Key Takeaways
- Nearly 80 % of health‑insurer leaders now favor purchasing vendor‑built AI tools instead of developing internal AI capabilities.
- The survey of 63 payer executives (mid‑Dec 2025 – mid‑Jan 2026) shows that 78 % already use AI to improve care, while 75 % plan to invest an average of $10 million over the next three‑to‑five years, with a third earmarking at least $20 million.
- Despite strong enthusiasm, 86 % of respondents say they are not yet ready to operationalize AI at scale; the top barrier is interoperability, followed by real‑time data access, inadequate data architecture and limited cloud capabilities.
- Innovaccer CEO Abhinav Shashank stresses that “context and data infrastructure” are the decisive factors for scaling AI, warning that without them most AI investments will fail to deliver enterprise‑wide value.
- The most compelling AI use case identified by payers is personalized member navigation, cited by 62 % as critical to payer success over the next three‑to‑five years.
- Shashank observes a cultural shift: payers are moving from “buying AI” to “using AI to solve specific problems.”
Overview of Innovaccer Survey Findings
Innovaccer’s recent survey of health‑insurer leaders reveals a decisive turn toward external AI solutions. Conducted between mid‑December 2025 and mid‑January 2026, the poll captured insights from 63 executives representing regional plans and national carriers, including senior and C‑suite officers. The results indicate that almost four‑out‑of‑five payers (≈80 %) now prefer to implement vendor‑built artificial intelligence tools rather than invest in home‑grown capabilities. This trend underscores a broader market realization that AI’s value lies less in internal development and more in deploying proven, scalable platforms.
Shift to Vendor‑Built AI Solutions
When asked why they are outsourcing AI, respondents highlighted speed, reduced risk, and access to specialized expertise. Innovaccer CEO Abhinav Shashank explained to Fierce Healthcare that the movement reflects a focus on “how to truly operationalize AI.” He noted, “The reality of it is the technology is going to be a massive addition to how payers operate.” By turning to established vendors, payers aim to bypass the lengthy maturation curve associated with building AI from scratch while still gaining the analytical power needed to improve care delivery and operational efficiency.
AI Adoption Among Payers
The survey shows that AI is no longer a peripheral experiment for most insurers. Nearly 78 % of respondents report already using AI solutions to enhance care, and three‑quarters say they are “aggressively pursuing or progressively experimenting” with AI in care innovation. This widespread uptake signals that payers view AI as a core component of their strategic toolkit, not merely a novelty. The adoption spans functions such as claims adjudication, risk stratification, and member engagement, reflecting a holistic embrace of machine‑learning technologies.
Planned Investments and Financial Commitment
Financial commitment mirrors the enthusiasm for AI. Seventy‑five percent of respondents said they plan to spend an average of $10 million on AI over the next three to five years, with a notable third earmarking at least $20 million for the same period. These figures illustrate that payers are prepared to allocate substantial budgets to acquire, integrate, and scale AI platforms. The anticipated spending also suggests expectations of measurable returns—whether through cost savings, improved health outcomes, or enhanced member satisfaction.
Readiness Gaps and Infrastructure Barriers
Despite the optimism, a significant readiness gap persists. Eighty‑six percent of respondents admitted they are not fully ready to operationalize AI at scale. Shashank identified the core issue: “Enterprises don’t necessarily have the core data infrastructure and context infrastructure to be able to operationalize these AI frameworks at scale today.” The survey’s top‑cited barrier was interoperability, followed by challenges in real‑time data access, inadequate data architecture, and limited cloud capabilities. Many payers still rely on legacy systems and siloed data stores, which impede the seamless flow of information necessary for AI models to learn and act effectively.
The Critical Role of Data and Context Infrastructure
Shashank emphasized that the readiness of the industry is “very dependent” on systems having the right data and context infrastructures. He warned, “If you do not have that data and context infrastructure, most of the efforts and investments in AI are not necessarily going to scale for you as an enterprise.” In practical terms, this means payers must invest in modern data lakes, standardized APIs, and robust governance frameworks before AI can deliver consistent, actionable insights. Without such foundations, even the most sophisticated algorithms risk producing biased or incomplete outputs, undermining trust and limiting adoption.
High‑Impact Use Case: Personalized Member Navigation
When asked to prioritize AI applications, 62 % of respondents singled out deploying AI to support personalized member navigation as “the use case critical to payer success” over the next three‑to‑five years. This focus reflects a shift toward proactive, member‑centric care—using AI to guide individuals through benefits, preventive services, and chronic‑disease management pathways. By tailoring interactions to each member’s health profile and preferences, payers aim to improve engagement, reduce avoidable utilization, and ultimately lower costs while enhancing satisfaction.
From Buying AI to Solving Problems with AI
Shashank concluded with an observation about the evolving mindset among payers: “A fundamental shift that we are starting to effectively see [is] that instead of people buying AI, people are now trying to solve problems using AI.” This statement captures the transition from treating AI as a technology purchase to viewing it as a problem‑solving tool embedded within clinical and operational workflows. The shift suggests that future AI initiatives will be more tightly aligned with specific business objectives—such as reducing readmissions, improving STAR ratings, or optimizing network performance—thereby increasing the likelihood of measurable impact.
Conclusion
The Innovaccer survey paints a clear picture: payers are enthusiastic about AI’s potential, are willing to allocate multi‑million‑dollar budgets, and overwhelmingly favor vendor‑built solutions to accelerate adoption. Yet, the path to scaling AI is hampered by entrenched data silos, interoperability gaps, and insufficient cloud‑native infrastructures. Success will hinge on payers’ ability to modernize their data foundations, integrate context‑rich information, and deploy AI strategically against well‑defined use cases—particularly personalized member navigation. As Shashank aptly puts it, the true test lies not in acquiring AI, but in harnessing it to solve real problems that drive better health outcomes and operational excellence.
https://www.fiercehealthcare.com/payers/nearly-80-payers-prefer-vendor-built-ai-solutions-innovaccer