# UK Insurers Integrate AI, Face Execution Gap > Source: > Published: 2026-06-15 09:43:00.990159+00:00 # UK Insurers Integrate AI, Face Execution Gap According to Earnix's survey of more than **400** global insurance executives, including **40** UK insurance leaders, **55%** of UK insurers have integrated artificial intelligence into core business functions. The report, cited by Reinsurance News, found **98%** of UK insurers are either already using or planning to use generative AI to process unstructured data, while **30%** say they are falling significantly behind customer expectations on personalisation. Earnix's research also reports **91%** of UK insurers plan to increase investment in third-party data and **53%** of respondents say regulation is moderately slowing AI innovation. The survey indicates UK firms are prioritising AI for operational workflows such as claims processing and policy issuance ahead of customer retention. Adrian Mincher of Earnix said, "There's no shortage of ambition or investment across the market, but many firms are still finding it difficult to turn insight into action at scale." ### What happened According to a report and survey published by Earnix and reported by Reinsurance News, more than **55%** of UK insurers have integrated AI into core business functions. Earnix's survey covers responses from over **400** global insurance executives, including **40** UK insurance leaders. The findings state **98%** of UK insurers are either already using, or planning to use, generative AI for unstructured data processing, **30%** acknowledge lagging customer expectations for personalisation, **91%** plan to increase investment in third-party data, and **53%** say regulation is moderately slowing AI innovation. The research also reports operational workflows such as claims processing and policy issuance are being prioritised over customer retention use cases. ### Editorial analysis - technical context Companies undertaking comparable transitions from pilots to enterprise AI frequently encounter three technical choke points: inconsistent data ingestion and feature engineering across legacy systems, underdeveloped MLOps for continuous model deployment and monitoring, and gaps in model governance that complicate regulated use cases. For insurers, personalisation at scale typically requires real-time scoring, robust feature stores, and stronger unstructured-data pipelines (claims notes, emails, documents) to realise the kinds of generative-AI workflows Earnix highlights. ### Context and significance Industry observers have noted a broader pattern where early adoption metrics (high share of firms running AI pilots) do not automatically translate into operational impact. Earnix's numbers fit that pattern: high headline adoption alongside a reported "execution gap" and customer-experience shortfalls. The reported emphasis on third-party data reflects a common industry tradeoff, where enriched external signals can improve models but raise integration, privacy, and compliance complexity. ### What to watch Monitor whether follow-up studies show movement on the personalisation deficit and whether investments in data-platform engineering and MLOps rise in published budgets. Also watch regulatory guidance and supervisory feedback, since **53%** of respondents told Earnix that regulation is moderately slowing innovation. For practitioners, signals to track include published case studies of productionised pricing/claims models, investments in feature-store tooling, and vendor announcements addressing unstructured-data ingestion for insurance workflows. ### Quoted source Adrian Mincher, Head of UK, Ireland and South Africa at Earnix, said, "There's no shortage of ambition or investment across the market, but many firms are still finding it difficult to turn insight into action at scale." ## Scoring Rationale The report is notable for documenting broad AI adoption in UK insurance and a clear execution gap, which matters to practitioners building production ML systems. It is not a frontier technical milestone, so it ranks as a solid, industry-relevant development. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets [Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget) [High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page) [Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model) 250 free problems · No credit card [See all Ad Tech problems](/problems/datasets/adtech)