What Happens When Payers and Providers Both Have AI: The New Claims Adjudication Landscape

When payers and providers both use AI in claims workflows, the important question is not which side has the more impressive model. It is whether each decision can be explained, supported by the record, challenged when necessary, and reconciled to the applicable coverage and payment rules.

Publisher and evidence note: QuickIntell sells revenue cycle software. This article is scenario analysis, not an independent performance comparison or an original claims dataset. Earlier unsourced national cost totals, adoption forecasts, and outcome percentages have been removed. The scenarios below are possibilities, not predictions with a proven timetable.

The current claims-adjudication problem

A provider assembles the clinical and administrative information supporting a claim. A payer evaluates that claim within the relevant benefit, coverage, contract, and processing rules. Both sides can use software to organize information, detect inconsistencies, route work, and propose decisions. The presence of AI does not determine whether a particular decision is correct.

Different events require different evidence: a submission rejection, a prior authorization denial, an initial claim denial, a partial payment, and a final appeal decision are not interchangeable. Separating them makes it possible to identify whether a problem is in data transmission, documentation, policy interpretation, or payment reconciliation.

What provider-side AI can assist with

Pre-submission review

A system can flag missing fields, inconsistent claim information, or patterns associated with prior denials. Treat a prediction as a review signal. It should identify the input evidence, model or rule version, limitations, and the action that requires approval. A predicted denial is neither a coverage determination nor permission to change clinical documentation.

Payer-specific checks

Payer rules differ by plan, jurisdiction, service, and effective date. Maintain source-linked policy records and distinguish an explicit published rule from an inferred pattern in historical responses. A pattern may change because the incoming case mix changed, not because the payer silently changed its policy.

Appeal preparation

AI may help organize the denial notice, source documentation, timeline, and relevant policy material into a draft. The responsible reviewer still needs to verify the facts, authority, filing route, and deadline. A plausible-sounding argument is not supporting evidence, and generated citations must be checked before submission.

Coding support

A coding suggestion must follow the documented service and applicable coding rules. It should not be selected merely because a model predicts a higher payment or fewer denials. Preserve the clinician/coder review trail and the original record.

Scenario 1: Adversarial escalation

In an adversarial scenario, organizations invest in increasingly complex automated checks and counter-checks without improving the information exchanged. Providers may receive more requests for documentation, while payers may receive more repetitive appeals. Faster activity can still produce more work if the underlying dispute remains unresolved.

Watch for rising repeated submissions, duplicate contacts, unexplained edit changes, or appeals that fail for the same missing evidence. Those are reasons to investigate workflow quality rather than simply increase automated volume. The cost of this scenario would need to be measured; this article does not assign it a national dollar total.

Scenario 2: Collaborative improvement

In a collaborative scenario, both sides exchange more complete information and make requirements easier to interpret. A missing-document response can be routed to the right owner with the original request attached. An authorization can be reconciled to its service details before the claim is prepared. A payment variance can be investigated against the agreed contract.

The practical requirements are shared identifiers, usable status information, source provenance, access controls, and an agreed exception process. Machine-to-machine exchange alone does not establish agreement about medical necessity or contract interpretation. Measure whether additional connectivity actually reduces repeated work and resolves cases correctly.

Scenario 3: Regulatory and contractual change

Rules may change what information must be exchanged and how affected organizations report their processes. Separate effective requirements from proposed rules and hypothetical policy choices.

For example, CMS-0057-F establishes certain prior authorization reporting and denial-reason requirements beginning in 2026, with API requirements generally following in 2027 for impacted payers. The rule has a defined scope and does not create universal real-time claims settlement or authorize autonomous clinical decisions. CMS final-rule fact sheet

The initial public metrics concern the previous calendar year. A report posted in 2026 must not automatically be labeled a 2026 outcome study. CMS prior authorization FAQ

A future transparency mandate, audit requirement, or standardized exchange could change the workflow. Until adopted and applicable, those ideas belong in scenario planning, not in a statement that every payer is already required to comply.

The data advantage depends on quality and rights

More data is not automatically better evidence. Historical claims can contain coding errors, inconsistent labels, missing final dispositions, and changes in policy or patient mix. A model that repeats those patterns may become more confident without becoming more correct.

Evaluate the provenance and permitted use of training and operational data, the treatment of missing outcomes, the separation between customers, and the process for correction. Ask how performance is evaluated on cases outside the training sample and how results vary by the relevant cohorts.

For operational decisions, prefer evidence that is traceable to the current case. Aggregate patterns can help prioritize review, but they should not replace an individual clinical record, payer notice, or contract.

Revenue cycle strategy under any scenario

Use the same foundations whether relationships become more adversarial or more collaborative:

1. Define denial, appeal, write-off, and collection metrics consistently.
2. Keep policy sources and effective dates attached to operational rules.
3. Assign human approval for consequential coding, clinical, financial, and patient-facing actions.
4. Require logs that show what the automation proposed, what was approved, and what was transmitted.
5. Monitor errors, unresolved exceptions, overrides, and rework as well as throughput.
6. Preserve export rights and a functioning fallback when a model or connection is unavailable.

A pilot should start with a bounded workflow and an agreed comparison method. Neither an AI-native label nor a short implementation promise establishes that the product is suitable for your data, staff, and payer mix.

What would automated settlement require?

More automated payment reconciliation is a plausible direction, but autonomous agreement between payer and provider systems is not established by this article. It would require compatible data, explicit authority, transparent rules, dispute handling, security controls, and a clear allocation of responsibility when the systems disagree.

Treat demonstrations as demonstrations. Ask which actions are live, which require approval, which are simulated, and which remain on the roadmap. Do not base a procurement decision on a forecast that a particular adoption milestone will occur by a particular year.

Questions to take into a vendor evaluation

• Can the vendor reconstruct a contested decision from original inputs?
• Can a reviewer override the recommendation without losing the audit history?
• Are performance claims linked to a defined cohort, dates, denominators, and limitations?
• Does the contract distinguish software responsibilities from your organization's clinical and compliance responsibilities?
• What happens to active cases when an integration fails or a policy cannot be verified?

Use the denial measurement guide, revenue cycle waste guide, and vendor evaluation checklist to structure that discussion.

Discuss a workflow evaluation with QuickIntell. This article does not promise an appeal success rate, denial reduction, implementation date, or financial return.