For decades, the relationship between payers and providers has been defined by misaligned incentives, fragmented data, and operational friction. Yet as financial pressures intensify and value-based care accelerates, that long-standing dynamic is beginning to shift. Increasingly, organizations are embracing integrated “payvider” models that align health plans capabilities with care delivery — not simply to reduce friction, but to improve outcomes, control costs, and deliver a more cohesive patient experience.
AI is emerging as a critical enabler of these aims. By unifying clinical, operational and financial data and embedding intelligence into workflows, AI helps payviders break down silos that once kept payers and providers at odds, creating a shared foundation for collaboration, accountability and performance improvement.
To explore how AI is transforming the payvider ecosystem, Becker’s Healthcare spoke with EXL leaders Will O’Neill, vice president II of healthcare solutions, and Aneesh Menon, assistant vice president of payment services.
Forging payvider alignment
At its core, the payvider model is designed to align accountability for both care delivery and financial performance — helping organizations improve outcomes, enhance the patient experience and lower total cost of care.
Achieving those goals requires more than structural integration. Successful payviders must deliberately align financial incentives, operational processes and clinical systems across what were historically two separate, siloed worlds.
That evolution represents a fundamental shift: from a model where payers primarily managed financial risk and providers focused on delivering care, to a collaborative ecosystem built on shared responsibility and transparency. A critical step in that transition is eliminating data silos and establishing a single, standardized dataset that follows the patient across the entire episode of care.
According to O’Neill, creating this unified dataset requires that payers and providers first agree on the underlying metadata.
“Once there is alignment on the metadata, that can be leveraged by AI to prompt different interventions to derive better outcomes, both from a health perspective and an economic perspective,” O’Neill said.
Creating a common understanding of claims adjudication
Beyond a foundational dataset, achieving alignment requires payviders to reassess the claims adjudication process. This process has been governed by a series of payer criteria including medical policies, reimbursement policies, utilization management policies, contracts and more. Since providers typically work with multiple payers, understanding and aligning with each payer’s requirements can be extremely time-consuming and difficult.
AI can change this by consuming relevant policy information and creating shared visibility into the claims adjudication process for all parties.
O’Neill elaborated on this vision: “With the advent of the AI tools and capabilities that are coming forth today, it’s the first time where I believe we can get a common understanding of what’s to occur in the claims adjudication lifecycle to both the payer and the provider.”
After establishing this common understanding, agentic workflows can significantly reduce the amount of payment integrity activity — enabling faster, more accurate, more collaborative, and less costly environments for payviders.
The future of payment integrity: real-time and predictive decision-making
Historically, payment integrity has functioned as the final checkpoint in the payment value chain and has often involved post-payment audit and recovery.
With the application of AI, payment integrity is shifting upstream. By synthesizing intelligence embedded in payer policies, payment data, operational workflows, and clinical information, AI enables prompt and predictive decisions that help organizations identify and correct issues before claims are paid.
For example, AI can proactively flag misconfigured benefits or deductibles, identify employer groups that are set up incorrectly, and detect abnormal denial patterns.
“In the future, payment integrity won’t be about fixing things after the fact — it will be about identifying and incorporating fixes at the source,” O’Neill said. “That’s going to be the job of payment integrity over the next 10 years.”
Right now, EXL sees many organizations undertaking AI pilots and proof of concepts. Mr. Menon observed that leading payviders will quickly move from pilots to using AI strategically in the payment cycle and beyond.
“The leaders will take it to another level,” he said. “The ultimate aim in bringing payviders together and integrating AI goes beyond payment integrity; it is to drive better patient outcomes. The sky is the limit.”
