AI is often framed as a choice between helping people and automating work. In claims payment integrity, that framing misses the point.
There are two valid ways to apply AI: assisting humans within existing workflows and orchestrating work across workflows. Both introduce real intelligence. Both improve outcomes.
What determines success is not which path an organization chooses, but whether both are built on the same underlying foundation.
Claims payment integrity isn’t just about numbers or data. It’s about documents, policies, and rules that govern what should be paid and what shouldn’t.
Provider contracts, coverage policies, clinical guidelines, and regulatory requirements define the “correct” outcome for each claim. Even when AI tools identify anomalies or risk, the final determination still depends on interpreting these complex, text-rich sources in context. That’s why many plans find claims workflows cumbersome and manual – the work lives in interpretation and application, not in abstract analytics alone.
Attempts to automate without this grounding can fracture decisions, creating inconsistent outcomes or unexpected denials. For example, industry reports indicate that poorly implemented AI and algorithmic tools have been linked to rising denial rates and regulatory scrutiny because they can lack nuance or enforce rules without human oversight.
Before discussing AI at scale, it's important to recognize that claims integrity is a document-centric business. The question isn’t whether AI can help – it’s how that help touches the text, rules, and decisions that drive the work.
AI can help in at least two practical ways:
Assistive AI supports human reviewers inside existing workflows. It might:
This model improves speed, reduces routine work, and makes human decisions more consistent. It accepts the current workflow and augments it, not replaces it.
Orchestrated AI takes a broader view. It embeds intelligence into the flow of claims work so that:
This is more than the automation of one task. It rewires how work moves across teams and systems.
Neither approach eliminates humans – assistive AI keeps humans in the loop, and orchestrated AI makes human roles more strategic by reducing low-value work.
Despite surface differences, both approaches require the same core foundation:
Decision logic rooted in policy and contracts:
AI that assists or orchestrates must interpret the same underlying content – provider contracts, benefit language, billing rules, clinical guidelines – in a way that is auditable and defensible.
Traceable, explainable steps:
Whether a claim is routed for review or auto-approved, there must be a recorded trail of why that decision happened.
Human oversight in areas of nuance:
Many industry experts emphasize that AI works best when paired with human expertise, especially for nuanced decisions where policy meets clinical or contractual interpretation.
If these foundations are inconsistent across workflows, the organization ends up with siloed implementations that cannot scale together.
As health plans expand AI beyond pilots, they often run into a familiar problem: inconsistency.
If one team’s logic for identifying overpayments differs from another’s, or if contract terms are read differently across tools, the result is fragmentation – inconsistent outcomes, unclear governance, and higher risk.
Shared decision logic changes that. When the same interpretable foundation powers multiple workflows:
Industry leaders reported embedding AI across the entire payment integrity lifecycle to accelerate precision and reduce waste, but they emphasize that success depends on harmonizing decision logic and workflows, not just stacking point tools.
If AI is deployed in isolated silos, it might help one team but not the enterprise. The real opportunity is in building foundational components that can be reused.
These components include:
With these in place, organizations can start with assistive applications where most useful, then progressively orchestrate broader workflows where risk is manageable, and value is clear.
When AI support is built on a shared foundation, each new use case doesn’t reinvent logic – it compounds value.
When AI is built on a shared foundation, organizations gain flexibility without sacrificing control.
If your organization is piloting AI in claims but struggling to scale, let’s discuss the underlying decision-making foundation. A short working session can clarify whether your current approach is built to compound or fragment over time.