Legacy systems and fragmented data hinder efficiency gains
A new report from AutoRek highlights how entrenched inefficiencies in insurance operations are not only reducing productivity but also obstructing the effective deployment of artificial intelligence across the sector.
Bottlenecks embedded across core processes
The report, Insurance Operations & Financial Transformation 2026, draws on a survey of 250 senior managers across the UK and US insurance markets. Its findings point to a systemic pattern of operational drag, where inefficiencies in one area cascade into broader organisational friction.
Among the most prominent challenges identified are slow settlement processes, often caused by manual intervention and outdated workflows. Claims processing, in particular, remains heavily dependent on legacy systems that struggle to integrate with newer digital tools. This results in delays, increased operational costs, and reduced customer satisfaction.
Compounding the issue is widespread data fragmentation. Many insurers continue to operate across multiple disconnected platforms, limiting visibility and making it difficult to achieve a single source of truth. This fragmentation undermines decision-making and complicates regulatory compliance.
AI adoption constrained by operational reality
While artificial intelligence is widely recognised as a transformative force within insurance, the report suggests that its implementation is being significantly constrained by these underlying inefficiencies.
Rather than enabling seamless automation, fragmented data environments and inconsistent processes often force AI systems to operate with incomplete or unreliable inputs. This reduces their effectiveness and limits the return on investment for digital transformation initiatives.
The findings indicate that many insurers have begun deploying AI in targeted areas, such as fraud detection, underwriting support, and customer service automation. However, large-scale, enterprise-wide adoption remains limited due to structural barriers within existing operations.
Interconnected inefficiencies create systemic drag
A key insight from the report is that these challenges are not isolated. Instead, they form a network of interconnected bottlenecks that reinforce one another.
For example, poor data quality can slow claims processing, which in turn increases manual workloads and reduces the capacity for automation. Similarly, legacy IT infrastructure can hinder both data integration and the deployment of advanced analytics tools.
This interconnected nature means that incremental improvements in individual areas may not be sufficient. Instead, insurers may need to adopt more comprehensive transformation strategies that address operational, technological, and organisational dimensions simultaneously.
Pressure mounts for structural transformation
The report concludes that insurers face a critical juncture. While the potential benefits of AI are substantial, realising them will require a fundamental rethinking of internal processes and data architecture.
Modernisation efforts are likely to focus on streamlining workflows, consolidating data systems, and investing in scalable digital infrastructure. Without such changes, the sector risks falling short of the efficiency gains and innovation potential that AI promises.
As competition intensifies and customer expectations evolve, the ability to overcome operational drag may become a defining factor in determining which insurers successfully transition into a more automated, data-driven future.
Newshub Editorial in Europe – March 23, 2026
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