Data Challenges Hamper AI Adoption in Insurance Sector

A new report from Autorek, an AI solutions provider for the insurance industry, reveals that fundamental data and operational inefficiencies are holding back large-scale adoption of artificial intelligence across insurance firms in the UK and US. The findings are based on a survey of 250 sector managers detailed in the "Insurance Operations & Financial Transformation 2026" report.

The report identifies interlinked bottlenecks such as slow settlement processes, governance risks, and the widespread use of fragmented data systems that contribute to both inefficiency and elevated costs. On average, survey respondents manage 17 separate data sources, a challenge often exacerbated by industry mergers and acquisitions.

Key findings include:

• 14% of operational budgets are spent correcting manual errors.
• Reconciliation complexity is cited as a significant cost factor by 22% of respondents.
• 22% tie inefficiencies to governance and audit risks.
• Nearly half of the firms face settlement cycles exceeding 60 days.

With transaction volumes projected to increase by around 29% in the next two years, operational expenditure related to manual processing and legacy systems is expected to rise. Despite industry awareness of these issues, practical progress has been limited.

There is a clear disconnect between expectation and reality regarding AI's potential. While 82% of surveyed firms expect AI to fundamentally reshape the sector, only 14% have fully integrated AI solutions, and 6% report no use of AI at all.

Autorek’s analysis points to legacy technology integration, fragmented data, and limited in-house expertise as primary barriers to effective AI deployment. Fragmented data not only complicates operations but also undermines the creation of cohesive data governance models—an essential foundation for scalable AI and automation initiatives.

The study suggests reconciliation processes as promising candidates for early AI testing due to their rules-based nature, which may benefit rapidly from automation. Yet, the authors caution that applying AI or any automation tool over fractured data architectures will likely increase costs without significant efficiency gains. Modernising data infrastructure and adopting cloud-based AI solutions may facilitate better outcomes.

The persistent gap between stable, structured processes and the scattered architecture of data is, according to the report, why so many insurance firms are slow to realise the benefits of AI. Data standardisation and improved governance are considered prerequisites for both scalable automation and cost reduction. While AI holds potential to tackle data fragmentation in ways traditional automation, such as Robotic Process Automation (RPA), cannot, the extent of performance improvement beyond reduced costs remains to be seen.

Autorek concludes that insurers who proactively resolve data and infrastructure challenges are likely to widen their operational advantages over slower competitors, emphasizing that long-term competitiveness may increasingly depend on the successful integration of AI-driven efficiencies.

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