Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
How do you measure HR team readiness for AI adoption?
Start by listening to where each person actually stands on their AI journey—without assumptions. Only 25 to 30 percent of Seneca's HR team initially trusted AI enough to use it in the workspace, a baseline that revealed the true readiness gap and enabled the team to build meaningful shifts in confidence.
The assessment Diana Mouhan's team at Seneca Polytechnic employed was fundamentally human-centered. Rather than deploying surveys or formal readiness audits, they spent time in genuine conversation with each team member to understand their starting point.
This listening approach uncovered a fragmented landscape: some employees didn't yet know what a prompt was, while others were already experimenting independently with AI tools. Mapping this spectrum proved essential, because it showed the team that readiness wasn't binary—it was a continuum that demanded different interventions for different groups.
Knowing that trust was the foundation, Diana's team recognized that the 25–30% figure became their starting point for designing messaging, training, and peer-led adoption pathways. The baseline revealed which skeptics needed reassurance and which early adopters could become internal champions.
"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. Diana brings over a decade of organizational strategy and AI adoption expertise, with prior leadership roles at Deloitte and Kearney. Her work focuses on redesigning HR operations to embrace technology while maintaining human-centered change management.
The specificity of this finding—not just "we measured trust," but "we discovered exactly what percentage trusted us and built from there"—reveals why listening as an assessment tool outperforms formal readiness models. Diana's approach at Seneca shows how vulnerability and acknowledgment anchor adoption faster than mandates or cheerleading alone.
As the team rolled out AI-driven tools like ServiceNow and knowledge-management platforms, this readiness baseline informed every design choice—from how they framed AI's role to which features they prioritized. The result wasn't instant universal adoption, but rather a sustainable rhythm in which skeptics gradually moved toward experimentation once their real concerns were named and addressed.
From baseline trust to measurable behavioral shift
The 25–30% baseline wasn't a ceiling; it was a launchpad. Once identified, Seneca's team redirected their entire change strategy to close that gap systematically. Rather than assuming everyone was equally ready, they could now segment communication, training, and peer support according to where each cohort actually stood.
This granular readiness picture also revealed allies: the 25–30% who already trusted AI became mentors and co-creators of the transformation journey. Their confidence became visible to hesitant colleagues, accelerating social proof far more than any leader's mandate could.
See also
What is the foundation for building trust in AI adoption within an HR team?
Diana emphasizes that trust isn't simply telling people not to be afraid of AI. Instead, it requires acknowledging team members' concerns vulnerably and being genuinely present in their adoption journey.
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 itself—shifting focus from technology investments to maximizing the tools already available.
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-AI collaboration and skill enhancement accelerates adoption across the organization.