Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
How do you build real trust in AI adoption within HR teams?
Trust in AI adoption isn't built through reassurance alone—it requires acknowledging team members' concerns vulnerably and openly, preserving human judgment and employee agency, and anchoring accountability with leadership. Diana Mouhan emphasizes that bringing leadership together to co-create use cases and develop shared AI principles transforms fear into partnership.
When Seneca Polytechnic began its AI adoption journey in HR, the team faced a common barrier: fear. Yet Diana Mouhan recognized that telling people not to worry about AI replacing their jobs would ring hollow. Instead, she took a fundamentally different approach centered on honesty and inclusion.
The foundation for trust starts with acknowledging real concerns rather than dismissing them. Diana brought her transformation team and HR leadership together not as a top-down mandate, but as partners in shaping how AI would actually work within the organization. This vulnerability—admitting uncertainty and inviting the team to solve the problem together—signaled that leadership wasn't operating from a hidden agenda. They were genuinely exploring the technology alongside their teams.
A critical element was making explicit what would and would not change. As Diana explains in the episode, the team reinforced that human judgment remains with employees and HR specialists—AI would be a tool for enhancement, not replacement. This distinction matters deeply: workers retain final decision-making authority in their domains, and their expertise isn't being automated away.
"Trust isn't just telling people don't be afraid. Trust is actually built on acknowledging the concerns that they have and being super vulnerable in that journey with them."
Diana Mouhan — Executive Director of Transformation and Change at Seneca Polytechnic, a transformation specialist with over a decade of organizational strategy and AI adoption experience, previously at Deloitte and Kearney.
Equally important is clarity about where accountability lives. Leadership owns the responsibility for how AI is deployed, what it optimizes for, and the outcomes it produces. This boundary protects team members from feeling that AI failures or misaligned decisions are somehow their burden to carry. When people see leadership standing behind both the technology and its ethical application, trust deepens significantly.
The practical mechanism for building this trust at Seneca was experimentation and co-creation. Rather than announcing AI solutions, Diana's team ran pilots with HR staff, gathered their input on what worked and what didn't, and revised principles collaboratively. This approach generated buy-in before full rollout—people weren't surprised by technology arriving; they'd helped shape it. You can explore more about how Seneca redesigned its entire transformation strategy when funding constraints emerged, revealing how trust and flexibility work hand-in-hand.
Initial adoption metrics at Seneca reflected the challenge: only 25% to 30% of the HR team initially trusted AI for workspace utilization. Yet by grounding trust-building in vulnerability, transparency, and shared ownership, the team moved past fear toward genuine partnership with the technology.
Key takeaways
- Trust requires acknowledging and validating team concerns openly, not dismissing or minimizing them.
- Human judgment and decision-making authority must remain clearly with employees and specialists, not be absorbed by AI.
- Leadership must own accountability for AI outcomes and ethical deployment, shielding teams from carrying that burden.
- Co-creation and pilot-based experimentation build buy-in and trust before enterprise-wide rollout.
See also
How can an HR organization continue transformation when technology funding disappears unexpectedly?
Rather than pausing the transformation until funding returns with indefinite timing, Diana's team at Seneca Polytechnic redesigned the transformation to work with available tools and resources, shifting focus from technology investment decisions to operational redesign.
What is the adoption curve when introducing AI-driven enablement tools to traditional L&D and business teams?
Greater than 60 percent of people initially fear that AI technology will replace their jobs, but disarming these concerns through messaging that emphasizes human judgment and employee agency significantly improves adoption rates.
How can data integration from multiple organizational systems enable skills inference and personalized enablement at scale?
By consolidating data from sources like Salesforce, product infrastructure, and customer interaction systems into a unified data lakehouse, organizations can derive deeper skills insights and deliver personalized learning at enterprise scale.