The State of AI in Insurance: Key Takeaways from the InsurTech Insights Conference

Every spring, top executives and thought leaders from the insurance industry gather in London to discuss technological advancements and innovation trends. For the second year in a row, generative AI dominated the conversation. While the pace of adoption varies widely across insurers, there is little debate that AI and automation are now critical to future competitiveness.

If you missed this year’s event, here are the key insights on AI in the insurance sector:

1- A Challenging Market Landscape – And It’s Only Getting Tougher

Several macroeconomic factors are reshaping the insurance industry, making innovation a necessity:

• Mounting Financial Pressures: Climate change, political instability, and economic uncertainty make risk assessment increasingly complex, while inflation continues to drive up the costs of claims. In 2024 alone, natural catastrophes resulted in $140 billion in insured losses, making it the third most expensive year since 1980.
• Evolving Customer Expectations: Digital-savvy consumers, particularly Generation Z, demand seamless, personalized, and real-time interactions. Insurers that fail to meet these expectations risk losing relevance.
• Workforce Challenges: A significant portion of the insurance workforce—up to 50%—is set to retire in the next 15 years, taking with them decades of industry expertise. Combined with the sector’s struggle to attract new talent, this threatens insurers’ ability to maintain service levels.

The most pessimistic voices predict that only the top three insurers in each market will survive these pressures. Across the board, there is strong consensus that leveraging AI and automation is the only way to stay competitive in these difficult times.

2- Insurers Are Reacting in Different Ways

Insurers are responding to AI adoption in three distinct ways:

• The Front Runners: These organizations made AI a strategic priority years ago. They have built in-house machine learning and data science teams, launched large-scale AI initiatives, and are now setting industry benchmarks. Their innovations are shaping expectations across the sector and increasing the urgency for others to catch up.
• The Cautious Followers: While recognizing AI’s importance, these insurers remain hesitant. They experiment with small-scale projects, often through partnerships with established vendors or consultancies. However, a lack of internal expertise, innovation-friendly culture, or strategic focus prevents them from fully capitalizing on AI’s potential.
• The Skeptics: A small minority still question AI’s long-term viability, likening it to past tech fads like blockchain. However, by waiting for others to fail before acting, they risk falling irreversibly behind.

A thriving ecosystem of InsurTech companies and software vendors has emerged alongside the insurance industry, eager to capitalize on the excitement surrounding generative AI. The exhibition halls at InsurTech Insights were packed with AI-driven solutions promising to revolutionize the sector. However, in a crowded marketplace, differentiation is proving to be a challenge. As one insurer put it, “With so many vendors making the same promises, it’s hard to tell them apart.”

3- The Four Pillars of AI Success in Insurance

To maximize AI’s potential, insurers must focus on four foundational principles:

Foster a Data-Driven Culture

Insurers must capitalize on one of their biggest assets: data. Yet much of this wealth remains unstructured, outdated, or siloed within different systems and departments. This fragmentation prevents the extraction of meaningful insights and limits enterprise-wide synergies. To fully leverage AI, insurers must prioritize data quality, integration, and accessibility across platforms. Additionally, creating better-quality data at the source should be encouraged.

Leading carriers are already tapping into new data sources—such as wearables, IoT sensors, and telematics—to personalize products and proactively mitigate claims. The ultimate goal? Reduce future claims by helping customers prevent incidents before they happen—enhancing both profitability and customer trust. Of course, fostering a data-driven culture requires investment in data governance while ensuring compliance with evolving data privacy regulations.

Embrace User-Centricity

Lack of alignment with user needs is a key reason why half of AI initiatives fail beyond the pilot stage. Before committing significant resources to new technology investments, first identify end-user pain points and workflow bottlenecks. Cross-functional teams including business stakeholders are essential to ensure AI investments deliver real value to your organization.

Best practice: Engage business teams early in the ideation phase and maintain iterative feedback loops for continuous refinement.

Measure and Benchmark Impact

During planning, prioritize AI projects based on their business impact. Try to quantify this impact, both in terms of direct savings and indirect benefits, like improved customer satisfaction and long-term retention. Defining key performance indicators (KPIs) and setting clear benchmarks help ensure AI investments drive tangible value. Regular progress reviews, transparent reporting, and predefined exit criteria for underperforming initiatives can prevent wasted resources and help secure stakeholder buy-in.

Key to success: Maintain transparency on progress, setbacks, and results. Clear success metrics make it easier to demonstrate value and secure funding for future initiatives.

Shift Mindsets for AI Readiness

Technology alone isn’t enough– It’s the people who drive transformation. Successful AI adoption requires guiding your organization through change. Address concerns by listening, educating, and demonstrating AI’s value (which should be easy if you’ve prioritized user-centricity!). To accelerate adoption, consider incentives that encourage engagement with new technologies.

A Note on AI Talent: Build or Buy?

Does every insurer need an in-house AI team? Not necessarily. Organizations must align AI talent strategies with business goals:

• AI-Making Insurers (who develop proprietary AI solutions) need for example machine learning engineers, data scientists, and NLP experts.
• AI-Ready Insurers (who integrate off-the-shelf AI solutions) should rather focus on hiring software architects and cloud engineers, but also business analysts and cybersecurity specialists.

Regardless of strategy, AI-related expertise is becoming a critical asset. Insurers should start hiring and upskilling now to stay ahead.

Conclusion: Doing nothing is not an option

The insurance industry faces both unprecedented challenges and exciting opportunities. As customer expectations shift, climate risks grow, and digital channels take over, insurers that proactively invest in AI will emerge as winners. This goes far beyond chatbots and customer service automation—it’s about rethinking the entire insurance value chain.

The message from InsurTech Insights 2025 is clear: Insurers that fail to embrace AI risk obsolescence. The time to act is now.