Artificial intelligence may be moving from pilot projects into mainstream insurance strategy, but the sector’s real constraint is increasingly looking like data, not software. In reinsurance especially, where underwriting, finance, risk and capital management intersect across multiple systems and jurisdictions, industry observers say the promise of AI is being held back by fragmented records, manual reconciliation and uneven governance.

That warning is echoed beyond the reinsurance niche. TechRadar Pro reported that insurers are keen to deploy tools such as AI, blockchain and real-time fraud detection, yet many are still working around legacy infrastructure and scattered data. The publication cited research showing that 82% of insurers believe AI will shape the industry’s future, but only 14% have integrated it into financial operations, underscoring how wide the gap remains between ambition and execution.

In reinsurance, that gap can be particularly costly. Data sets covering exposures, recoverables and financial flows must be accurate, current and auditable, but many firms still rely on spreadsheets and disconnected systems. According to risk specialist Risktec, the insurers and reinsurers getting the most value from AI are the ones investing first in data quality, common terminology and better documentation, rather than trying to automate flawed processes.

The industry is also beginning to recognise that scale requires more than isolated use cases. AM Best said in a recent survey that nearly 60% of respondents expect AI to materially alter business models within the next one to three years, while 41% are already using AI across core functions. Even so, the same survey pointed to persistent problems around data readiness, cybersecurity and legacy integration, suggesting adoption is advancing unevenly.

That is why cloud-native, “always-current” systems are becoming more central to the conversation. Reuters has previously reported a broader push across financial services for standardised data formats, automated reconciliation and stronger governance, themes that also run through ACORD’s work on insurance data standards. The logic is simple: AI models can only perform reliably if the information beneath them is consistent, explainable and easy to share across business lines.

For reinsurers and carriers alike, the strategic shift is moving from asking where AI can be used to asking whether the business is ready for it. The firms that modernise data foundations first are likely to gain faster pricing decisions, better capital deployment and more responsive risk selection. Those that do not may find that the technology is not the bottleneck after all.

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Source: Noah Wire Services