- Why insurers must move beyond point-of-sale models to continuous customer relationships
- The shift from static underwriting to dynamic, data-driven engagement over time
- What insurers still don’t understand about their customers, and how better data reduces uncertainty
- How behavioural insight, health data and engagement models can improve both customer outcomes and risk selection
MODERATOR: Fabio Sarrico, Senior Insurance Analyst, Celent
We explore how insurers can upskill teams, redesign roles, and unlock productivity through AI adoption today
- What separates high-performing teams in AI adoption from those that struggle
- Upskilling non-technical talent: enabling underwriters and business users to do advanced analytics
- Embedding AI into day-to-day workflows rather than isolated use cases
- Creating the right culture: time, incentives, and leadership needed to drive adoption
MODERATOR: Fabio Sarrico, Senior Insurance Analyst, Celent
what Allianz learned from supporting the Olympic and Paralympic games – a real-world test of operating at global scale, speed and precision.
How real time risk, cyber resilience and continuous readiness were built for an environment where failure is not an option.
Why insurers must now shift from traditional back-office models to performance critical infrastructure capable of world stage responsiveness
and trust.
Jason Howes, Chief Transformation Officer, Allianz UK.
AI risk is no longer confined to standalone AI products. We examine the rise of “silent AI”, where AI is reshaping risk exposure, amplifying existing perils and creating accumulation risk across portfolios faster than underwriting and governance frameworks can adapt.
- Clarifying how AI interacts with current wordings
- implications for product strategy, underwriting and governance
- How insurers and reinsurers are responding
We cut through the hype to identify where AI is delivering measurable value in insurance today, and where it still falls short
- Where AI is already working at scale: high-volume, decision-heavy workflows across claims, underwriting and operations
- Why some use cases fail: complexity, lack of structure, and overreliance on generic models without workflow design
- How to think about AI as an operational layer: combining agents, human oversight and structured processes to drive accuracy and efficiency
- What to prioritise now: selecting use cases with clear ROI, measurable outcomes and a path to production rather than experimentation
UK financial regulators recently held urgent discussions with cybersecurity officials and major banks to assess risks posed by new AI models. This session will examine today’s complex cyber landscape and explore what needs to be done, and when, to make it safer.
- NCSC CEO Richard Horne warns, AI makes it “easier, faster, and cheaper” for attackers to discover and exploit weaknesses, creating growing pressure on organisations to patch systems far more rapidly than current practice
- In the near term, AI will expose organisations that haven’t taken adequate steps to protect themselves.
- Looking further ahead, organisations must prepare for post-quantum cryptography, while more immediate priorities include agentic AI risks, supply chain attacks, ransomware, cyber insurance, and building genuine cyber-resilience
Three human capabilities amplified by AI.
Why hybrid teams outperform automated/manual models.
What leaders must redesign in culture and workflow.
