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Welcome to AI Insurer Brief!
Hey it’s Fabio here,
In today’s Executive Series, we’re joined by Bernard Ruyssen, AXA’s Headquarters Chief Data & AI Officer.
Across the market, insurers are seeing the same pattern: pilots show promise, scaling gets stuck. So we focused on the core question - why AI feels easy in pilots and hard at scale.
Bernard broke down the real blockers, how AXA prioritises initiatives through a clear ROI lens, and what needs to change in the operating model to fully unlock AI’s potential.
This is one not to miss!
Let’s get into it!
Bernard, from your perspective, what’s stopping AI from scaling in Insurance today?
Two issues come up repeatedly: people adoption and data governance.
People: AI only scales when it’s adopted broadly, almost like computers and the internet were. That requires structured onboarding led from the top (executives to managers to teams), paired with strong AI engineering capability to support the business.
Data governance is the second major constraint.
Many AI initiatives work well as pilots and then stall at industrialisation - not because the models fail, but because data ownership, quality, controls, and process discipline weren’t designed to support AI at scale.
When hundreds of users start interacting with AI tools:
prompts might get changed
the wrong documents get uploaded
steps are skipped or processes drift
The outcome is predictable: inconsistent outputs and a rapid loss of trust.
To scale AI, insurers need clear ownership, robust governance, and disciplined process design.
Live feedback: how's this issue so far?
With AI changing business users job so quickly, how can insurance professionals stay relevant?
Think of AI as a super-junior analyst: extremely fast, but still dependent on human judgment from experienced operators.
As AI takes over more “production” work, people’s roles shift toward:
Review
Validation
Business oversight
The bigger challenge is capability-building. Historically, juniors became seniors by doing the basics repeatedly: reading files, validating, reconciling, every day.
If AI now performs much of that foundational work, we need new ways to develop the next generation’s technical and decision-making ability.
That means training professionals to:
Use AI effectively (prompting, checking, improving outputs)
Understand the broader business context (controls, financial implications)
Think strategically (what changes, why it matters, where value is created)
Continuous learning becomes the core skill.
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How do you decide which AI initiatives to invest in, scale, or stop at AXA?
The approach is fundamentally ROI-driven.
We assess processes team by team to identify where the highest costs sit - in time spent, FTE effort, and internal or external spend.
Those processes are then broken down into specific tasks where AI can deliver measurable impact.
Over time, as agentic AI matures, more end-to-end automation will become viable.
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Where do you see AI fitting in Insurance and where would you apply it?
At AXA, we have started working on many areas, like claims management, frauds, pricing, but also customer facing interactions - we realised that in some specific use cases where AI can be more efficient than people, and we can delegate the work to human agents.
In B2B, however, the human relationship remains critical.
More broadly, AI has value potential across global processes and all functions - not only in core insurance operations like underwriting, but also in corporate functions such as HR, legal, accounting.
Many initiatives today focus on improving existing processes. And one of the challenges is to make people rethink business processes because they can be setup in a very different way that they were used to be. The bigger opportunity comes from rethinking processes end-to-end, rather than simply automating what already exists.
When processes are redesigned from scratch with AI in mind, insurers can unlock far greater efficiency and impact.
See you next Wednesday! 👊
Fabio Caravita — say hi on Linkedin
Founder, AI Insurer Brief
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