Insurers Struggle to Turn AI Spending Into Returns
Insurers are spending heavily on artificial intelligence but failing to extract meaningful financial gains because they deploy it in isolated pockets rather than across the business. AI for Insurance applications work best when integrated systematically, not scattered across departments.
AI spending among property and casualty insurers is expected to more than triple to 1.9% of revenue by 2026, according to Boston Consulting Group. Yet many insurers are not seeing proportional returns.
The Gap Between Spending and Results
Insurers already use AI for underwriting, claim processing, fraud detection, and pricing. These tools automate routine work and catch problems earlier. The problem is compartmentalization.
Boston Consulting found that insurers integrating AI across operations could cut costs by about 20% and increase gross written premiums by 5%. Most are nowhere near those figures because they treat AI as separate initiatives rather than a connected system.
Among insurers already using AI tools, 63% reported modest productivity improvements. Only 11% achieved measurable gains in output per employee.
Legacy Systems and Data Fragmentation Block Progress
Two obstacles stand in the way. Outdated systems were not built to share data across functions. Fragmented or poorly governed data makes AI produce unreliable results.
"Legacy systems can create significant barriers when implementing AI because they simply were not built for this type of data integration," said Kaitlin Piasecki, industry research analyst at A.M. Best Company.
Data quality matters more than spending. AI Data Analysis depends on clean, connected information flowing through the organization.
Investment Plans Remain Strong Despite Challenges
Two-thirds of insurers plan to increase AI investment from 2026 to 2028. Nearly 60% expect AI to reshape their business models between 2027 and 2029, according to an A.M. Best survey from April.
The appetite for AI is there. The execution remains the bottleneck.
What Comes Next
The key questions facing insurers are straightforward:
- How quickly can legacy systems be modernized to support wider AI deployment?
- Will stronger returns come from underwriting, claims processing, or fraud detection first?
- Which insurers will solve the data governance problem before competitors do?
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