As insurers move from AI experimentation to operational deployment, one question keeps resurfacing:
Should insurers build AI-driven policy coverage capability internally — or buy specialist expertise?
The findings from The State of Policy Coverage Checking report — combined with insights from the webinar Insurance Claims Policy Checking 2025: Bottlenecks, Benchmarks & Breakthroughs — suggest the industry is reaching a tipping point.
While a small group of insurers is beginning to operationalize AI in coverage determination, 44% still report zero automation in coverage checks today.
Coverage intelligence is becoming a critical capability in claims.
The real question is, how insurers industrialize it at scale?
The appeal of building internally
Building internally can appear attractive at first glance.
It offers:
- Control over models and data
- Tailoring to internal workflows
- Ownership of intellectual property
For insurers with strong AI capability and deep insurance expertise, internal development may seem the logical route.
But coverage determination is not a typical AI use case.
It requires systems capable of interpreting complex policy language, handling endorsements and exclusions, analyzing layered policies, and providing explainable, auditable reasoning at clause level.
As policies evolve, these systems must also evolve.
What begins as a manageable project can quickly become a long-term engineering commitment.
The hidden complexity of building
Many internal AI initiatives underestimate the operational reality of scaling AI in production environments.
Common challenges include:
- Scarcity of insurance-domain AI expertise
- Long development cycles before production value appears
- Significant infrastructure and model management requirements
- Continuous maintenance as policy wording and regulations evolve
- Governance demands around explainability and auditability
As Ian Thompson explained during the webinar AI in Insurance: Myths, Realities & the ExCo Gap:
“Build versus buy isn’t just a technology decision. It comes down to scale, capital, speed and capability — and whether you actually have the expertise to deliver transformation internally.”
These challenges often emerge after the proof-of-concept phase, when insurers attempt to operationalize AI across real claims portfolios.
The strategic drivers behind build vs buy
In practice, insurers rarely make the decision based on technology alone.
Several strategic questions shape the approach:
Scale
The largest insurers with exceptionally high claim volumes may justify internal development. However, the majority of carriers often achieves faster impact through partnerships.
Speed
AI capability can take years to develop internally — while insurance domain specialist solutions may deploy within months.
Capability
Few insurers currently have large-scale AI teams with deep claims domain expertise.
Strategic differentiation
Some insurers view AI capability as core intellectual property. Others see it as ever-evolving operational infrastructure best sourced externally.
Why leading insurers are buying
Increasingly, insurers are adopting a pragmatic hybrid model.
They retain governance, oversight and decision authority internally, while leveraging specialist AI providers to accelerate development and optimization.
This approach offers:
- Faster deployment timelines
- Proven insurance-domain models
- Embedded explainability and governance frameworks
- Reduced internal strain on scarce AI talent
- Faster operational impact
Importantly, buying does not mean losing control.
Control is exercised through architecture, governance and oversight, rather than by writing every line of code.
The real value of AI in claims
Another misconception influencing the build-versus-buy debate is the assumption that AI’s primary value lies in reducing headcount.
But claims leaders increasingly see the opportunity differently.
As Ian Thompson noted:
“Efficiency is only one part of the equation. The bigger opportunity is improving customer outcomes and optimizing the cost of settling claims.”
Better coverage decisions drive:
- More consistent indemnity outcomes
- Reduced leakage
- Faster settlement
- Improved customer experience
These improvements directly influence loss ratios and Combined Operating Ratio (COR) — often delivering greater financial impact than expense reduction alone.
The bigger risk: standing still
The market is beginning to split into two distinct groups.
One group is actively operationalizing AI in core claims decisions such as coverage checking.
The other remains in pilot mode — experimenting with AI, but struggling to scale it into production.
In that environment, the greater strategic risk may not be vendor dependency. It may simply be moving too slowly.
The question for insurers is no longer just build or buy. It is whether their organization can industrialize coverage intelligence quickly enough to keep pace with growing policy complexity.
To understand how leading insurers are approaching coverage automation download the report.
To see AI-driven coverage checking in action request a demo.