The next era of insurance: Competing on data, AI, and speed

The insurance industry sits at the intersection of risk and uncertainty. Its very purpose is to measure the unmeasurable, price the unpredictable, and provide a safety net in moments of loss. 

AI brings both promise and peril to this equation. On one hand, AI offers insurers opportunities: the ability to harness vast datasets, spot emerging risks, personalise coverage, and increase the speed and accuracy of decision-making. On the other hand, it introduces new questions around fairness, explainability, and trust. If AI becomes a black box that customers or regulators cannot understand, it risks undermining the very confidence on which insurance is built.

The AI opportunity is hard to ignore 

Even for risk-averse insurers and underwriters, the AI opportunity is hard to ignore. Innovators are already harnessing data through predictive analytics, AI, and other cutting-edge technologies to make confident decisions and enhance risk assessments. Over the past decade, a wave of InsurTech companies has redefined the possibilities in insurance. Unlike traditional carriers, these firms were born digital, leveraging AI and seamless digital experiences from the start. 

This doesn't mean that traditional insurers are obsolete. In fact, many have partnered with or acquired InsurTech firms to accelerate innovation and modernise their platforms. Though InsurTechs have proven that AI-driven insights and customer-centric design are essential for improving risk modelling and transparency.

Despite the potential, most insurers have not fully adopted AI, with only 27% using predictive modelling to anticipate risk and behaviour. 37% use advanced third-party data from outside datasets. 

Why rules-based processes must be left behind

Insurers are facing many challenges as the ecosystem shifts towards technologically savvier players. Customers want faster, more transparent, and fairer insurance experiences like instant quotes, smooth claims, and personalised offers, while new risks and tighter regulations create pressure. Among these risks are cybercrime, volatile financial markets, and digital fraud, which complicate what insurers need to assess and account for in underwriting and claims management.  

Together, these expose the limits of rules-based processes. The traditional, fixed decision frameworks that rely on predefined formulas, criteria, and thresholds are designed to handle risks consistently, but not adapt well to complexity and changing conditions. This creates an opportunity for a new kind of insurer, the one that relies on intelligent data and instant insight.

Why legacy platforms no longer work

Years of market consolidation have left insurers grappling with disparate datasets and outdated technology platforms, many dating back decades. These legacy systems, heavily customised around underwriting, claims, and policy administration, once provided stability and accumulated company knowledge. Today, they stall innovation and drain resources without ROI. 

According to Earnix, 49% of global insurance executives report that technical debt is a major barrier to transformation. In another survey, 44% highlight inconsistencies in their data, while 41% cite a lack of interdepartmental transparency, making it difficult to build a unified view of customers and risks. Perhaps most shocking of all, only 8% of property and casualty insurers used data-driven underwriting in 2024.

Product launches could take up to 12 months for legacy systems (compared with 3–6 months for InsurTech challengers). Adding AI on top could be particularly challenging, especially when innovation budgets are already constrained by legacy maintenance.

Where to start with automation and AI

Automation and AI adoption don't have to be a sweeping, all-or-nothing transformation. Insurers can take a phased approach – building momentum with targeted initiatives that prove value quickly and create a foundation for broader change. 

One starting point is adopting cloud-native integration. Modern platforms can be connected to existing policy administration, claims, and CRM systems through API-first architecture, unlocking flexibility without a full system overhaul. From there, insurers can shift toward modularised platforms with microservices, which allow them to scale specific capabilities independently. This accelerates delivery and ROI while also making it easier to embed AI directly into the services of most value. 

Focusing on high-value use cases is another proven strategy. Claims automation and underwriting AI are strong candidates to prove ROI, and free up resources to reinvest in broader modernisation. Over time, this creates a cycle of efficiency and innovation. Insurers should look beyond their own industry, borrowing lessons from the banking and retail sectors, which are further ahead in AI adoption.

The most critical investment will be in people and culture. Employees need training and encouragement to embrace AI as an opportunity rather than a threat. Early adopters should be rewarded, best practices shared, and experimentation encouraged. But leadership has a pivotal role to play by filling knowledge gaps at the board level. Executives need to grasp what AI really is, or is not, the differences between machine learning and AI, and how emerging tools can solve their problems. With that depth of understanding, leaders will be better positioned to present opportunities clearly and set a vision that balances innovation with responsibility. 

Strategic imperative: wider adoption and scale-up of AI

For insurers determined to seize the future, the path forward is clear: transform legacy systems and fragmented data into platforms that support the next era of underwriting, claims, and risk management. Align your business vision and technology modernisation to unlock AI's full value. 

Those who start experimenting with targeted use cases and scaling what works will build the agility needed to compete in a world of intelligent data. The insurers that succeed will anticipate risk, personalise coverage with precision, and deliver fairness and transparency by design.