AI isn’t transforming insurance from the outside. It’s quietly rewriting it from the core—and exposing who’s ready, and who’s not.Insurance leaders talk about innovation, but many are still constrained by legacy systems that dictate how decisions are made, how fast change happens, and how much risk leaders are willing to take. In a world where customer expectations evolve in real time, that hesitation has become a liability.In this episode of Scouting for Growth, Sabine VanderLinden is joined by Manish Shah, President and Chief Product Officer at Majesco, to unpack what happens when intelligence is embedded directly into the core of insurance operations.Manish challenges a dangerous assumption: that being “cautious” with AI is safer than moving forward decisively. In reality, the greatest risk facing insurers today isn’t automation—it’s irrelevance. As AI evolves from copilots to agentic systems capable of autonomous action, insurers must decide where technology ends and human accountability begins.This conversation tackles the hard questions leaders are wrestling with—but rarely say out loud. Which decisions should never be fully delegated to machines? How do insurers earn trust operationally, across underwriting, claims, and service, rather than promising it in marketing decks? And how do you modernise without breaking the very promise insurance was built on?Manish shares why the future isn’t about efficiency alone. The real opportunity lies in redesigning insurance to feel more relevant, more responsive, and more human—using AI to augment judgment, not replace it.Looking three to five years ahead, this episode paints a credible vision of insurance powered by intelligent cores, guided by humans, and built around participation, prevention, and peace of mind.This is a must-listen for CEOs, CIOs, CPOs, and innovation leaders who know that AI is no longer a future conversation—it’s a leadership test.Because the real question isn’t whether AI will reshape insurance. It’s who will have the courage to redesign it responsibly—and first.