Biggest Takeaways from the Insurance Innovators Summit & 2 AI Tracks
This year’s Insurance Innovators Summit in London made one thing abundantly clear: the conversation around AI in insurance has shifted. The industry, including the many participants we spoke with at the event, has moved past experimentation and isolated proofs of concept. The focus now is on execution and applying AI in ways that are scalable, explainable, and demonstrably valuable.
Sapiens' team at the Insurance Innovators Summit
Across the Summit and in our roundtable discussion on AI and the future of underwriting, leaders emphasised that meaningful progress depends less on dramatic breakthroughs and more on thoughtful integration, trusted data, and human-guided intelligence.
After listening, discussing, and comparing experiences with insurers at different stages of the journey, four themes stood out as the most important takeaways. Would it surprise you that one of them explains how Gen AI isn’t currently the most valuable use case for insurers?
1. Insurers Are Advancing on 2 Complementary Tracks
We’re seeing AI mature along two different vectors. Embedded AI is becoming part of the underlying decision fabric and is guiding pricing, triage, and risk assessment with greater transparency and standardisation. At the same time approaches such as copilot AI are enhancing cognitive capacity: it reads, summarises, and prepares information soexperts and personnel can focus on the high-value, strategic parts of their roles.
Crucially, these approaches aren’t about replacing human expertise, they’re about enabling it to operate at greater scale, clarity, and speed.
2. AI Can’t Save You from Poor Data Integration
A recurring message across conversations: AI will not fix poor data or a lack of integration, it will amplify it.
The insurers gaining the most value are those who:
- Connect and standardise data across underwriting, policy, claims, and risk
- Define where and when AI should support decisions
- Maintain transparency and the ability to override automated recommendations
This is as much about an operating model and governance, as it is about technology.
3. The Highest-Value Use Cases Are…Not Gen AI?
While generative AI draws attention, the strongest returns today are coming from high-confidence, deterministic applications, such as:
- Automated data extraction and enrichment
- Underwriting pre-analysis and prioritisation
- Claims fraud pattern detection
- Pricing and market calibration insights
These applications are already delivering measurable improvements in efficiency, risk selection, and consistency without destabilising workflows.
4. Human Expertise Remains Central – AI Should Strengthen It
Underwriters and claims professionals continue to be the differentiators. What they want from AI is less manual/repetitive work:
- More time for evaluation and negotiation
- Less manual searching, reading, and rekeying
- Clear reasoning they can interrogate and trust
AI adoption accelerates when it supports judgment, rather than tries to automate it.
What’s Next?
The next stage of AI in insurance will be defined not by technology itself, but by how effectively we embed it into workflows, strengthen data foundations, and empower experts with clearer insights. Our recent whitepaper and blog series on agentic AI and cloud modernisation show you what’s up next.
Sapiens is working with insurers to modernise underwriting and claims processes in ways that improve decision accuracy, reduce friction, and amplify human expertise.
If you’d like to continue the discussion or compare where your organisation sits on the maturity curve, I’d welcome the conversation.
