The Next Dental RCM Stack Is Automation + Analytics + AI. Not Another Point Solution.

Forvis Mazars recently published an analysis on leveraging automation, analytics and AI in the revenue cycle.

The piece is aimed broadly at healthcare, but the implications for dental may be even more interesting.

Dental revenue cycle management is still unusually fragmented.

A single insurance payment can involve a practice-management system, clearinghouse, payer portal, eligibility tool, imaging system, EOB, bank account, spreadsheet and human billing team before the revenue cycle is actually complete.

Technology has entered nearly every one of those workflows.

But most of the technology has entered them one task at a time.

That may be the next thing to change.

The future dental RCM stack is unlikely to be a collection of disconnected tools that automate individual clicks.

It increasingly looks like a connected system built from three different capabilities:

automation to execute the work, analytics to decide where attention is needed, and AI to handle the ambiguity between the two.

Explore the Revenue Cycle category and broader dental technology market map on Avized.

Healthcare RCM Still Has a Performance Problem

Forvis Mazars' broader 2026 healthcare executive research found that only 45% of executives rated their revenue cycle function strong or very strong, down from 50% the prior year.

The most common pain points included denials management, patient collections and eligibility verification. At the same time, 63% cited payer mix and declining reimbursement as a financial sustainability concern, while 44% specifically identified revenue cycle.

Those are hospital and health-system findings, not dental benchmarks.

But the categories should sound familiar to nearly every dental operator.

Dental groups spend enormous administrative effort on:

• eligibility and benefit verification,
• treatment estimates,
• claim creation and submission,
• attachments,
• claim-status follow-up,
• denials,
• appeals,
• aging accounts,
• EOB processing,
• payment posting,
• reconciliation,
• patient balances,
• and figuring out what a payer actually owes.

The strategic opportunity is not simply to add AI to each activity.

It is to connect them.

Automation, Analytics and AI Do Different Jobs

The three technologies are often grouped together, but they solve different problems.

Automation executes predictable work

Traditional automation is strongest when the rules are known.

In dental that can mean:

Retrieve an eligibility response. Send a claim. Upload an attachment. Check claim status. Import an ERA. Post a payment. Route an account based on age or balance.

These tasks are repetitive, high-volume and relatively structured.

If the rule is:

When X happens, do Y.

automation is often the right tool.

And dental still has enormous amounts of this work available to automate.

Analytics tells the organization where the problem is

Automation alone does not answer a more important question:

What should we fix?

That requires analytics.

A DSO may know it has $14 million in outstanding insurance AR.

That number is almost useless by itself.

The more useful questions are:

Which payer is driving the increase?

Which locations?

Which CDT codes?

Which denial reason?

Which aging bucket?

Which provider?

Did the problem begin after a payer-policy change?

Is the issue claim submission, documentation, authorization, contractual adjustment, reimbursement or payment posting?

Forvis Mazars describes revenue-cycle analytics as a way to identify trends, drill into claim-level data and find root causes rather than merely creating another work queue.

That distinction is especially important in dental.

A dashboard that tells a team it has more denials is reporting history.

A useful analytics layer tells the organization why the denials increased and what operational change would prevent the next thousand.

AI Handles the Messy Middle

Then comes AI.

Healthcare revenue cycles contain enormous amounts of information that do not fit clean deterministic rules.

Dental may have even more of it relative to the size of the operation:

payer policies, narratives, EOB remarks, scanned documents, clinical notes, attachment requirements, benefit limitations, frequency rules and inconsistent portal responses.

This is where AI can potentially fill the gap between automation and analytics.

Consider a denied crown claim.

Traditional automation can retrieve the denial.

Analytics can tell the DSO that D2740 denials for a specific payer increased materially this month.

AI can potentially help interpret the denial, compare it with the supporting documentation and applicable payer requirements, identify the likely reason, recommend the next action and prepare the account for review.

Those are three fundamentally different functions.

Automation moves the account.

Analytics identifies the pattern.

AI interprets the context.

The real opportunity emerges when all three operate together.

Dental Has Been Buying the Pieces Separately

This is one reason the dental technology market has become so crowded.

There are products for eligibility.

Claims.

Attachments.

Payment posting.

Denials.

AR follow-up.

Patient payments.

Voice AI.

Analytics.

Payer policies.

Credentialing.

Fee schedules.

Contract management.

Each can solve a legitimate problem.

But from the operator's perspective, there is still only one revenue cycle.

A rejected eligibility response can eventually become a patient-balance problem.

A missed payer requirement can become a denial.

A denial can become aged AR.

A reimbursement discrepancy can become an incorrect adjustment.

An EOB can expose a fee-schedule problem.

Those are not independent workflows.

They are different stages of the same financial transaction.

The more compelling architecture therefore looks less like:

12 tools automating 12 tasks

and more like:

one intelligence layer continuously observing the entire revenue cycle.

The Goal Should Be Fewer Touches, Not More Software

This is where dental RCM vendors should be measured differently.

The question should not be:

How many AI features do you have?

It should be:

How many human touches did you remove between treatment and cash?

Take insurance verification.

A system that downloads benefit information has automated something.

