AI experimentation is everywhere in insurance. But how much of it is actually making it into production and delivering measurable value? In this episode, Robin Merttens speaks with John Hele, Co-Founder and Executive Chairman, and Greg Framke, CEO and Co-Founder of Portage AI, about why they believe the biggest opportunity for AI lies not in automating the easy work, but in tackling some of insurance’s most complicated processing and data challenges. John and Greg bring experience from both sides of large-scale technology transformation. Before founding Portage AI, they worked together at Resolution Life on a major technology programme to establish independent operations following acquisitions in Australia, New Zealand and the US. The transformation involved around $400 million of investment and was designed to deliver annual savings of $150 million. That experience, alongside their work applying AI in production, helped shape their view of where the technology can create meaningful value today. Greg explains why Portage is deliberately targeting the difficult “last 20%” of processing: work that has historically been too complicated, expensive or technically challenging to automate. Rather than starting with the technology, their approach is to identify a specific problem, train models on a client’s own data and demonstrate value through a production-ready solution before asking the client to commit. The conversation explores one of the insurance industry’s longest-running challenges: turning fragmented, inconsistent data into something genuinely useful. In specialty insurance and reinsurance, information can arrive from brokers, clients and other parties in different formats and using different terminology. John explains how AI can help establish a common ontology across that information, potentially overcoming data cleansing and standardisation problems that insurers have spent years trying to solve. But better processing is only the starting point. Once detailed underwriting and claims information is structured and accessible, John and Greg believe insurers can begin applying predictive models to identify patterns that humans may struggle to find. John uses the example of “Move 37”, the unexpected move played by AlphaGo against world champion Lee Sedol, to illustrate the possibility: could AI uncover similarly unexpected insights in insurance data that lead to better pricing, risk selection and loss ratios? They also discuss where this is already being applied. Greg explains the opportunity in post-bind processing, bordereaux and delegated authority business, particularly across London, Bermuda and the US. He also discusses Portage AI’s acquisition of technology and people from Insurwave and how bringing together pricing submission processing with post-bind capabilities could ultimately make operational data more useful for underwriting decisions. The bigger ambition is not to replace insurers’ existing technology stacks. Instead, Greg describes an AI engine that can sit between systems, receiving and structuring information before feeding it into the applications that need it. With AI developing rapidly, he also explains why remaining technology-agnostic could matter, allowing different underlying models to be selected and trained depending on the problem being solved. Finally, John and Greg explain why they see AI as a much bigger change than another wave of process automation. John believes the industry is entering a fourth industrial revolution and that, within the next three to five years, some insurers will begin using these technologies to grow market share profitably. Their message to insurers is simple: move beyond experimenting with AI, apply it to meaningful business problems and demand demonstrable value from the technology. In this episode you’ll learn: Why John and Greg believe AI represents one of the biggest technology shifts of their careers What their experience delivering a major insurance technology transformation taught them about putting AI into production Why some of the greatest opportunities for AI could lie in the difficult “last 20%” of insurance processing How AI can help extract, harmonise and structure information from complex insurance datasets Why creating a common ontology could unlock more value from underwriting and claims data How better structured operational data could ultimately contribute to pricing, risk selection and loss ratio improvements Why specialty insurance, reinsurance, delegated authority and bordereaux processing are particularly interesting applications for AI How Portage AI approaches accuracy, explainability, security and human oversight when deploying models Why Greg believes AI should integrate with existing insurance technology rather than requiring insurers to replace their core platforms What AlphaGo’s famous “Move 37” can teach insurers about the potential for AI to uncover patterns and opportunities humans may have missed Why John believes the next three to five years could reveal which insurers are successfully turning AI into competitive advantage Why insurers should move beyond proofs of concept and demand measurable business value from AI If you like what you’re hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn. 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