As insurers move from AI experimentation to operational deployment, one question keeps resurfacing: Should insurers build AI-driven coverage checking solutions internally – or buy specialist expertise?
The question for insurance carriers and MGAs 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.
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 proportion of insurers is beginning to operationalize AI in coverage determination – more commonly for straightforward claims – 44% still report zero automation in coverage checks today, with complex claims coverage proving especially challenging.
Coverage intelligence is becoming a critical capability in claims.
The real question is, how do insurers industrialize AI at scale?
The appeal of building internally
Building internally can appear attractive at first glance. It offers:
- Control over models and data
- Tailoring to bespoke claims automation 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 standard 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.
For in-house development teams, 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, former Group Chief Claims Officer, 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 sometimes justify internal development. However, most achieve faster impact and shorter time to value through co-building partnerships.
Speed – AI capability can take years to develop internally, whereas solutions provided by insurance claim specialist Insurtech firms can deploy within months.
Capability – Few insurers currently have large-scale AI teams with deep insurance claims expertise.
Strategic differentiation – Some insurers view AI capability as core intellectual property. Others see it as ever-evolving operational infrastructure best sourced and maintained 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 acknowledges that operationalising AI in insurance is not a one-time event, but a continuous process of refinement. By partnering with specialists, carriers can skip the “trial and error” phase of model building and focus their internal resources on strategic integration.
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
One misconception influencing the build-versus-buy debate is that AI’s primary value lies in reducing adjuster headcount.
But claims leaders increasingly see the opportunity differently. According to Ian Thompson, “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), which delivers greater financial impact than expense reduction alone.
The bigger risk: standing still
The insurance sector is beginning to split into two distinct groups.
One is actively operationalizing AI in core claims decisions, including coverage checking.
The other remains in pilot mode – experimenting with AI, but in many cases stalling, and struggling to scale AI into production across the enterprise. In these cases, the greater strategic risk may not be vendor dependency – it may simply be moving too slowly.