Could GenAI free insurance companies from pilot purgatory?

Generative AI (GenAI) has taken the business world by storm, promising to transform industries with its potential $4.4 trillion impact on the global economy, according to McKinsey.

In data-intensive sectors like insurance, the allure of GenAI is particularly strong. Yet, many insurers find themselves trapped in “pilot purgatory,” where AI initiatives linger in experimental phases without scaling up or delivering significant returns.

The insurance industry stands at a critical juncture. While some companies have begun deploying GenAI for tasks like claims processing and underwriting automation, they’re often missing the bigger picture. To truly harness the transformative power of AI, insurers need a comprehensive strategy, that goes beyond isolated applications.

Why is the insurance industry stuck?

A significant hurdle is the industry’s tendency to focus too much on the technology itself rather than the business outcomes it can achieve. McKinsey’s Cameron Talischi points out that insurers often spend excessive time testing and benchmarking tools like large language models (LLMs), even though the choice of LLM usually has a marginal impact on performance.

This technology-centric approach diverts attention from the real goal: delivering business value.

Experimentation is essential, but it shouldn’t become a distraction. Successful insurers prioritise identifying commonalities between use cases and developing reusable technology modules that can be scaled across the organisation.

The untapped potential of GenAI in insurance

The insurance industry thrives on data—much of it unstructured, complex, and dispersed across various platforms. GenAI excels at processing this type of information, making it invaluable for enhancing operational efficiency and customer engagement.

In claims management, GenAI can swiftly and accurately analyse vast amounts of unstructured data like medical records and legal documents. This accelerates the process, reduces human error, and improves customer satisfaction. For underwriting, particularly in property and casualty insurance, GenAI can extract critical information from submissions, helping underwriters assess risk more effectively and make faster decisions.

Beyond operational tasks, GenAI can reshape customer service. By automating routine inquiries—such as coverage questions or personal information updates—insurers can offer more self-service options. This not only improves the customer experience but also frees up employees to focus on more complex tasks.

Moving beyond isolated use cases

One reason many insurers struggle to scale AI initiatives is their reliance on isolated use cases that fail to deliver significant ROI. Instead, companies should consider reimagining entire business domains—like claims processing, underwriting, and distribution—by integrating GenAI with traditional AI and robotic process automation (RPA). This holistic approach allows for a complete overhaul of how data is collected, processed, and utilised across the organisation.

By identifying common elements across different use cases, insurers can develop reusable components that expedite AI deployment in new areas. This strategy minimises the need to “reinvent the wheel” for each new application, saving time and resources.

Building the right capabilities

A strong data foundation is crucial for scaling AI effectively. Without clean, well-organised data, even the most advanced AI tools will underperform. Investing in robust data management systems ensures that AI initiatives have the quality input they need to deliver meaningful results.

Beyond technology, having the right people is essential. Insurers need teams that combine data science expertise with deep industry knowledge. Building this talent pool may require upskilling existing staff or recruiting new specialists who understand both AI and the insurance landscape.

Addressing risks and strategic decision making

Deploying AI at scale brings ethical and regulatory considerations to the forefront. Issues like data privacy, algorithmic bias, and the potential for AI-generated errors (or “hallucinations”) pose significant risks. For instance, GenAI could be misused to generate fraudulent claims or manipulate images, exposing insurers to new forms of fraud.

While regulations like the EU’s Artificial Intelligence Act are starting to address these concerns, insurers shouldn’t wait for legislation to dictate their actions. Implementing robust ethical frameworks and compliance protocols proactively can mitigate risks and build trust with customers and regulators alike.

Understanding when to make strategic decisions is as important as the decisions themselves. Assessing the timing allows insurers to gather necessary information and reduce risks associated with AI implementation.

Starting with small-scale pilot programmes can provide valuable insights and data. These pilots should be designed to test critical assumptions and de-risk larger initiatives. Successful pilots can then be scaled up, ensuring that resources are allocated to projects with proven potential.

Insurers need to strike a balance between exploiting existing assets and exploring new opportunities. GenAI offers avenues for both—enhancing current operations and opening doors to innovative business models.

Conclusion: Escaping pilot purgatory

Breaking free from pilot purgatory requires a shift in mindset and strategy. By focusing on business outcomes, developing reusable technologies, and addressing ethical considerations, insurers can unlock the full potential of GenAI.

The journey involves more than adopting new technology; it’s about transforming organisational processes, building the right capabilities, and making strategic decisions that position the company for long-term success.

As the industry stands at this inflection point, the choice is clear: adapt and thrive or remain stuck in endless pilots that fail to deliver. With a comprehensive approach, insurers can not only escape pilot purgatory but also lead the way in innovation and customer satisfaction.

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Lee Arthur is the General Manager at Blenheim Chalcot, and Chair at our Publisher, specialising in business strategy and technological innovation in the financial sector.

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