Maximising the potential of AI in insurance
Artificial intelligence has moved quickly from “future opportunity” to a day-to-day reality for insurers. But while investment in AI accelerates, the practical experience of implementation is proving more complex.
A recent webinar hosted by Insurance Post in association with GitLab brought together technology leaders to discuss best practice: where value is emerging, where the risks are growing, and why integration and orchestration are now critical.
Watch the webinar
Watch the webinar
1. Introductions
Chapters
2. Common AI problems for tech teams
8. Orchestration and platform thinking
9. Productivity winners and losers
Introductions
Chapter 1:
Common AI problems for tech teams
Chapter 2:
Building a receptive culture
Chapter 3:
Dangers of disconnected initiatives
Chapter 4:
Key insights
1. Consider the full value stream
AI can speed up coding, but without an end-to-end view, bottlenecks can shift downstream.
2. AI needs governance built in, not bolted on
Without controls, AI risks creating code bloat, inconsistent outputs and security vulnerabilities.
3. Integration beats experimentation in silos
Disconnected AI initiatives can lead to fragmented workflows, duplicated effort and new risks.
4. Data residency is now a frontline issue
As models proliferate, insurers must keep tight control of how sensitive data is processed and managed.
5. Boards need realism, not hype
Forget the headlines. Leaders must reset expectations around probabilistic outputs and phased delivery.
6. Orchestration turns AI from a tool into capability
Platform thinking means consistent pipelines, reduced cognitive load and visibility from plan to production.
Martin Young
CTO, Convex Insurance
Pankaj Kane,
Chief engineer, Admiral Group
Tim Gough,
CTO, Simplyhealth
George Kichukov,
Field CTO, Financial Services, GitLab
The panel
“When you connect the whole value stream, that’s when AI becomes genuinely powerful.”
11. ESG considerations
10. Applying AI to specific domains
4. Dangers of disconnected initiatives
3. Building a receptive culture
13. Best practice delivery improvement
12. Team structure and skills
6. Building the value case for AI
5. Managing board expectations
7. Security and compliance issues
Orchestration and platform thinking
Chapter 8:
Security and compliance issues
Chapter 7:
Building the value case for AI
Chapter 6:
Managing board expectations
Chapter 5:
Team structure and skills
Chapter 12:
ESG considerations
Chapter 11:
Applying AI to specific domains
Chapter 10:
Productivity winners and losers
Chapter 9:
Best practice delivery improvement
Chapter 13:
