How to Evaluate an AI Denials Vendor: The 4-Question Test for RCM Leaders

TLDR: How to evaluate an AI denials vendor comes down to four questions, not a feature list: where the AI gets its data, whether it knows your payers or just "payers," what it does when a claim is missing clinical documentation, and how it proposes to measure outcomes against a baseline your own team built. A vendor that answers all four cleanly deserves a one-payer pilot. A vendor that answers with a demo deserves another meeting, later.

Best For: VPs and SVPs of Revenue Cycle and Directors of Payer Operations at US provider, lab, and diagnostics enterprises (1,000 to 15,000 employees) who have a quantified denial problem, a vendor demo on the calendar, and a CFO waiting for a recommendation.

An AI denials vendor is a software provider that applies AI to the identification, prioritization, documentation matching, and appeal drafting of denied claims, usually layered on top of a provider's existing billing and clearinghouse systems. The category is crowded and the demos look alike, which is the problem. Every deck shows a denial reduction on somebody else's claims. The question a revenue cycle leader has to answer is narrower and harder: will this tool's results transfer to my payer mix, my documentation gaps, and my queue, and how would I know within ninety days? This post lays out a four-question test that separates a denials engine from a worklist with a chat window on top.

Why how to evaluate an AI denials vendor is a different question than buying any other RCM tool

How to evaluate an AI denials vendor differs from a normal software purchase because the product's value depends almost entirely on data the vendor does not control: your claim history, your payer contracts, and your clinical documentation. A scheduling tool works the same in every hospital. A denials tool works only as well as the claims and documents it is pointed at, which means the evaluation has to test the fit, not the feature.

The demo runs on clean claims, and yours are not clean

Vendor demos are built on curated claim sets where the documentation exists, the payer rules are known, and the denial reason codes are populated. Real queues are not like that. In Experian Health's 2025 State of Claims survey, 50% of respondents named missing or inaccurate claim data as their top denial driver, 35% named authorizations, and 32% named incomplete registration data. Those three causes are upstream of anything a denials tool touches. If half of your denials start with a bad field at registration, a tool that works the back end is working the wrong end, however good it looks on stage.

The baseline keeps getting worse, too. Kodiak Solutions' 2024 data across more than 2,100 hospitals put the initial denial rate at 11.81% of claims, with medical necessity denials up 5% and requests for information up 5.4% in a single year. McKinsey's 2024 analysis traced the same climb over a longer window: 15% of initial claims were denied by the end of 2023, up from 9% in 2016. A vendor that cannot explain how its results hold up as the denial mix shifts toward medical necessity and documentation requests is describing last year's queue.

Most of the market has not bought yet, so there is little reference data

AI in denials management is still early, whatever the conference floor suggests. KLAS's 2025 research across 3,370 respondents from 1,742 organizations found 17% using AI for denials management, against 79% for ambient clinical documentation. A 2025 Bain survey cited by HFMA put it at roughly one in five providers. Experian's number was 14%. Adoption is now climbing fast, though: a McKinsey report cited by Modern Healthcare in January 2026 found that AI use for denials rose from 23% of health systems in 2024 to at least 60% in 2025. The practical consequence is that most of the reference customers a vendor offers you signed within the last eighteen months. Their results are real but short, and they rarely cover a full payer cycle.

What a VP of RCM can approve, and what the four questions protect

A revenue cycle VP can usually approve a pilot and a process change. Platform spend goes to the CFO. The four questions below are built for that authority: each one can be answered in a procurement meeting, each one has a red flag you can state in a sentence, and together they produce a pilot design the CFO can underwrite. Before any of this, it helps to have an honest map of what AI can and cannot do for claim denials, because a vendor will happily sell you capability in the column where AI does not help.

The 4-Question Vendor Test: how to evaluate an AI denials vendor on what actually transfers

The 4-Question Vendor Test is a procurement method for AI denials tools that asks four things in order: where the AI's data comes from, whether its rules are payer-specific, what the tool does when clinical documentation is missing, and how outcomes will be measured against a baseline the provider built. A vendor passes each question with a specific, checkable answer. A vendor fails it with a demo, a benchmark from another client, or a promise to configure later.

Question 1: Where does the AI get its data?

The data source question decides whether the tool is working your denials or a copy of them. A denials tool that ingests the 835 remittance and 837 claim files directly, pulls denial reason codes as the payer sent them, and reads from the same document repository your appeals team uses is operating on the record. A tool that asks your staff to upload a worklist, or that "enriches" your claims against the vendor's own cross-client database, is operating on a derivative, and every derivative introduces a lag and a reconciliation problem. Ask who owns the mapping between the vendor's denial categories and your reason codes, and who maintains it when a payer changes a code. If the answer is "our implementation team" with no named role on your side, the mapping will drift within two quarters.

Revenue cycle leaders tend to trust their own data team's validated numbers over vendor-entered data, and the instinct is right. The practical test is whether the vendor can reproduce your denial rate by payer from your files before it touches a single claim. If it cannot, its reported results later will not be reconcilable to yours either. The five master tables an RCM data foundation needs are the same tables the vendor should be reading from; if you do not have them yet, the vendor is about to build a private version you will not own.

Question 2: Is the logic payer-specific or generic?

Payer specificity is the question vendors most want to skip, because the honest answer is a list of payers, and the list is usually shorter than the sales deck implies. It matters because denial behavior differs sharply by payer type. The American Hospital Association's 2024 report found commercial claim denials rose about 20.2% from 2022 to 2023 while Medicare Advantage denials rose 55.7% over the same period. Ensemble's 2025 analysis of 80 million transactions found Medicare Advantage inpatient denials up 42% in twelve months while commercial and traditional Medicare stayed roughly flat, and identified nearly 600 recurring denial reasons across payers. A model that treats "payer" as one variable is averaging across behaviors that are moving in different directions.

Ask for the named list of payers and plans the tool has rules or tuned models for, the date of the last policy update for each, and what the vendor does when a payer changes a medical necessity policy mid-quarter. Then ask the harder version: show me the overturn rate your tool achieved, by payer, at a client with a similar Medicare Advantage share. If the vendor can only show an aggregate, the aggregate is hiding the payers where it does not work.

Question 3: What does the tool do when clinical documentation is missing?

Missing-documentation behavior is the question that most reliably separates a denials engine from a text generator. AI cannot recover a clinical fact that was never documented, and a tool that drafts an appeal anyway is manufacturing a weak appeal at scale. The stakes of getting the appeal right are high because appeals work when they are grounded: KFF's analysis of 2023 Medicare Advantage data found that only 11.7% of denied prior authorization requests were appealed, but more than 80% of those appeals were partially or fully overturned. An HHS Office of Inspector General review found 13% of sampled prior authorization denials met Medicare coverage rules and should have been approved. That is the pool a good tool goes after. A tool that cannot tell a grounded appeal from an ungrounded one spends your reviewers' time on both.

The acceptable answer is specific: the tool identifies the missing element (a physician note, a test result, an order date), routes the claim to a named human queue, and does not draft. The red flag is any version of "the AI writes the best appeal it can with the available information." Here the honest limit applies to the whole category: without complete clinical documentation and clean claim data, AI cannot improve appeal outcomes, and no vendor should claim otherwise. Ask to see the routing screen for a missing-documentation case, not the appeal screen.

For a provider with Medicare Advantage inpatient denials rising, multiple payer appeal windows running at once, and a documentation gap on a meaningful share of denied claims, a generic denials tool is not enough. The answer is a dedicated program that does three things: builds the validated denial, appeal, and documentation tables the tool will read from before any tool is connected; designs the human-review handoff for missing-documentation and clinical-judgment cases with named owners and turnaround standards; and runs a one-payer pilot with a pre-built baseline and an exit criterion the CFO signed before the pilot started.

Question 4: How will outcomes be measured, and who built the baseline?

Outcome measurement decides whether the pilot produces a number you can take to the board or a number the vendor takes to its next prospect. The 2025 AI in RCM national survey from Smarter Technologies and Modern Healthcare found 54% of respondents named claims denial management their most burdensome function and 34% named insufficient ROI demonstration as a barrier to adopting AI at all. The barrier is real because most pilots never set a baseline. The baseline has to be yours: denial rate, overturn rate, days from denial to resolution, and write-off rate, by payer, for the ninety days before the pilot, pulled by your data team from your files.

Then insist that the vendor report the same four measures, on the same payer, separately. A single blended "denials reduced 28%" figure can hide a tool that improved overturn rates on easy denials while the hard ones aged past the appeal window. Ensemble's 2025 data showed claims stuck in a request-for-information process took 158 days to pay against 41 days for standard claims, and about 90% were eventually paid exactly as billed. Days to resolution is where that kind of leakage shows up, and a vendor dashboard that reports only denial counts will never surface it. The revenue-at-risk prioritization model gives you the scoring inputs a vendor should be able to expose.

AI denials vendor vs. denials outsourcing vs. a workflow tool

An AI denials vendor, a denials outsourcing firm, and a workflow tool solve different problems and fail in different ways, and the three are routinely confused in the same procurement cycle. An AI vendor sells capability on top of your queue. An outsourcer sells labor and takes the queue away. A workflow tool organizes the queue without changing what happens inside it.

The outsourcing decision has its own math, covered in how AI changed the build-vs-vendor decision in RCM. The workflow decision matters here because of sequencing: an AI tool dropped onto a fragmented flow multiplies the handoffs rather than removing them, which is the argument in why AI on a fragmented RCM workflow makes things worse. If you cannot describe your denial flow as one queue with named owners per step, buy nothing yet.

How to run a one-payer pilot before you sign

A one-payer pilot is the correct scope for an AI denials vendor because it lets your data team validate every number the vendor reports against a baseline it built, on a payer whose behavior it already understands. Running the pilot on your full payer mix hides the payers where the tool underperforms inside an average, and it produces a result nobody on your side can reconcile.

Pick the payer, and pick it for a reason

The right pilot payer has volume, a known policy set, and a denial category where documentation usually exists. Medicare Advantage inpatient status denials are tempting because the number is large, but they combine clinical judgment and payer escalation, which are exactly the steps the tool should route to a human. A commercial payer with a high share of request-for-information and coding-related denials is a cleaner first test. MGMA's 2024 poll of 235 medical group leaders found 60% saw denial rates rise year over year; pick the payer where your own breakdown shows the rise, not the one the vendor suggests.

Build the baseline before the vendor connects

Your data team pulls ninety days of pre-pilot figures by payer: initial denial rate, appeal rate, overturn rate, days from denial to resolution, and write-off rate. The vendor gets read access only after those numbers are frozen. HFMA's May to June 2025 survey of 272 revenue cycle respondents found about half had implemented AI in mid-cycle work such as coding and denials, and the ones who could prove anything had done this step first. If the vendor offers to calculate the baseline for you, decline, politely. That offer is the vendor-entered data this whole test exists to keep out, and it is made in almost every pilot negotiation.

Write the exit criterion before the start date

An exit criterion is a sentence the CFO signs: "If overturn rate on Payer X improves by at least N points and days to resolution falls by at least M days against the frozen baseline, with no increase in write-off rate, we extend to two more payers; otherwise we stop." Without it, the pilot ends in a renewal conversation instead of a decision. The Experian survey reported by AJMC found 82% of providers name denial reduction as a key organizational goal; a goal without an exit criterion is how a pilot becomes a line item.

Common objections from revenue cycle leaders, and what to say to them

Revenue cycle leaders raise the same objections to AI denials vendors, and most of them are correct as stated. The useful move is to turn each one into a test the vendor has to pass rather than a reason to skip the evaluation.

"Every vendor shows a 30% denial reduction. The number is meaningless." Agreed, as an aggregate. The Experian 2025 survey found 69% of AI users reported reduced denials or better resubmission success, which is encouraging and also unverifiable at that level. Ask for the number by payer, at a client with your Medicare Advantage share, measured against that client's own baseline. A vendor that has the number will show it. One that does not will change the subject.

"We are 43% understaffed and a pilot is one more project." Staffing is the reason to run the pilot narrowly, not to skip it. Experian's figure, 43% of providers understaffed in 2025, is why a one-payer scope with a frozen baseline matters: it is roughly two weeks of data-team effort up front and then measurement, and the human-review design in question 3 is what keeps the tool from adding to the queue.

"If I bring in a tool, the board will think denials are solved." The 4-Question Test is the defense. Present the board with what the tool does and what it routes to humans, by design, and with the baseline your team built. The number that goes up on the slide is overturn rate on one payer, not "denials." If anything, the most useful thing the pilot can produce is a clear statement of the share of denials that no tool can fix without upstream documentation work, because that is the budget conversation the board actually needs. For the broader method of testing vendors past the demo, see how to evaluate AI vendors beyond the demo.

This analysis was developed using methodologies and operating experience from Assembly.

Frequently Asked Questions

How do you evaluate an AI denials vendor?

An AI denials vendor is evaluated on four questions: where its AI gets data, whether its logic is payer-specific, what it does when clinical documentation is missing, and how outcomes are measured against a baseline your team built. Specific, checkable answers earn a one-payer pilot. A demo, a cross-client benchmark, or a promise to configure later is a failing answer.

What is the 4-Question Vendor Test for AI denials tools?

The 4-Question Vendor Test is a procurement method that asks an AI denials vendor about data source, payer specificity, missing-documentation behavior, and outcome measurement, in that order. Each question has one acceptable answer and one red flag, so a revenue cycle VP can run it in one meeting and turn the results into a pilot design the CFO can underwrite.

What data source should an AI denials tool use?

