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Compliance is important, but it is not the same thing as trust.
In this episode of Trust Issues, Bruno Lecoq and Jeremy Patterson speak with Arvita Tripati about what happens when AI moves from a controlled environment into real-world workflows. Arvita explains why organizations need to look beyond written regulations and consider customer expectations, human impact, business context, and the actual behavior of AI systems.
The conversation covers the tension between moving quickly and building mature safeguards, why organizations should make room for responsible experimentation, and why AI governance needs clear ownership rather than being divided between teams.
What You’ll Learn:
- Why regulatory compliance and customer expectations are not always the same thing
- Why AI governance has to account for the human impact of technology
- How organizations can balance grassroots AI experimentation with formal governance
- Why different AI use cases require different risk assessments
- Why AI decisions and assumptions need to be continuously reevaluated
- How clear ownership and escalation pathways make AI governance more effective
- How organizations can detect when governance processes exist on paper but fail in practice
- Why AI systems need clear accountability, monitoring, and consequences
Episode chapters:
00:26 — Welcome to Trust Issues
01:51 — When Technology Creates Real Human Impact
03:59 — Balancing AI Risk and Potential Benefit
04:48 — Why Compliance Is Not a Checkbox
08:00 — When Should You Invest in Security?
09:37 — Why AI Governance Requires Constant Reevaluation
10:50 — Why Visibility Matters in AI Adoption
12:09 — A Two-Lane Approach to AI Adoption
14:11 — Getting Leadership Behind Responsible AI
17:00 — Why AI Use Cases Need Different Risk Profiles
20:55 — What Boards Need to Know About AI
22:03 — Why AI Accountability and Escalation Matter
25:02 — How to Know if AI Governance Is Working
26:32 — Who Owns AI Risk?
29:21 — Why AI Managers Need Clear Accountability
30:14 — Why AI Managers Need Training
31:09 — Why AI Governance Can't Wait for a Crisis
32:13 — What Trustworthy AI Leadership Looks Like
34:32 — Key Takeaways
Quotes:
- “Your product needs that balance, how do you make sure it is trustworthy enough but not overbuild it?”
- “How much risk is acceptable? How much benefit do you need to see?”
- “Good governance with AI requires you to constantly be reevaluating your assumptions.”
- “The escalation pathway is only as good as if somebody uses it.”
Connect with the team:
👉 Arvita Tripati on LinkedIn: https://www.linkedin.com/in/atripati/
👉 Bruno Lecoq on LinkedIn: https://www.linkedin.com/in/brunolecoq/
👉 Jeremy Patterson on LinkedIn: linkedin.com/in/jeremypatterson2026
👉 BEMO Website: https://www.bemopro.com/
👉 Arvita Tripati on LinkedIn: https://www.linkedin.com/in/atripati/
👉 Bruno Lecoq on LinkedIn: https://www.linkedin.com/in/brunolecoq/
👉 Jeremy Patterson on LinkedIn: linkedin.com/in/jeremypatterson2026
👉 BEMO Website: https://www.bemopro.com/
Trust Issues is handcrafted by our friends over at: fame.so
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