Eight Generative-AI Use Cases, Shipped to Production

Most enterprise AI programmes die in the lab…impressive demos, no production traffic, no P&L owner. This one was measured on a single standard: use cases running in production, pointed at the real P&L, not the press release.

The Situation

A century-old, Fortune 50 insurer with legacy platforms, regulatory obligations, and a brand that cannot afford a public AI failure…exactly the environment where generative AI usually stalls. The innovation function I led was chartered to prove the technology on the business's terms: in production, at scale, inside the risk appetite.

The Moves

We inverted the usual sequence. Instead of starting with the technology and hunting for applications, every candidate use case had to arrive with a P&L owner, a measurable operating metric, and a path through model risk and compliance before a line of code was written. Legacy constraints were treated as design inputs, not blockers…the architecture wrapped modern, service-based capabilities around the core rather than betting the programme on replatforming first. The wider innovation function ran the same discipline, generating six to eight new offerings a year against a five-year P&L-impact forecast of $1.2B…the majority of it cost take-out rather than new revenue.

The Result

Eight generative-AI use cases shipped to production…not pilots, not proofs of concept. The transferable lesson for boards: the binding constraint on enterprise AI is not the models, it is governance, data plumbing, and ownership. Fix those three and the technology is the easy part.