Dental's Next Value-Creation Era: Why AI Must Prove Its ROI for DSOs

For years, a dental support organization's growth story could be told in one number: how many practices it added. That is becoming an incomplete measure of enterprise value.

In a new analysis of dental value creation, investment bank Lincoln International argues that acquisition activity still matters, but investors increasingly care about what a platform does with the practices it already owns or supports: organic growth, clinician stability, standardized operations and demonstrable financial returns.

For dental technology vendors, the implication is straightforward: a feature list is not a value-creation strategy. The question is whether a tool consistently improves collections, patient access, treatment completion or clinical productivity across a real operating footprint.

What is changing in DSO economics?

The traditional dental consolidation model paired practice acquisitions with centralized support functions and the expectation of better operating leverage over time. That remains a viable strategy, but higher financing costs and more selective buyers make it harder to rely on deal volume or multiple expansion alone.

Lincoln's analysis identifies a growing distinction between size and quality of earnings. A platform that adds locations without improving practice-level performance may be less compelling than one that can demonstrate durable same-store growth and a strong operating infrastructure.

The diligence conversation is therefore becoming more specific:

• Can existing offices grow production and collections without relying on acquisitions?
• Are clinician retention and recruitment sustainable?
• Do central services reduce work at the practice level or merely move costs elsewhere?
• Does the technology stack make acquired offices easier to integrate?
• Are performance gains measurable and repeatable across locations?

This complements Avized's analysis of changing dental ownership patterns: the more practices operate within groups, the more consequential decisions about software, workflows and data infrastructure become.

RCM is a direct lever for practice-level value

Revenue cycle management is one of the clearest examples in Lincoln's analysis. Improvements in insurance verification, claims handling and patient accounts receivable can create incremental earnings from an existing patient and production base.

Lincoln points to AI-enabled insurance verification as an early source of administrative leverage. It reports that The Smilist has seen time savings approaching one full-time-employee equivalent in many offices, along with reduced centralized RCM workloads.

That is a reported operator outcome, not a guaranteed result for every DSO or a controlled cross-industry benchmark. The distinction matters. Verification automation only translates into value if the underlying process is reliable and downstream outcomes improve.

The questions worth measuring are broader than minutes saved:

• Verification quality: Are benefit estimates more complete and accurate before appointments?
• Revenue protection: Are preventable eligibility, coverage and documentation issues declining?
• Collection performance: Are accounts receivable and cash conversion actually improving?
• Capacity recovered: Is front-desk time being redirected to scheduling, patient communication and other useful work?
• Net economics: What is the measurable benefit after implementation costs, exceptions and vendor fees?

For vendors selling into multi-location dentistry, a credible ROI case must connect the automation to these operating outcomes. Explore Avized's dental RCM automation analysis and insurance verification software guide for the related product landscape.

Treatment completion may be an even larger opportunity

Cutting administrative expense is only half of the equation. Better use of existing patient demand and clinical capacity can also drive organic growth.

Lincoln cites dental industry benchmarks of 61% case acceptance versus 49% treatment completion. The gap illustrates the difference between a patient agreeing to treatment and actually completing it.

That is important for how practices evaluate patient-engagement technology. Automated reminders and follow-up should not be judged only on messages sent or appointments booked. The business and patient-care questions are whether diagnosed treatment gets scheduled, barriers to care are resolved and patients complete recommended treatment.

Relevant capabilities include patient communication, scheduling, treatment visualization, financing workflows and follow-up of unscheduled care. However, the specific 61% and 49% figures come from benchmarks cited by Lincoln; they should not be treated as universal rates for every practice or a forecast of achievable improvement.

AI cannot fix a fragmented operating model by itself

One of the strongest points in Lincoln's argument is that technology works best when layered onto a sound operating foundation.

A DSO may have multiple practice management systems, different eligibility workflows, inconsistent payer data and varied reporting definitions across locations. Installing an AI product into each office does not automatically resolve those differences.

To demonstrate enterprise value, technology buyers should ask three practical questions.

1. Does it fit the workflow? A successful pilot should not depend on extra manual steps or a few unusually engaged employees.

2. Does it change an outcome? Time savings can be useful, but improvements in collections, case completion, clinician productivity or patient experience are stronger evidence of value.

3. Can it scale? A result at five offices should be evaluated again across different geographies, payer mixes, specialties and practice-management systems before extrapolating to 100.

The point is not to buy fewer tools at all costs. It is to build a stack whose components work together and whose benefits can be measured. Avized's 10–100-location DSO technology-stack guide outlines a framework for that evaluation.

What this means for dental AI vendors

For companies building insurance, scheduling, clinical AI, analytics and patient-engagement products, the buyer's questions are getting harder. Demonstrations of model capability are not a substitute for implementation evidence.

A more compelling commercial case will show:

• Baseline performance and a clear definition of the problem
• The cost and disruption of deployment
• Verified post-deployment operating changes
• How exceptions and human oversight are handled
• Results segmented by office type rather than only an enterprise average
• Whether performance persists over multiple reporting periods

Vendor-provided case studies can be useful signals, but buyers should distinguish reported outcomes from independently validated or controlled results.

For DSOs and private-equity sponsors, the opportunity is to make vendor selection part of a broader value-creation plan: choose priority metrics, standardize the workflow, deploy technology where appropriate, and revisit the economics after adoption.

Avized take: operational intelligence is becoming an investment criterion

Lincoln's thesis is fundamentally about the next phase of dental investing. Acquisitions will continue, but the strongest platforms may distinguish themselves by making their current offices consistently better.

That changes the opportunity for dental technology. The winning vendor is not necessarily the one that automates the most tasks. It is the one that helps the customer demonstrate repeatable operating improvement.

The next generation of DSO value creation will be measured less by the number of tools deployed and more by the quality of earnings, patient experience and practice-level performance those tools enable.

Primary source

Lincoln International — The Next Generation of Dental Value Creation

Avized's commentary and vendor-evaluation implications are editorial analysis; reported statistics and The Smilist example are attributed to Lincoln International.