How Insurance Companies Can Improve AI Effectiveness by Organizing Their Data Properly

Autorek, an AI solutions provider for the insurance industry, has released a comprehensive report highlighting operational inefficiencies within companies’ internal processes. These inefficiencies not only reduce overall effectiveness but also create significant barriers to the successful implementation of AI technologies in the insurance sector.

The report projects a 29% rise in transaction volumes over the next two years, which will likely increase operational expenses unless inefficiencies are addressed. Experts attribute this challenge to the persistence of manual processing, fragmented data systems, and transactional complexity typical of modern insurance operations, despite industry awareness.

There is a notable disparity between expectations and reality regarding AI adoption: while 82% of firms expect AI to be transformative, only 14% have fully integrated AI solutions in their workflows; 6% report no AI use at all.

Barriers to AI adoption in insurance

The report identifies three primary challenges hindering AI implementation:

• Legacy system integration difficulties
• Fragmented data environments
• Limited internal AI expertise

Fragmented data also undermines effective data governance frameworks, worsening the overall challenge. Respondents reported managing an average of 17 different data sources, with complexity amplifying especially after mergers or acquisitions.

The report suggests AI’s potential benefits include cost reduction and improved scalability. AI-powered automation could significantly reduce manual error correction and reconciliation mistakes. Notably, reconciliation processes are highlighted as an ideal initial use case due to their rules-based, bounded workflows where automation can deliver rapid results.

Structural challenges remain

The ongoing tension between structured workflows like reconciliation and fractured data sources requiring manual intervention continues to drive high costs and long cycle times. This situation remains despite widespread recognition of these problems.

The report emphasizes that firms resolving these foundational issues—especially through data standardization and robust governance—will pull ahead in performance. While robotic process automation (RPA) addresses some tasks, AI is uniquely positioned to handle complexity inherent in fragmented data and software layers more economically.

However, the speed of progress depends heavily on legacy technology constraints and ongoing operational burdens. Although the full potential of AI to boost performance beyond cost cutting remains to be proven, achieving meaningful expense reductions by fixing structural problems would be a strong foundation for future AI-driven automation initiatives.

(Image source: “Scattered pieces” by Cle0patra licensed under CC BY-NC-SA 2.0)