While insurers are moving beyond experimentation with AI into broader deployment, data quality remains the key challenge to scaling the technology, according to new insights from EXL’s latest study.
EXL says insurers are moving beyond experimentation with artificial intelligence and into broader deployment, but the company’s latest study suggests that data quality remains the biggest obstacle to scaling the technology. According to the report, 54% of insurers say AI is delivering process efficiencies at significant scale, while 51% are using it to attract new customers.
The study also shows the industry is beginning to redesign work around AI rather than simply layering the technology on top of existing systems. EXL said 46% of insurers have fully deployed AI in actuarial and underwriting, areas that rely heavily on structured data and repeatable workflows. Rup Goswami, who leads insurance growth at EXL, said the insurers seeing the strongest returns have treated data infrastructure as a business priority rather than an IT exercise.
Yet the same report suggests that poor data practices are still holding many carriers back. EXL found that 56% of insurers see data as a challenge to AI success, while 38% blame silos, or disconnected databases and systems, for slowing progress. Only 24% described their data management maturity as leading edge. Goswami said the gap between AI leaders and laggards is “almost entirely a data story”, with 91% of leaders rating themselves ahead in data management maturity compared with 61% of laggards.
The report also indicates that insurers are better than most industries at turning pilots into production systems. EXL said 62% of AI pilots in insurance make it into production, a stronger rate than in other sectors. Even so, many projects still stall before reaching scale. Industry reports cited by other firms tell a similar story, describing a “pilot purgatory” in which insurers have plenty of trials but too few enterprise-wide rollouts. EXL said the firms that succeed are the ones that build for production from the start, focus on areas with cleaner data and tie each project to a clear business owner.
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