Insurance leaders are facing a structural shift with the rise of AI that is challenging long-standing assumptions about speed, cost, and customer engagement. The core misunderstanding is not about whether AI will matter, but how quickly it will reshape competitive dynamics. “The biggest misconception is the speed. Leaders think it either doesn’t affect them or it will move at a traditional insurance pace,” says Christopher Bannocks, Insurance Partner at Elixirr and award-winning data and AI Executive.
At a time when declining rates are putting pressure on margins, many organizations are responding with cost-cutting measures that leave underlying operating models unchanged. Bannocks sees this as a short-term fix with limited upside. “If you cut costs and don’t change your operating model, you get marginal returns. If you rebuild AI-first, the returns are exponential.” This distinction defines the gap between legacy carriers and AI-native insurance models. It also underpins a broader shift toward data-driven underwriting, underwriting automation, and insights gained from analyzing claims as foundational capabilities rather than incremental upgrades.
The Mechanics of AI-First Transformation
The path to AI-native insurance does not require sweeping, capital-intensive transformation from day one. “You don’t have to boil the ocean,” he says, emphasizing focus over scale. Instead, insurers can isolate a single line of business, such as motor or fleet, and redesign it around AI-first principles. This approach enables measurable improvements in quote-to-bind performance and operational efficiency. Bannocks has led initiatives that increased quote-to-bind ratios by 58%, while delivering substantial revenue growth within months. The model is straightforward: improved risk selection reduces loss ratios, lower costs improve combined ratios, and the resulting pricing advantage fuels growth.
“How insurers increase quote-to-bind ratio with AI comes down to better risk selection, pricing power and speed,” he says. Faster, more accurate decision-making allows insurers to capture more business at higher margins, while maintaining disciplined underwriting. Importantly, Bannocks challenges the assumption that AI transformation requires significantly higher infrastructure investment. In many cases, AI-native systems reduce overall complexity and cost, reinforcing the economic case for insurtech transformation.
Speed, Pricing Advantage, and Market Share
AI-native insurers operate with a fundamentally different cost structure, enabling sustained pricing advantages. Bannocks notes that once this advantage is established, it becomes difficult for competitors to reverse. “If you capture that pricing advantage, it’s very hard for others to win it back.” This dynamic is already reshaping the competitive landscape. While a small percentage of insurers are rebuilding their operating models from the ground up, the majority are still layering AI onto legacy systems. “About 95% are bolting AI onto existing infrastructure,” Bannocks says. “Only a small group are truly AI-native.”
That imbalance is unlikely to persist. As AI-native insurance models demonstrate superior performance, value will concentrate among early movers. Bannocks compares the shift to previous digital transformations, noting that first-mover advantage is both real and durable. “The future of insurance is not evenly distributed,” he says. “It’s concentrated in those who move first.” For boards and executives, this reframes the risk equation: the primary risk is inaction. Enterprise AI governance and clear strategic direction become essential, choosing the right partner for that journey and getting the right advice from an organisation that has experience is essential.
Responsible AI and the Boundaries of Customer Trust
As automation expands across underwriting and claims, the question of customer trust in AI becomes central. Bannocks sees the boundary between helpful automation and overreach as fluid. “The line is pliable,” he says. “It will move as customers become more comfortable with AI.” However, timing and context remain critical. Certain interactions, particularly those involving emotional or high-stakes claims, require human involvement. “There are areas where AI should not be the interface,” he notes. “You have to understand the human at the other end.”
At the same time, AI can actively strengthen trust when deployed thoughtfully. Faster claims payments, proactive risk alerts, and personalized service experiences all contribute to stronger customer relationships. Bannocks points to examples where AI identifies customer frustration in real time and intervenes with human support, turning potential friction into positive engagement. “AI can enhance trust if it’s used at the right moment,” he says. This principle sits at the heart of responsible AI and insurance AI governance, where regulatory frameworks and ethical considerations guide deployment at scale.
From Strategy to Revenue Growth
The broader implication of Bannocks’ approach is that AI is a business model transformation. From data strategy to revenue growth in insurance, the link is increasingly direct. Organizations that embrace AI-native insurance can achieve both operational efficiency and market expansion. “There’s as much unlearning as learning,” Bannocks says. Traditional assumptions about infrastructure, processes, and customer engagement must be reconsidered. The goal is not to embed AI into existing systems, but to redesign those systems entirely. For leaders, the board-level case for AI investment in insurance is becoming harder to ignore. “Doing AI is not the same as building AI-native. That’s where the real prize is.”
Follow Christopher Bannocks on LinkedIn or visit the elixirr website for more insights.