An AI denials tool should read directly from your 835 and 837 files, payer denial reason codes, and the same document repository your appeals team uses. Tools that require staff to upload worklists or that enrich claims against a vendor database operate on a derivative of your data, which creates lag and reconciliation problems your data team cannot resolve.

Why does payer specificity matter in an AI denials vendor?

Payer specificity matters because denial behavior moves differently by payer. The American Hospital Association found commercial denials rose 20.2% from 2022 to 2023 while Medicare Advantage denials rose 55.7%. A model that treats payer as one variable averages across behaviors heading in opposite directions, so ask for named payers, policy update dates, and overturn rates by payer.

What should an AI denials tool do when clinical documentation is missing?

An AI denials tool should identify the missing element and route the claim to a named human queue without drafting an appeal. AI cannot recover a clinical fact that was never documented, so a tool that drafts anyway produces weak appeals at scale. Ask to see the routing screen for a missing-documentation case rather than the appeal screen.

How should outcomes be measured in an AI denials pilot?

Outcomes in an AI denials pilot are measured against a frozen pre-pilot baseline your data team built: initial denial rate, overturn rate, days from denial to resolution, and write-off rate, by payer, for ninety days. The vendor reports the same four measures separately on the same payer. A single blended denial-reduction figure hides underperformance on hard denials.

What is the difference between an AI denials vendor and denials outsourcing?

An AI denials vendor sells capability on top of your queue, while a denials outsourcing firm sells labor and takes the queue away. The AI vendor leaves the data, reviewers, and payer knowledge in-house; the outsourcer keeps payer-specific knowledge and typically works on contingency. The outsourcing decision has its own payer-level cost and overturn math.

Why do AI denials vendor demos mislead?

AI denials vendor demos mislead because they run on curated claims with complete documentation and populated reason codes. Experian's 2025 survey found 50% of providers name missing or inaccurate claim data as their top denial driver, and no back-end tool fixes a bad registration field. Test the tool on your files, not the vendor's sample.

How many providers use AI for denials management?

Roughly one in five providers used AI for denials management in 2025. KLAS found 17% across 3,370 respondents, Experian found 14%, and a Bain survey cited by HFMA put it near 20%. McKinsey data reported in 2026 shows adoption rising sharply, which means most vendor reference customers signed within the last eighteen months.

Why should an AI denials pilot run on one payer?

A one-payer pilot lets your data team validate every vendor-reported number against a baseline it built on a payer whose behavior it already understands. A full-mix pilot hides underperforming payers inside an average and produces a result nobody on your side can reconcile. Pick a payer with volume, a known policy set, and denials where documentation usually exists.

Which payer should an AI denials pilot start with?

The best first pilot payer is a commercial plan with a high share of request-for-information and coding-related denials, where documentation usually exists. Medicare Advantage inpatient status denials look attractive because the number is large, but they involve clinical judgment and payer escalation, which are precisely the steps a good tool routes to a human reviewer.

What is an exit criterion in an AI denials pilot?

An exit criterion is a sentence the CFO signs before the pilot starts that names the overturn improvement and days-to-resolution reduction required, with no increase in write-off rate, for the tool to extend to more payers. Without one, the pilot ends in a renewal conversation rather than a decision, and the vendor's dashboard becomes the scorecard.

What are the red flags when evaluating an AI denials vendor?

The red flags are four phrases: "your team uploads the worklist," "our model generalizes across all payers," "the AI drafts the best appeal it can with available information," and "we typically see a 30% reduction." Each one signals a derivative data source, generic logic, ungrounded appeals, or vendor-calculated outcomes, and each fails the corresponding question in the 4-Question Vendor Test.

What cannot an AI denials vendor fix?

An AI denials vendor cannot fix denials caused upstream by bad registration data, missing prior authorization, or clinical documentation that was never written. Experian's 2025 data attributes 50% of denials to missing or inaccurate claim data and 35% to authorizations. The most useful pilot output is often the share of denials no tool can touch.

How does a fragmented RCM workflow affect vendor evaluation?

A fragmented RCM workflow should delay vendor evaluation, because an AI tool placed on a flow with many systems and manual handoffs multiplies those handoffs instead of removing them. If the denial process cannot be described as one queue with a named owner per step, the sequencing answer is to fix the queue first and evaluate tools afterward.

What should a VP of RCM present to the board after a denials pilot?

A VP of RCM should present overturn rate and days to resolution on one payer, measured against the team's own frozen baseline, alongside the share of denials the tool routed to human review by design. That framing prevents the board from concluding denials are solved and opens the upstream documentation budget conversation the organization actually needs.