The insurance industry is not facing a technology problem. It is facing a speed problem. AI-native entrants are not winning because they have better data or more sophisticated models. They are winning because they have eliminated the organizational friction that forces traditional insurers to move at a pace the market has already left behind. Christopher Bannocks, partner at Elixirr and a financial services strategist who works with insurance leaders on strategy creation and execution, has a precise view of where that friction lives and what it costs. “If your decision cycles take quarters, you are always going to be behind your competitors,” Bannocks says.
Speed Is the Strategy
Traditional insurers optimize for risk control. That is not the wrong instinct: it is the foundation of the business. What it has become, in many organizations, is an excuse for decision cycles that no longer reflect competitive reality. AI-native players test weekly, sometimes daily. They treat every deployment as a learning opportunity and every learning opportunity as a source of competitive advantage. By the time a legacy carrier has completed its internal approval process for a single underwriting change, a faster competitor has iterated through three versions and is already deploying a fourth.
Bannocks advocates for identifying two high-value decisions and rebuilding them around tight feedback loops that compress the distance between action and learning. The goal is not to abandon risk discipline. It is to stop letting organizational architecture move slower than the market demands. “The advantage is not scale alone,” Bannocks says. “It is speed.” Incumbents have scale. What separates those who will lead the next decade from those who will spend it catching up is the willingness to redesign how quickly that scale can act.
Decision Advantage, Not Automation
The trap most insurers fall into when they begin AI transformation is treating automation as the destination. Automation reduces operational cost, but it does not create the kind of competitive separation that reshapes a market. Decision advantage does. Better underwriting judgment produces better risk selection. Faster, more accurate claims resolution builds customer trust and reduces leakage. Fraud detection that operates ahead of emerging patterns rather than responding to established ones changes the economics of the book. Price advantage that emerges from genuinely superior risk assessment is the kind of moat that is difficult for competitors to close quickly. “For every AI use case,” Bannocks says, “define what decision you are improving, what metrics matter, and what guardrails protect fairness and explainability.”
The Friction Is Organizational, Not Technical
The most persistent misconception in insurance AI transformation is that the primary obstacle is technological. In practice, the algorithm is rarely the bottleneck. What slows transformation to a pace that AI-native competitors can exploit is the handoffs, approval layers, unclear ownership, and organizational structure built for a different era of decision-making.
Trust must be designed in from the outset, not bolted on when regulators ask for it. Customers want transparency in how decisions about them are made. Regulators want auditability. Employees want clarity about how AI interacts with their judgment. Pick one outcome that matters this quarter. Build the right team around it. That is not a pilot strategy. It is how transformation actually begins.
Follow Christopher Bannocks and Elixirr on LinkedIn for more insights on AI strategy, insurance transformation, and financial services leadership.
