The effective implementation of Artificial Intelligence (AI) in the insurance sector is hindered by operational inefficiencies and data fragmentation. A report by Autorek, a provider of AI solutions to the insurance industry, highlights how inefficient internal processes negatively impact AI adoption.

The report "Insurance Operations & Financial Transformation 2026", based on a survey of 250 managers in the UK and the US, reveals bottlenecks related to slow settlement processes and data fragmentation. The survey highlights the following structural inefficiencies:

  • 14% of operational budgets are spent correcting manual errors.
  • 22% of those questioned cite reconciliation complexity as a significant cause of cost increases.
  • Around 22% link inefficiencies to governance and audit risks.
  • Nearly half of firms operate settlement cycles in excess of 60 days.

Barriers to AI Adoption

The report identifies legacy system integration, fragmented data, and limited internal expertise as the main obstacles to AI implementation. Firms manage an average of 17 data sources, and most consider this a problem, exacerbated by mergers and acquisitions.

AI has the potential to improve scalability and reduce costs, addressing issues related to manual error correction and errors in reconciliation processes. Reconciliation processes, being rule-based, could represent fertile ground for AI-based automation.

Structural Issues

The discrepancy between structured reconciliation processes and disparate data sources creates complexity, measurable in terms of costs and cycle times. Data standardization and governance are prerequisites for scalable automation. AI could address the complexity of fragmented data and software layers more effectively than robotic process automation (RPA).