employees and act on what you hear? Disney’s Dave Lindbom asked the obvious question: “We look at Disney parks user feedback daily, the Nielsen TV ratings overnight, but we listen to employees once a year?”
Respect and support: Do people feel valued and supported professionally and personally? Zapier proved that customer support teams will adopt new AI tools if they feel supported.
Career mobility and rewards: Do you provide a path for growth? Mastercard’s talent marketplace connected employees to stretch projects beyond their roles—one million hours of skill-building that also delivered business value..
Most importantly, it requires leaders to roll up their sleeves and understand reality at the level of individual employees. Scott Salmirs, CEO of ABM Industries, described what building this culture requires: “The job is a contact sport—I have to be out in the field.”
Three actions for leaders
First, understand employees’ real sentiment around AI. The research shows executives consistently overestimate enthusiasm and understanding. Before pursuing more AI-driven productivity, don’t just measure how employees are feeling—find ways to have real conversations about how they’re feeling and what they’re seeing.
Second, stop talking about efficiency and start talking about goals. As MIT’s Ton advised, “What are those outcome metrics we need to improve? Let’s move from focusing on initiatives to outcomes.” If you can’t measure what good AI adoption looks like in business results, you can’t manage the transformation.
Third, practice intentional design in both flexibility and AI adoption. The same leadership muscle that makes flexible work successful—setting clear goals, providing context, building trust through actions—is exactly what drives AI adoption. Any significant change requires an environment where people feel safe enough to experiment, empowered enough to solve problems, and engaged enough to continuously learn.
The organizations winning at AI aren’t the ones with the biggest technology budgets or brashest demands. They’re the ones that built employee-centric cultures first—creating the trust and psychological safety that allows people to embrace real change. That’s the competitive advantage in an AI world.