What we consistently observe is that AI systems today are advisory, not autonomous. They inform human decision-makers but do not replace them. This is not merely a limitation of current technology—it is a deliberate, principle-driven design, rooted in the insurance industry’s deep responsibility to manage risk with care, accountability and human oversight. As we look ahead, this balance must evolve. We cannot indefinitely preserve the human-in-the-loop as a constraint on scale or innovation. Nor can we remove it without confidence in the safeguards, explainability and governance required for responsible autonomy. This raises a fundamental strategic question: How do we build systems that are autonomous, but remain accountable? Understanding artificial general intelligence Artificial general intelligence (AGI) refers to a type of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks—much like a human. Unlike narrow AI, which is trained for specific functions (e.g., fraud detection or language translation),AGI can: • Generalize knowledge across domains and unfamiliar problems • Adapt dynamically to new and unpredictable environments • Reason and make decisions with minimal human guidance • Learn continuously from experience and feedback • Exhibit creativity and innovation, going beyond preprogrammed logic Understanding reinforcement learning Reinforcement learning (RL) is a branch of machine learning where AI agents learn by interacting with their environment. Instead of learning from static datasets, RL agents receive rewards for correct decisions and penalties for errors— continuously refining behavior through feedback loops.
Two paths forward in solving the autonomy challenge This introduces an urgent strategic imperative: If autonomy is the goal, how do we get there safely, reliably and with the current constraints? We believe there are two distinct—but complementary—pathways.
4 | Reinforcement before autonomy: Engineering trustworthy autonomy in insurance AI
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