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Why AI Adoption Stalls When Trust Runs Out
Summary
In this episode of The Human Element, Barb Bidan sits down with Azurée S. Montoute-Lewis, Global Chief People Officer at Burson, to discuss what it actually takes to move AI from pilot programs to organization-wide adoption. Azurée argues that the organizations succeeding at AI adoption aren't moving the fastest. They're the ones who built a foundation of trust before deploying tools, designed metrics around decision quality rather than usage, and drew clear lines between where AI assists human work and where humans must still make the call. With 5,000 employees and 9,000 internal AI agents, Burson is a working example of what that approach produces.
Chapters
00:00 Azurée's role at Burson and what makes it unique
03:10 AI forces an operating model change, not just a technology change
07:30 Why scaling AI breaks down: ownership, data gaps, change fatigue
11:45 Bringing along early adopters, skeptics, and global audiences
14:00 The right way to measure AI impact (not usage)
17:20 When Burson's data came back inconclusive and what they did next
20:15 Agentic AI: from AI that assists to AI that acts
23:50 The feedback coaching agent and where Burson draws the line
27:00 Trust, fear response, and psychological safety in an AI workplace
29:30 Lightning round: one metric, one governance mistake, one line AI won't cross
Takeaways
- Measuring AI adoption by usage is the wrong metric — decision quality and output quality are the signals that matter.
- Organizations that deploy AI without a transparent trust conversation trigger a predictable fear response; building trust first makes adoption easier and more durable.
- The strongest HR AI use cases augment human capability rather than replace human judgment — Burson's feedback coaching agent is a working example.
- Calibration, succession, and leadership assessments should stay with humans; AI can aggregate inputs but cannot supply the nuance of real leadership observation.
- HR's core job in the AI era is designing the human architecture around the technology: the measurement frame, the trust foundation, and the human/AI boundary.
Guest links
Azurée S. Montoute-Lewis on LinkedIn: https://www.linkedin.com/in/azureesmontoutelewis/
Company Website: https://www.bursonglobal.com/
Sponsor
The Human Element is brought to you by Harper, Wisq's always-on AI HR Generalist transforming how work gets done. Powered by deep HR intelligence, Harper delivers instant, accurate, and empathetic support—from policy questions to performance coaching—so your people get answers fast and your teams stay focused.
Learn more at https://www.wisq.com/
View all episodes
By Barb Bidan | CHRO & AI in Human Resources ExpertSummary
In this episode of The Human Element, Barb Bidan sits down with Azurée S. Montoute-Lewis, Global Chief People Officer at Burson, to discuss what it actually takes to move AI from pilot programs to organization-wide adoption. Azurée argues that the organizations succeeding at AI adoption aren't moving the fastest. They're the ones who built a foundation of trust before deploying tools, designed metrics around decision quality rather than usage, and drew clear lines between where AI assists human work and where humans must still make the call. With 5,000 employees and 9,000 internal AI agents, Burson is a working example of what that approach produces.
Chapters
00:00 Azurée's role at Burson and what makes it unique
03:10 AI forces an operating model change, not just a technology change
07:30 Why scaling AI breaks down: ownership, data gaps, change fatigue
11:45 Bringing along early adopters, skeptics, and global audiences
14:00 The right way to measure AI impact (not usage)
17:20 When Burson's data came back inconclusive and what they did next
20:15 Agentic AI: from AI that assists to AI that acts
23:50 The feedback coaching agent and where Burson draws the line
27:00 Trust, fear response, and psychological safety in an AI workplace
29:30 Lightning round: one metric, one governance mistake, one line AI won't cross
Takeaways
- Measuring AI adoption by usage is the wrong metric — decision quality and output quality are the signals that matter.
- Organizations that deploy AI without a transparent trust conversation trigger a predictable fear response; building trust first makes adoption easier and more durable.
- The strongest HR AI use cases augment human capability rather than replace human judgment — Burson's feedback coaching agent is a working example.
- Calibration, succession, and leadership assessments should stay with humans; AI can aggregate inputs but cannot supply the nuance of real leadership observation.
- HR's core job in the AI era is designing the human architecture around the technology: the measurement frame, the trust foundation, and the human/AI boundary.
Guest links
Azurée S. Montoute-Lewis on LinkedIn: https://www.linkedin.com/in/azureesmontoutelewis/
Company Website: https://www.bursonglobal.com/
Sponsor
The Human Element is brought to you by Harper, Wisq's always-on AI HR Generalist transforming how work gets done. Powered by deep HR intelligence, Harper delivers instant, accurate, and empathetic support—from policy questions to performance coaching—so your people get answers fast and your teams stay focused.
Learn more at https://www.wisq.com/