We had little time to perform a strategic review of our client’s innovation lab. The analysis was part of a broader enterprise strategy program, and I needed to work with the lab’s leadership team to provide an update that would feed into the broader assessment.
Executive Summary
Several years ago, consultant Chris Bassett worked with the leadership team of an insurance innovation lab set up to revitalize their insurer's business. The team had superstar performers with deep expertise in underwriting, actuarial and insurance product design. But after years of investment, they had yet to land a significant win.According to Bassett, the hurdle was an issue of perspective—of "not seeing the forest for the trees." Easy to diagnose but hard to solve.
Here, he reveals that solving it entailed analyzing key management decisions, exploring the relationships between those to isolate their underlying pattern, and then using these insights to disrupt the team's prevailing narrative. The approach draws on a method Bassett refers to as "reframing like a fox," based on Philip Tetlock's research into expert predictions.
In this article, Bassett unpacks the foundations of this method. Using the innovation lab engagement by example, he reveals how insurance leadership teams can deploy it to break free of limiting narratives. According to Bassett, this discipline is becoming more consequential as carriers begin delegating operational judgment to agentic AI systems (prediction engines in their own right).
The lab originally had been established with a clear business model: It would disrupt the insurance value chain, capturing value that increasingly flowed through to intermediaries, and in doing so generate meaningful revenues for their parent organization (in the region of hundreds of millions of dollars annually) after an initial ramp-up period.
Early results signaled that it would take longer than expected to yield meaningful returns. Internal feedback shared with the lab suggested a shift toward building and partnering to create platform-based InsurTech services. When that didn’t prove fruitful either, the lab was steered toward incrementally broader topics—data brokerage, blockchain—before settling into a venture capital-style approach of placing many small bets in the hope that one would strike big and realize the returns that had been promised at the outset.
Now it was year five and the lab was nowhere close to achieving their target. It wasn’t clear how they had missed expectations so significantly. The answer lay in the sequence of decisions that gradually separated the business reality from the assumptions that supported its original model and revenue ambitions.
A Slow Drift
We approached this challenge by breaking down the key strategic decisions, both to formalize a storyline of how the lab had developed and to isolate the key moves so they could be viewed independently of each other. We found that the revenue expectations set at launch were plausible based on a clear set of assumptions. If all these assumptions held, and the business followed the path mapped at the outset, it was plausible that the lab could have realized the predicted results.
