Autorek: AI Held Back by Data Chaos in Insurance

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Key Takeaways

1A report from Autorek reveals that 14% of operational budgets are spent on correcting manual errors in insurance.

2Nearly 82% of companies hope that AI will dominate the sector, but only 14% have fully integrated it.

3Companies manage an average of 17 data sources, complicating the implementation of AI.

💡Why it matters — Data inefficiency hinders AI adoption, threatening insurers' competitiveness in the face of increasing transactions.

An Alarming Report on Data Inefficiency

Autorek, a provider of artificial intelligence solutions for the insurance sector, has published a report highlighting the operational obstacles that hinder the efficiency and adoption of AI in the industry. Titled Insurance Operations & Financial Transformation 2026, this document is based on a survey conducted among 250 managers in the UK and the US.

The survey results reveal interconnected bottlenecks, such as slow settlement processes and data fragmentation. These structural issues persist despite widespread awareness among the surveyed companies.

Key Figures on Inefficiency

According to the report, 14% of operational budgets are allocated to correcting manual errors. Additionally, 22% of respondents identify the complexity of reconciliation as a major cost driver. About 22% of respondents also link inefficiencies to governance and audit risks. Nearly half of the companies have settlement cycles that exceed 60 days.

Transaction volumes are expected to grow by 29% over the next two years, which could further burden operational costs. This increase is attributed to a combination of manual processing, disparate data systems, and transactional complexity.

The Gap Between AI Expectations and Reality

While 82% of companies expect AI to dominate the sector, only 14% have fully integrated this technology into their operations. Six percent of companies do not use it at all.

The report identifies several barriers to AI adoption, including the integration of legacy systems, data fragmentation, and a lack of internal expertise. The issue of data fragmentation affects data governance frameworks, making them fragmented as well. The authors of the report cite the complexity of data states in many companies as the primary reason why AI deployments are limited in the sector.

Companies manage an average of 17 data sources, complicating AI implementation, especially after mergers and acquisitions.

Proposed Solutions for Successful Adoption

The authors of the report suggest that reconciliation processes could serve as a testing ground for AI, as they are structured and rule-based. However, any automation applied to a fragmented data architecture may not scale without increased costs.

The report recommends using cloud-based AI platforms to structure fragmented data sources. Data normalization and better governance are essential before considering large-scale automation.

Structural Challenges and Outlook

The dichotomy between structured reconciliation processes and disparate data sources creates measurable complexity in terms of costs and timelines. Companies that manage to resolve these structural issues could gain a significant competitive advantage.

AI could potentially reduce reconciliation costs and address the complexity of fragmented data and software layers that rule-based automation, such as RPA (robotic process automation), may not be able to economically handle. However, the pace of resolving these issues depends on legacy technology and daily operational burdens. While the impact of AI on overall performance remains uncertain, cost reduction could be a sufficiently positive outcome to justify the effort for structural improvement.