Only 7 percent of insurers successfully scaled AI into production last year, according to a 2025 BCG report. The bottleneck is no longer model accuracy. It is assigning permanent ownership for monitoring, updates, and support once a pilot ends. Without a designated operator, promising projects disappear when data scientists rotate off the team.
What separates a pilot from a product
Rob Galbraith, CEO of Forestview Insights, calls the gap between those numbers a product-management failure inside carriers. A pilot proves a concept works. A product requires an account owner, billing support, implementation procedures, and a defined patch path. Insurers with large innovation budgets sometimes explore untested tools, but most need stable systems for daily operations.
"You need to have an account representative if there's any issues," Galbraith said. "Somebody you work with, with billing, if you have any issues with the product implementation, how are you going to get updates, how are you going to get patches?"
The same standard applies to models built internally. Carriers often possess strong data scientists, complete datasets, and the subject-matter expertise needed to build a useful tool. That capability does not guarantee the operating structure to keep it running.
"Once it implements, those resources are gone," Galbraith said. "Those data scientists move on to a different project. The people who are the subject-matter experts on the business side, they move on to their regular day jobs. And then if you need to upgrade or you need some monitoring or whatever, you're kind of stuck."
The funding gap after launch
Project teams typically vanish after implementation. The launch budget covers proof of concept and initial rollout, but rarely funds retraining, documentation, performance reviews, or model retirement. AI systems degrade as underlying data shifts, workflows evolve, and regulatory expectations tighten.
NIST's AI Risk Management Framework treats oversight as a continuous cycle across design, development, use, and evaluation. Accountability does not end at go-live. Executives must shift funding from experimentation to long-term model care. The business unit needs to finance ongoing maintenance once project grants expire.
Why this matters for insurance professionals
Successful deployment depends on operational discipline, not just algorithmic accuracy. Teams should align their roadmaps with AI for Insurance frameworks that prioritize lifecycle maintenance over isolated proofs of concept. Leadership must also evaluate their governance playbooks under AI for Executives & Strategy standards to ensure budget lines cover post-launch support.
Operations leaders should assign a dedicated model owner before signing vendor contracts or approving internal development sprints. Budget lines must explicitly cover quarterly drift checks and compliance audits. The carriers that succeed will treat every successful pilot as a permanent product.
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