Innovaccer 2026 Payer AI Report: Nearly 80% of Insurers to Buy or Co-Develop AI Capabilities – Image for illustrative purposes only (Image credits: Pixabay)
Health plan leaders have quietly reversed course on how they intend to bring artificial intelligence into their operations. Where many once favored building tools in-house, a fresh survey shows most now lean toward buying capabilities or working alongside vendors. The change reflects both the complexity of modern data systems and the pressure to deliver measurable results quickly.
From Internal Builds to Collaborative Models
The latest findings from Innovaccer’s survey of 63 C-suite executives at major insurers reveal a clear pivot. Nearly 80 percent of respondents now plan to purchase or jointly develop AI solutions rather than create them entirely on their own. This marks a notable departure from late 2024, when 78 percent of the same group reported efforts to handle development internally.
Executives appear to recognize that specialized vendor expertise can accelerate progress in areas such as bias detection and regulatory compliance. At the same time, they want to retain control over their own data during training. The result is a growing preference for co-development arrangements that blend payer-specific information with outside technical strengths.
Investment Plans and Near-Term Priorities
Financial commitments remain substantial. Roughly three-quarters of payers expect to allocate more than $10 million over the next three to five years toward AI initiatives focused on payment accuracy and care outcomes. Nearly one-third of national plans have already set aside $50 million or more for these efforts.
In the short term, risk stratification and predictive analytics top the list for 60 percent of respondents. These applications help identify high-risk members and forecast utilization patterns. Yet leaders also keep an eye on longer horizons, with 62 percent naming personalized member navigation as the single most important use case for sustained success.
Persistent Gaps Between Ambition and Readiness
Despite the surge in planned spending, operational readiness lags. Eighty-six percent of executives acknowledge they are not fully prepared to scale AI across their organizations. The primary obstacles center on data infrastructure rather than a lack of vision or funding.
Interoperability ranks as the leading barrier, cited by 46 percent of those surveyed. Fragmented legacy systems make it difficult to incorporate external data sources such as social determinants of health or provider cost information. Real-time data access and cloud architecture limitations follow closely behind. Many payers continue to operate with siloed records that predate current AI requirements, slowing integration efforts even when the strategic intent is clear.
Co-development offers one practical path forward. More than half of respondents favor collaborative models over ready-made solutions, allowing them to maintain oversight while gaining access to proven technical frameworks. This approach also helps address concerns around regulatory compliance and model fairness without requiring payers to build every safeguard from scratch.
AI as a Bridge to Value-Based Care
Health plans increasingly see artificial intelligence as a means to shift from volume-driven models to those centered on outcomes. Personalized member navigation stands out as the long-term priority because it aims to connect individuals with the right care at the right moment. When executed well, such tools can reduce unnecessary spending while supporting better clinical results.
External pressures have added urgency. Rising medical loss ratios and the rollout of HCC V28 in Medicare Advantage have shortened the timeline for digital upgrades. Payers now operate under what the report describes as a mandate for speed, where delays in AI adoption carry direct financial consequences.
What matters now
- Most payers have moved past the build-versus-buy debate and favor partnerships.
- Significant spending is planned, yet infrastructure gaps remain the chief constraint.
- Personalized member navigation emerges as the pivotal long-term application.
The survey underscores that success will depend less on the size of AI budgets and more on the ability to unify data across outdated systems. Organizations that address these foundational issues stand to gain an edge in both operational efficiency and member outcomes over the coming years.
