DSOs and multi-location practices are getting squeezed from every direction: staffing shortages, wage inflation, rising supply costs, and insurance requirements that seem to multiply overnight. For many dental industry finance leaders, these pressures are showing up in EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization). Traditional levers such as incremental cost-cutting or renegotiating payer contracts no longer move the financial needle enough to offset today’s operating realities.
Enter artificial intelligence (AI). And before you roll your eyes at another "AI will save everything" pitch, hear me out. I'm approaching this as a CFO, and my lens is simple: if a technology can protect margin, accelerate cash flow, reduce administrative complexity, or increase practice capacity, it directly influences enterprise value. AI is becoming one of the few levers that can actually move all four variables.
Treating AI as a Margin Engine
Finance teams are trained to view new software as an expense line that needs justification. AI-powered tools require a different framing. Many of the largest financial drains in a DSO originate from operational friction caused by manual claim follow-up, inconsistent eligibility checks, slow A/R cycles, scheduling inefficiencies, and incomplete data flowing between systems, among others.
These aren't just operational inconveniences; they're structural contributors to EBITDA erosion. When a practice loses production due to inaccurate estimates, when teams burn hours reconciling claims, or when chairs sit empty because no one proactively rebalanced the schedule, that shows up directly in your financial performance.
New advancements in AI help reduce this “process debt.” By removing the repetitive, manual, error-prone work that burdens revenue cycle teams and front office staff, AI can begin shifting the financial model from reactive to predictive. And that’s where things get interesting.
The Economic Impact: What AI Improves First
Financial gains from AI typically fall into four buckets:
Revenue Cycle Performance
Research shows that nearly 3% of production is lost each year due to inaccurate insurance estimates and preventable claims issues. At scale, this becomes millions in unrealized revenue for a multi-location DSO. Income just…evaporating. AI can reduce this leakage by automating eligibility checks, identifying at-risk claims, and accelerating follow-up, long before a denial impacts cash flow.
Labor Efficiency
Staffing remains one of the greatest EBITDA constraints. Three out of five practices report challenges with recruitment, training, and retention. AI creates a new form of labor flexibility by taking on administrative work that would otherwise require several full-time employees. Let me be clear: this isn't about replacing staff. It's about removing the burden of tasks that don't require clinical judgment, so your teams can focus on patient care and growth rather than paperwork.
Scheduling and Capacity
Even a small percentage of no-shows or unfilled hygiene openings has a measurable financial impact. AI can rebalance schedules, identify opportunities to fill gaps, and streamline patient communication. Practices that adopt automated reminders and scheduling support report significant reductions in no-shows, which drives both top-line revenue and operational consistency. This is really about maximizing capacity utilization.
Enterprise Visibility
When your practice management, imaging, and RCM systems don't talk to each other, you get fragmented reporting and delayed insights. This slows decision-making and makes it nearly impossible to identify the true drivers of profitability across locations. AI surfaces anomalies, flags performance risks earlier, and enables real-time financial monitoring. Practically speaking, the data we have is also imperfect across multiple systems—this is where the power of AI can help determine insights across large amounts of imperfect data.
All four categories point to the same conclusion: AI is not a cost center. It is a tool that directly influences EBITDA protection and growth.
Moving from Assistance to Autonomy
AI adoption doesn't happen overnight. It unfolds in stages, and understanding this progression matters:
Assist: Automate repetitive tasks such as eligibility checks, patient reminders, and basic claims processing.
Advise: Generate predictive insights, surface trends across locations, and forecast production or A/R risk.
Act: Operate autonomously within defined guardrails. This includes following up on unpaid claims, rebalancing schedules, and triggering workflows without human initiation.
The financial impact compounds as DSOs progress through these stages. Early wins usually come from cycle-time improvements and cleaner data. Over time, autonomous agents create a staffing model that is more resilient and consistent, even during turnover or periods of high demand.
Private Equity Expectations and Enterprise Value
Private equity firms often straightforwardly evaluate DSOs: is EBITDA reliable, and is the operating model scalable? Fragmented systems, inconsistent technology adoption, and manual financial processes can reduce valuations by creating perceived risk.
The inverse is also true. DSOs that modernize infrastructure and demonstrate consistent cash conversion, predictable collections, cleaner data, and scalable workflows become more attractive. AI helps reinforce this discipline by reducing variation across locations and strengthening financial oversight.
It is worth acknowledging the tension that sometimes exists between clinicians and financial stakeholders. I get it. Profitability should never come at the cost of patient care. The CFO's role is to create conditions in which both can improve. Modernized workflows, fewer administrative burdens, and better visibility support enhanced patient experiences. When implemented responsibly, AI can reinforce that partnership rather than undermine it.
The Changing Role of the CFO
The old version of the job focused on accurate reporting and cost control. The modern version requires fluency in data, comfort with technology, and a willingness to challenge long-standing assumptions about how dental organizations operate.
Instead of issuing vague directives to "increase revenue" or "reduce costs," today's CFO can point to specific, data-backed opportunities: locations with unfilled chair time, inconsistencies in claim submission, opportunities to shorten the cash conversion cycle, workflow gaps that inflate administrative labor.
AI strengthens this evolution by making insights more accessible and more timely. When the data is clear, the conversations across operations, clinical leadership, and finance become more constructive.
Setting the Stage for Long-Term Value
The next decade of dentistry will be shaped by the organizations that treat AI as a core financial strategy rather than an experiment. EBITDA is no longer protected by traditional levers alone. It is protected by data quality, workflow speed, collection reliability, and the ability to scale without adding proportional headcount.
AI elevates all four. It reduces operational noise, clarifies financial signals, and increases the predictability that DSOs depend on.
For dental organizations under pressure to improve performance while navigating labor shortages, rising costs, and investor expectations, AI is the beginning of a new economic model for running a dental practice. And as a CFO, that’s the kind of shift I can get behind.
About the author
Stephen Fong is the Chief Financial Officer at Planet DDS. As CFO, he oversees strategic finance, business analytics, accounting, financial operations, and legal functions.