But if an employee still has to interpret the output, enter information into the PMS, determine whether a limitation applies and explain the estimate to the patient, the workflow has not been automated.

The same applies to denials.

Categorizing a denial with AI is useful.

Automatically fixing the root cause, generating the needed documentation, resubmitting the claim and learning whether the correction worked is much more valuable.

That is the progression dental technology should be moving toward:

visibility → recommendation → execution → verification.

The last step is particularly important.

The system needs to know whether its action actually produced the intended outcome.

Denial Management Should Become Denial Prevention

Forvis Mazars makes another useful distinction in its healthcare analytics work: the objective of denial analytics is not merely to create a better denial work queue. It is to understand the underlying drivers well enough to prevent future denials.

That principle translates directly to dental.

A large billing organization can become extremely efficient at working denied claims and still have a bad revenue cycle.

If the same denial continues entering the queue every day, the organization has automated rework.

The better system asks:

Why did this claim deny?

Has it happened before?

Does it cluster around a payer, office, provider or procedure?

Was an attachment missing?

Did a payer policy change?

Was the eligibility information wrong?

Is the PMS configured incorrectly?

Can the issue be corrected upstream?

Then the best outcome is not faster AR follow-up.

It is no AR follow-up at all.

This Changes What Dental RCM Employees Do

None of this means every billing employee disappears.

It changes where humans should spend their time.

An RCM team should not need highly trained employees repeatedly checking whether a claim has been received.

Software should do that.

The employee should be working the unusual account that requires judgment.

The same principle applies throughout the cycle.

Machines handle:

volume, repetition and monitoring.

Humans handle:

exceptions, judgment, escalation, negotiation and complex clinical context.

That is an important distinction because the goal of AI in dental RCM should not simply be headcount reduction.

It should be higher revenue-cycle output per employee.

A ten-person team capable of managing the work that previously required twenty people has significant operating leverage.

A ten-person team still doing the same work but looking at a new AI dashboard does not.

DSOs Have an Additional Advantage: Data

This becomes more powerful at scale.

A 100-location DSO has something a five-office group does not:

thousands or millions of repeated payer interactions.

Every eligibility response, claim, denial, EOB, payment and appeal becomes another data point.

That creates an opportunity to understand payer behavior at a level an individual practice cannot.

A sophisticated system can eventually answer questions such as:

Which payers are slowing payment?

Which carriers are denying specific codes more frequently?

Which offices have unusually high denial rates?

Which payer policies changed?

Which claims are most likely to pay if appealed?

Which accounts are unlikely to justify additional labor?

Which contracted rates appear incorrect?

Where is the organization systematically under-collecting?

At that point, the revenue cycle stops being merely an administrative department.

It becomes an intelligence system.

This Is Where Dental RCM and Payer Intelligence Start to Converge

There is another logical consequence.

Revenue-cycle data cannot be fully understood without payer intelligence.

If a claim pays $742, the system needs to know whether $742 was correct.

If the payer denies a service, the system needs to know what policy governed that decision.

If reimbursement declines, the organization needs to know whether the issue is utilization, fee schedules, network participation, payer behavior or an operational error.

The traditional separation between:

billing

and

managed care

therefore begins to break down.

Future revenue-cycle platforms may increasingly combine claims performance with payer policy, contract terms, fee schedules, network relationships, negotiated reimbursement and historical payer behavior.

That creates a much richer system than AR management alone.

It begins to answer not just:

Did we collect the claim?

but:

Did we collect what we should have collected?

The Winning Dental RCM Platform May Be the One That Closes the Loop

Forvis Mazars frames technology, analytics and AI as tools for improving revenue-cycle performance.

Dental has an opportunity to build the same model around a much more fragmented operating environment.

But the winning architecture probably will not stop at automation.

It will look something like:

Observe → Detect → Prioritize → Act → Verify → Learn.

A claim denies.

The platform identifies why.

It determines whether the issue is isolated or systemic.

It takes the appropriate action.

It watches for the payer response.

It confirms whether the claim paid correctly.

And it changes the upstream workflow to prevent the same problem from occurring again.

That is not a better billing queue.

It is a closed-loop revenue cycle.

Avized Take

Dental RCM is entering the same transition happening across healthcare—but dental may ultimately move faster.

The first wave digitized the work.

The second wave automated individual tasks.

The next wave will connect automation, analytics and AI into one operating system.

The important question for dental technology buyers is therefore changing.

It is no longer:

Does this product use AI?

Or even:

What task does this automate?

The better question is:

What part of the revenue cycle no longer requires a person after this technology is implemented?

The biggest opportunity is not an AI eligibility company, an AI denial company or an AI payment-posting company operating independently.

It is a system that understands the entire path from patient coverage → treatment → claim → payer decision → payment → reconciliation, learns where revenue is leaking and increasingly fixes the problem before an employee ever touches the account.

That is where dental RCM becomes more than automation.

It becomes infrastructure.

Related Avized research: Revenue Cycle · Dental technology market map

Source: Forvis Mazars, Leveraging Automation, Analytics, & AI in the Revenue Cycle, October 2026.