Leadership in AI: Transforming Antibes into a Smart City

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Episode notes

How can AI transform the future of smart cities to create a more efficient, sustainable, and inclusive urban environment for citizens? Antibes, a city on the French Riviera, is leading the way in its AI-driven innovation to enhance public services and operational efficiency.

From automating budget alignment with sustainability goals to running on-premise AI for data privacy, Antibes is redefining digital transformation in the public sector. Tune in as experts explore how technology is driving ethical, citizen-focused innovation across government services.
 

Featured Experts

• Patrick Duverger, CIO and CTO, City of Antibes
• Michael Bradshaw, SVP & Global Practice Leader, Applications, Data and AI, Kyndryl

Conversation highlights

Please note the transcript has been modified for clarity and length.

Tom Rourke (Host): What stands out to me is how intentional your city has been in choosing AI use cases. How aware are citizens of these efforts? Is AI’s role in public services visible and embraced, or is awareness limited?

Patrick Duverger: Our current innovations focus on improving internal city services, but they’re part of a broader digital transformation that will lead to better public-facing outcomes and directly benefit citizens. A key example is video protection, where AI analyzes live footage to detect anomalies like a truck lingering near a school and alerts police when necessary. Since officers can't monitor hundreds of cameras constantly, AI highlights only the screens showing potential risks. This is a real-world case where citizens directly benefit from AI-powered services. (Hear the full response at 13:43) 

Tom Rourke: As you look forward to the possibilities of the application of AI in Antibes, what does progress look like over the next few years?

Patrick Duverger: The acquisition process in France is complex and challenging. We're exploring how AI can support people involved in acquisitions. Next year, we plan to take short, feasible steps, starting with applying AI to streamline tasks. Then, we’ll fine-tune the system based on our domain expertise. We want to have our own AI that can respond to people’s needs. (Hear the full response at 26:25)

Michael Bradshaw: It comes down to enterprise architecture elements—how we assess our customers' current environments and work with them to build their future journey. The challenge is modernizing their architecture without requiring a complete reset before progress can begin. How do we go from where we are to enhancing capabilities and delivering business value along the way? (Hear the full response at 27:21)