I would recommend that CEOs have their teams read this report prior to a business planning and strategy session. It raises questions that must be answered if insurers are to leverage AIs (including Digital Agents). Read the full report so the content below is to encourage that. I have quoted the report and the individuals below whilst they entirely reflect my own opinions in which I have frequently written about and experience of business transformation.

“This isn't an IT ops problem; it's a business and operating model problem”. Rory Yates

Insurers treat AI as a productivity layer rather than a decision architecture”. Oguz Anakoc

“Without aligned outcomes, even strong data foundations drift toward efficiency metrics”. Ollie Holden

A historical context

The Industrial Era (Late 19th - Early 20th C.): Technologies like the electric motor were available for decades before companies realized that putting motors on individual machines (rather than one giant steam engine with leather belts) required completely rebuilding factory layouts for workflow efficiency

The PC & Internet Era (1980s - 2000s): The lag was roughly 10 to 20 years. Companies initially used personal computers merely as "electronic typewriters," maintaining their old bureaucratic, paper-based hierarchies. It took years to restructure departments and processes to unlock the true productivity gains of digital workflows

Applying these points to the CapGemini Report

Capgemini shows that 72% of AI spending goes to technology, but only 28% is invested in change management initiatives. Technology creates capability. Change management determines whether this capability translates into performance. This imbalance leaves many programs short of the organizational support required to move from pilots to full‑scale implementation.

Strategically, only a small number of insurers connect AI to driving tangible business results beyond efficiency. Technically, legacy systems and data quality slow down progress. Organizationally, a lack of ownership and Return on Investment (ROI) metrics mean programs rely on individual champions.

Closing this gap requires an architecture built around expert human judgment – not human workflows with AI added on top. The longer insurers delay this redesign, the harder it becomes. AI agents can improve at executing defined tasks, but they can’t reorient themselves toward new strategic priorities as risk landscapes shift.

A top-performing group of about 10% of P&C insurers – Capgemini calls them “intelligence trailblazers” – demonstrate how the industry can move forward. Compared to mainstream insurers, these winning organizations have achieved 21% higher revenue growth and 51% greater share price increases over a three-year period. What sets them apart isn't spending, but strategic clarity. My feeling is that this is what makes the difference to the other 90%. They treat AI as a core operating capability and address strategy and talent, technology, and organizational adoption simultaneously. Ais are  a means to an end.

However, even trailblazers haven’t solved all of the industry’s challenges. Most AI still functions at the individual task level, despite significant employee time spent collaborating across teams. Only a small minority of players report high data readiness, limiting effective AI deployment where underwriting and claims decisions depend on unstructured data. Most importantly, AI has been layered on top of existing workflows designed for humans, rather than processes completely redesigned for AI capabilities. These gaps shape the upcoming competitive battleground and set the blueprint for what every insurer should now develop.

The shift from a focus on efficiency to the pursuit of competitive advantage is already differentiating a small, leading group of insurers who are building long-term benefits, from the majority of insurers who are optimizing current operations. The gap between these intelligence trailblazer insurers and most other organizations isn't just a technology problem. It’s about organizational redesign decisions yet to be made.

I urge you to read the full report and come to your own conclusions. Then it is hard work, sweat and tears often, and the ability to lead all parts of the company, learn and iterate and achieve competitive advantage. Efficiency and cost-cutting are about doing the same things more efficiently. Innovation is about rethinking what customers will actually need and redesigning processes and products to deliver that future. Henry Ford, his factories, and Model-T car are one example. 

Another is IBM; IBM was founded on June 16, 1911, as the Computing-Tabulating-Recording Company (CTR) by three key figures: Charles Ranlett Flint, Herman Hollerith, and George Winthrop Fairchild. In 1914, Thomas J. Watson Sr. joined as general manager and later became CEO. When IBM developed mainframes, Watson thought the world would only need a small number but innovation  beget more economic activity which changed the whole potential. Experiment, measure, iterate, learn and evolve constantly. These are not new concepts but forged over centuries. It’s a faster today but still takes time to perfect. 

That is the opportunity and challenge of leveraging the potential of AI. 

Bon Voyage!