How Great Lakes Dental Partners Thinks About AI, Data, and Vendor Vetting

The dental technology market is flooded with AI vendors, each promising to transform practice operations. For a 27-location DSO like Great Lakes Dental Partners (GLDP), sorting signal from noise isn't optional — it's a survival skill. In a recent episode of the Dental TeX-ray podcast, Alan Rencher, CTO of Henry Schein One, sat down with Khuzaan Screwvalla, VP of IT at Great Lakes Dental Partners, to talk through how the organization actually think about AI, data infrastructure, and vendor selection in the real world.

Here are the top takeaways.

1. GLDP doesn't ask "how do we use AI?" — they ask "what's the North Star?"
One of the most useful reframes in the conversation came early: GLDP doesn’t have a formal "AI strategy." They have a patient experience strategy, and technology — AI included — either serves it or doesn't make the cut.

As Screwvalla put it, the North Star is always the patient sitting in the chair. That patient doesn't care what technology stack powers the practice. They care about their experience. The question GLDP asks of any technology is whether it can be embedded seamlessly into clinical workflows without degrading that experience.

This framing cuts through a lot of industry noise. AI is a means to an end, not a strategy in itself, and DSOs that build their evaluation process around that principle will make better decisions than those chasing the technology for its own sake.

2. Most "AI" in dental tech isn't actually AI
Screwvalla was direct about something that gets glossed over at every trade show: the vast majority of products marketed as AI in the dental space are actually automation: rules-based systems that respond programmatically to inputs rather than acting with any real autonomy or intelligence.

That distinction matters practically. When a DSO evaluates a vendor claiming AI capabilities, the right question is whether the product is doing something genuinely intelligent and autonomous on their behalf, rather than executing a predetermined logic tree with a modern interface on top of it. The latter isn't useless, but it shouldn't command AI-level pricing or AI-level trust.

Screwvalla added that the vendor landscape compounds this problem. New AI dental tech companies appear constantly, and many of them are highly specific, solving one narrow problem, and are funded at an early stage, making their longevity genuinely uncertain. GLDP has added a new question to their formal vendor questionnaire: “What stage of funding are you in?” 

3. The new vetting shortcut: is the vendor an approved API partner?
GLDP used to field every vendor inquiry through a long, resource-intensive evaluation process. After migrating to Dentrix Ascend, they changed their approach entirely.

The first question now is simple: “Are you an approved vendor partner of Henry Schein One’s API exchange?” If the answer is no, most of the remaining evaluation becomes irrelevant. If the answer is yes, a large portion of the security and stability vetting has already been done by Henry Schein One.

Screwvalla framed this explicitly as a risk-transfer strategy. GLDP is not a technology company. Their focus is patient care. Outsourcing the baseline security vetting to a platform they already trust lets them redirect that energy toward clinical and operational outcomes.

From there, the remaining evaluation criteria in order are: core technology fit, pricing model, and partnership posture. On pricing, Khuzaan was candid: a number of otherwise solid vendors lose deals because they're pricing for short-term survival rather than long-term partnership. GLDP is willing to invest in vendors, but they expect those vendors to co-invest in adapting their product to GLDP's workflows — not deliver a static tool and walk away.

4. "Single pane of glass" is more than a buzzword
Before migrating to Ascend, Great Lakes Dental Partners was managing a fragmented ecosystem that included MyPax, Dolphin, OverJet, and numerous other add-ons, each with its own platform, its own login, and its own data silo. Front desk staff were toggling between 5-10 different systems depending on the task. IT was spending its time maintaining integrations rather than driving outcomes.

Post-migration, Screwvalla describes their operating goal as "SPOG" — Single Pane of Glass. Every vendor they bring in needs to work within that framework, accessed from a unified interface, with data flowing into a single source of truth. This isn't just a convenience preference. It's foundational to how GLDP makes decisions.

5. Data-driven decision making separates growing DSOs from struggling ones
About three years ago, GLDP made a deliberate cultural shift: no more decisions based on intuition or internal consensus. Every strategic move needed data to back it up.

This philosophy connects directly to their "fail fast" methodology. The only way to fail fast productively is to have accurate, timely data that tells you exactly when something isn't working, so you can stop it and redirect resources before the damage accumulates. Without reliable data, fail fast just becomes fail.

Their data architecture is built on layered reporting: bronze, silver, and gold tiers that allow them to start with high-level accuracy and drill down to increasingly granular detail. The goal, despite the sophistication of the underlying structure, is simplicity at the surface: one clean view that answers the question in front of you without requiring a data analyst to interpret it.

6. LLM-powered data querying is exciting, but still cost-prohibitive for mid-sized DSOs
Screwvalla shared that GLDP has explored what he calls "shower thought" querying — using a large language model to interface directly with their operational data and ask spontaneous, unscripted business questions: Why did production drop at this location? Which providers have the highest treatment acceptance rates on Tuesdays? What's the AR trend for a specific payor?

The appeal is real. When operators can ask those unstructured questions and get analysis and recommendations from their own data, it changes how organizations grow and iterate. GLDP went beyond the thought exercise, running live demos with their actual data and found the capability genuinely compelling.

But the cost was prohibitive. For now, this remains a capability they're watching closely and hoping either becomes more affordable or arrives as a native feature within a platform they already use — which Rencher hinted is a direction Henry Schein One is actively moving toward.

FAQ

How should DSOs vet AI dental software vendors?
Start by confirming whether the vendor is an approved API partner with your practice management solution (PMS). This single check answers most security and stability questions upfront. From there, evaluate core technology fit, pricing model sustainability, and whether the vendor is willing to adapt their product to your workflows.

What is the difference between AI and automation in dental technology?
True AI involves autonomous decision-making and learning. Most products marketed as "dental AI" are actually rules-based automation — systems that follow pre-programmed logic without genuine intelligence. The distinction matters for pricing, expectations, and long-term vendor evaluation.

What is a single pane of glass strategy for DSOs?
A single pane of glass strategy means consolidating all vendor platforms, data sources, and workflows into one unified interface that’s typically centered on the PMS. It reduces the number of logins, dashboards, and disconnected data exports staff need to manage, improving efficiency and data accuracy.

What is the "fail fast" methodology in DSO operations?
Fail fast is an operating philosophy where organizations test ideas quickly and abandon them early when data shows they aren't working, redirecting resources to approaches that succeed. It requires accurate, real-time data to function effectively.

How can dental DSOs use data to improve decision-making?  
By centralizing data from the PMS and all integrated vendor platforms into a single source of truth, DSOs can build layered reporting that starts with high-level trends and drills down to granular, location-level insights. Making decisions based on that data is what allows organizations to identify the incremental improvements that drive growth in a low-margin environment.

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Listen to the full episode on Dental TeX-ray and learn more about Dentrix Ascend and Henry Schein One's approved partner API ecosystem.