Short Reads

Michael Vermillion of J.D. Power recently asked me a few big questions about AI and the FUTURE OF WORK on their podcast. Tune in wherever you listen to podcasts: https://hubs.la/Q03rBk_r0

If you have time to listen, I’d welcome your feedback – please DM me.

But if you don’t, here are five key takeaways, courtesy of my P.A. (ChatGPT). (Not bad, I must say)

✅ Responsible AI adoption starts with people. It’s not just a tech project. Executing AI well means rethinking roles, accountability, and trust—not just hiring more data scientists.

✅ Execution gaps remain real. Many leaders still underestimate the organizational shifts needed to scale AI responsibly. Missteps often stem from knowledge gaps, unclear goals, and ignoring risks.

✅ Human + AI is the winning team. AI excels at speed and scale; humans bring judgment, context, and empathy. Roles will shift—not vanish.

✅ Upskilling can’t be an HR checkbox. It has to be business-led. Give people room to apply what they learn, and manage skills like strategic assets.

✅ What does good looks like? Behavior change, sharper questions, new roles emerging, and cross-functional collaboration—not just completed courses.

My biggest message to leaders?
🔑 Be intentional. Be clear on why you’re adopting AI and how it supports your business and values. And above all—build trust. It’s the real competitive advantage in this AI era.

Watch the first 10 minutes to appreciate the key shifts in computing: from Software 1.0 (code for computers), to Software 2.0 (weights for neural nets), to Software 3.0 (prompts for LLMs—potentially becoming the new OS).
Important takeaway: each version remains useful depending on the goal.
Many thanks to David sharing this valuable resource!

When I embarked on my AI journey, Deep Thinking by Garry Kasparov influenced me deeply on human+machine collaboration. It was a real delight to hear him live at the Insurtech Insights conference this week.

He encouraged us to embrace AI as tools to augment and extend human creativity with machines' speed and precision, while also reminding us of our responsibility in choosing future directions. His personal choice to re-engage and help evolve AI is a real-world example that human-machine partnership is not just conceptual, but the way forward.

My other key takeaways:
🔶AI is increasingly used to explore legacy codes and create pathways to modernize legacy systems – a critical step in leapfrogging.

🔶Insurance is a data-driven business but somehow lost its way. AI presents an opportunity to effectively map, integrate, and better utilize data.

🔶Insurtech–incumbent partnerships expedite innovation and upskill internal teams. The most successful collaborations start with modest goals and produce surprises over time, with flexible KPIs that adjust with pacing and rapid tech advances.

🔶Invest in design upfront, not change management to salvage poor design.

🔶Insurtech and enterprise AI are attracting talents to insurance as a purpose-driven industry as never before. Jobs, however, will look different in 4 years, and most valuable workers will be systemic thinkers who see problems to solve.

🔶Smart regulations expand business with clarity and certainly, rather than limit business growth.

🔶Insurtechs have learned not to disrupt but to modernize insurance, which is not a winner-take-all industry. Alliances matter in moving the industry forward.

🔶Agentic AI is the right direction despite the hype. AI agents (e.g., compliance agents) auditing other AI agents (e.g., underwriting agents) will be a natural evolution.

Invaluable insights from Masashi Namatame, Robert Pick, Gary Hoberman, Bill Martin, Amy Nelsen, Damion Walker, Richard L. McCathronand many others. I left inspired to keep playing my small part in advancing the purposeful and responsible adoption of AI in insurance.