Closing the AI Trust Gap

When AI Strategy Outpaces AI Readiness

Author

Andrea Schnepf
August 20, 2025

AI is now a standing agenda item in every boardroom — and while 75% of executives say their rollout is a success, only 45% of employees agree.1 That disconnect isn’t a side issue: in AI adoption, perception drives performance.

Most organizations aren’t falling behind because of poor technology — they’re falling short because the human dynamics required to make AI work are missing. 

The competitive risk is real: the organizations that close the AI trust gap will move faster, learn faster, and win markets.

Key Takeaways

  • AI adoption is a cultural shift, not a software implementation. Treating AI as a one-off deployment ignores the deep behavior shifts required to make it work. It must be embedded into the fabric of how work gets done, not just layered on top.

  • Fear is the hidden blocker. Job-loss narratives — whether accurate or not — can outweigh any “innovation” message from the C-suite. Left unaddressed, these stories spread faster than any AI adoption plan.

  • Outcome metrics beat usage metrics. Don’t track “hours spent in AI tools” as a proxy for success. Instead, track problems solved, accuracy improved, and efficiency gained.

The Trust Gap in AI Programs

Unlike most enterprise technology rollouts, AI adoption is highly visible to employees — in the news, personal apps, and even in the subtext of layoff announcements. This visibility fuels curiosity and anxiety.

Our work with transformation leaders shows four recurring missteps that widen the trust gap:

  1. Vision without translation. When executives fail to create a compelling vision that addresses fears and aligns with workforce concerns, AI adoption falters. Without linking strategy to day-to-day reality, employees are left wondering what AI means for them — and that uncertainty slows momentum.

  2. Low early engagement. Pilots are designed in the executive suite, not co-created with the people who will use the tools. When employees aren’t involved from the start, they see AI as something being done to them, rather than a capability they’re helping to shape.

  3. ‘Go live’ before readiness. Deploying AI without policies, training, or alignment leads to fragmented adoption, wasted investment, and a perception that AI is “just another initiative” that will fade.

  4. Training divorced from work. Knowledge sessions with no role-specific application leave employees informed but unprepared. Without hands-on opportunities to experiment in the context of their actual workflows, the gap between “knowing” and “doing” doesn’t close.

Closing the Gap: A People-First AI Playbook

The leaders who convert AI investment into measurable advantage do five things differently:

  1. Make the case in human terms. Tie AI to removing pain points, improving customer outcomes, and creating career-relevant skills — not just operational efficiency.  When employees see AI as a way to make their work easier, more impactful, or more future-proof, they engage enthusiastically rather than defensively. Shift AI from a “corporate initiative” to a personal and professional asset.

  2. Co-design adoption. Involve employees in shaping where AI fits, then spotlight their early wins. Co-creation builds ownership, and celebrating those successes shows that AI isn’t abstract — it’s driving real results.

  3. Equip managers as AI guides. Middle managers bridge strategy and execution. They need more than a briefing — they need hands-on experience, talking points for team discussions, and space to experiment themselves.

  4. Integrate into culture and talent systems. Recognize and reward collaboration, curiosity, and experimentation alongside technical achievement. Embedding these behaviors into performance reviews, recognition programs, and learning pathways signals that AI capability is core to how the organization grows and competes.

  5. Measure what matters. Don’t measure hours of AI usage — measure the problems solved, the accuracy improved, and the efficiency gained. When employees see their contributions recognized through meaningful metrics, it strengthens the link between their work and enterprise success.

If you’re accountable for delivering AI results in a complex organization, now is the time to pressure-test your adoption strategy.

We’ll unpack the disconnect between executive intent and workforce readiness, the cultural and behavioral barriers derailing progress, and explore practical strategies for driving adoption and translating AI initiatives into real business outcomes.

Hope to see you there!

Andrea

Citations