Eighty-six percent of insurers are confident AI can deliver on their business goals, but only 27% have anything running in production. The three most common reasons come down to data quality problems, security concerns with no clear owner, and ROI that was never properly defined. The organizations closing that gap run their AI initiatives with more discipline.
Insurance executives approved the budget. They sat through the demos. They launched the pilot. And now, months later, it’s still a pilot. Meanwhile, the board is asking about AI strategy and competitors are announcing deployments.
The frustration is legitimate, and so is the pressure.
Bevaya’s State of AI Adoption in Insurance 2026 Report, based on a survey of insurance professionals across claims, underwriting, policy servicing, and IT, identified three operational barriers that consistently stand between a promising pilot and a production program.
Reason 1: Data That Works in a Pilot Doesn’t Work in Production
Data quality and availability is the top barrier to AI adoption, cited by 51% of respondents.
In a controlled pilot environment, teams work with clean, curated datasets. In production, AI meets the actual data environment, fragmented systems, inconsistent formats, duplicate records, and sources that don’t connect. The gap between what the pilot ran on and what production demands is where initiatives quietly die.
The model performed in the proof of concept because the inputs were sanitized. When it hits real, messy, live data at volume, performance degrades and trust erodes quickly.
What to do instead: Treat data readiness as pre-work, not as a problem for the vendor to solve mid-deployment. Assess whether the data required for a specific workflow is sufficient, accessible, and reliable enough before the pilot starts, not after it succeeds. Although no data environment is perfect, most workflows have enough clean, accessible data to support a reliable AI deployment. Pick one, move it to production, and build data discipline from there.
Reason 2: Security and Privacy Concerns With No Clear Owner
Security and data privacy concerns rank second, cited by 48% of respondents.
When AI runs in a sandbox, compliance and risk teams are largely uninvolved. When it touches production data and integrates with core systems, they’re suddenly very involved, and if they weren’t part of the pilot design, there are no good answers ready for their questions.
In insurance, those questions carry real weight. Underwriting decisions, claims handling, and policyholder data all operate under regulatory expectations that require explainability, auditability, and clear access controls. Pilots that skip these requirements early stall at the production gate waiting for security reviews that should have happened months earlier. The absence of a clear owner makes this worse, because security concerns don’t resolve themselves when nobody is accountable for resolving them.
What to do instead: Bring compliance and risk into the pilot design, not the production review. Establish data handling standards, access controls, and audit requirements before the pilot runs. When the pilot ends, there’s nothing left to clear.
Reason 3: ROI With No Definition of Success
Unclear business case and ROI is the third barrier, cited by 40% of respondents.
Most pilots that stall on ROI never defined what success looked like before they started. The pilot produces results that are technically promising but operationally inconclusive, and nobody can make a confident go decision because nobody agreed upfront on what the go threshold was.
The problem compounds when organizations hold AI to a higher accuracy standard than their own manual processes. Even when AI outperforms human throughput, adoption can stall if results don’t exceed expectations by a wide margin. Without a baseline and a pre-defined target, there’s no way to call the pilot a win.
What to do instead: Define success criteria before the pilot starts, tied to KPIs already in the operating review. Cycle time, STP rate, handling time, FTE hours recovered. When the pilot ends, the production decision is already made. The data either clears the bar or it doesn’t.
The common thread across all three barriers is timing. Data readiness, security ownership, and ROI definition are problems that compound when they’re left to the pilot phase. The organizations reaching production solve them before the pilot starts, not after it stalls.
That shift in sequencing changes everything about how a pilot runs. When data is assessed upfront, the production environment doesn’t come as a surprise. When compliance and risk are part of the design, the deployment review moves quickly. When success criteria are defined before the first result comes back, the go decision is straightforward.
The insurance AI pilot itself becomes a confirmation, not a question mark. And that’s the difference between an organization that’s been evaluating AI for eighteen months and one that’s been running it in production for twelve.
