AI’s Shifting Landscape: Bubble or Breakthrough for Insurance?
AI spending has surged, but signs point to an economic bubble rather than a guaranteed leap in productivity. For the UK General Insurance sector, the stakes differ from those in manufacturing. Insurance is knowledge- and judgement-driven. Over-reliance on third-party models risks eroding institutional expertise and turning insurers into an enslaved application layer to a small number of monopolistic providers. That outcome would concentrate input costs and reduce strategic control over underwriting, claims and pricing decisions.
Unpacking AI’s True Costs and ROI Hurdles
Beyond licence fees, AI brings rising operational expense: per-query credits, inference compute, continuous data cleaning, annotation, security, and compliance. Many large-scale AI projects show delayed or negative returns as usage and maintenance scale. Hidden costs include vendor-specific formats, exit costs, auditability gaps and the human time needed to validate model outputs. These factors raise questions about long-term economic sustainability of current adoption patterns.
Strategic Resilience: Building an AI Game Plan
Leaders should treat AI as strategic infrastructure, not a plug-and-play upgrade. Practical steps:
- Protect IP: codify data provenance, record model lineage, and insert contractual clauses that preserve rights to derivative assets.
- Stress-test investments: run small, time-boxed pilots with defined ROI gates and shadow-mode comparisons to human performance.
- Avoid single-vendor lock-in: adopt modular architectures, open formats and API abstraction layers so models are interchangeable.
- Preserve human expertise: keep humans in loop for judgement tasks, document tacit knowledge and build labelled corpora from expert decisions.
- Consider proprietary models for core risk tasks, or hybrid deployment that offloads commoditised functions to partners while keeping strategic models internal.
The Leadenhall Project: A Vision for Collaborative AI
One defensive option is a sector-owned, mutualized AI platform for the risk value chain. The Leadenhall Project would pool data, fund shared model development via federated learning, and provide neutral governance to limit future input-cost shocks. A collective approach spreads infrastructure cost, preserves fair access to critical models and reduces dependency on a few commercial providers. The practical next step is a cross-industry feasibility study and formation of a governance steering group to define scope, funding and interoperability standards.
For C-suite leaders, the immediate imperative is to treat AI investment as strategic risk management: measure real economics, guard human expertise and consider collective mechanisms to protect the sector’s long-term autonomy.
