We are now in March of 2026. Q1 is almost over.

Crazy how quickly things move, at least it feels that way for me.

I've been to a ton of events already in Q1 and mixed in some meetings onsite with clients in the DSO landscape, along with industry partners who are focusing on solving important problems across dentistry.

There are a few consistent topics always discussed and one of those is of course, AI. More specifically, the discussion around whether AI is replacing full time hires.

This is obviously a sensitive subject in dental and frankly across all verticals.

There have been claims coming from AI companies talking about this pretty openly and real data is starting to come out now. We are much further along than we were a year ago in understanding what these operational AI systems actually do inside a business.

Depending on who you talk to though, these claims are very much disputed.

I’ve had direct, intentional conversations with both vendors and DSO executives who claim job replacement is happening.

At the same time, I’ve had conversations with CEOs and operators who say the AI they’ve tested hasn’t replaced staff at all. These aren’t isolated opinions either. I’ve heard variations of both perspectives multiple times over the last several months.

So which one is right? Let’s talk about it.

Let me clarify something before going further.

When I’m talking about AI here, I’m talking about all forms of AI, both clinical and operational.

On the clinical side, diagnostic AI has existed in the market for several years now and is now the norm when it comes to imaging. It’s caught on through heavy investment across the dental technology landscape from venture capital, private equity, and funds even within dentistry that focus on dental vertical specific investments.

Most individuals in dentistry have a take on diagnostic AI at this point. Some believe strongly in the impact it has on diagnosis rates and treatment acceptance.

Others behind closed doors are still evaluating the effectiveness and ROI of these systems.

Because of this skepticism you’re starting to see product enhancements from these companies to provide even deeper value and functions inside a dental practice. They’ve done this through recent strategic acquisitions and new product features to support operations and RCM.

Some of this product expansion likely reflects the amount of investment that has gone into the space and the pressure to prove long-term value and further explain whether there's true ROI and satisfy investors.

Now, I’m not saying dental practices and DSOs are going to start ripping these systems out nor am I saying there isn't ROI. However, it’s important to remember that:

The best technology companies pay attention to their clients, continue those conversations with key customers, and keep innovating.

Now as teams continue evaluating diagnostic AI, some of the newer solutions entering the market around operational AI are being tested and results are starting to come in.

We’re beginning to see a sample size of interesting stories.

Before getting into some of these stories, it’s probably helpful to explain where my perspective usually comes from. My network and access tends to sit across a fairly wide range of organizations.

Occasionally I’ll hear from a private practice owner, but that’s pretty rare in my world. That being said, I spent time at the practice level with private practice owners as recent as last week.

However, most of the conversations I’m having tend to be with emerging groups, mid-market DSOs, and operators that are starting to scale.

Depending on how you define enterprise, I don’t really spend much time working with groups like Heartland, Aspen, or Pacific but do have connections deep inside MB2. That being said, my sweet spot tends to fall somewhere between eight and seventy locations.

That’s usually the point where things start to get complicated enough that technology decisions really matter. Workflows start to stretch with growing teams and technology decisions have real consequences across the organization at scale.

It’s also the stage where consolidation and standardization conversations begin to show up.

Because of that, a lot of the conversations I’ve been having recently involve groups experimenting with AI tools across different parts of the business.

And we’re starting to see a sample size of interesting stories emerge. Now these stories I will share aren't formal case studies but they are real conversations with operators who are testing these systems inside their businesses.

Some of them are seeing meaningful impact while others feel like the tools still aren’t quite ready.

One of the more interesting conversations I’ve had recently came from a CEO who made a pretty bold move inside their call center operations. For context, this is a mid-market DSO with centralized operations.

Historically they had a fairly large team responsible for handling patient calls, confirmations, and scheduling.

Over the last year they implemented an AI system designed to manage a large portion of those interactions.

The system now handles incoming calls, scheduling requests, confirmations, and after-hours communication. Something the previous team struggled to cover consistently without expanding staff.

Missed calls and overflow volume are now captured automatically instead of going to voicemail.

Over time that organization replaced a double-digit call center team that had previously been managing that work.

Some of that reduction happened naturally through turnover. As employees left, the organization chose not to backfill those roles as the system proved it could handle more of the workload.

They still maintain a smaller internal group to handle escalations, complex scheduling issues, and patient situations that require a human touch.

But most of the routine scheduling and communication work that once required a large staff is now automated.

Another example came from the revenue cycle side of the business.

This conversation came from a regional dental group with roughly nine locations and a centralized team responsible for insurance eligibility verification across the organization.

Historically that team spent most of their time logging into payer portals, making calls to carriers, and confirming benefits before patient visits.

Much of that work happened close to the appointment. In some cases teams were still verifying eligibility the same day patients arrived.

Over the last year the organization implemented an AI-driven eligibility verification system integrated into their practice management platform.

The system now pulls eligibility data directly from payer portals, validates the information, and pushes it back into the workflow ahead of the appointment.

Verification is now happening days in advance instead of the last minute and according to the operator I spoke with, the biggest change wasn’t just staffing, it was predictability.

Front desks now have clearer information when patients arrive and teams spend less time trying to sort out insurance issues after treatment has already been completed.

Over time the team responsible for eligibility verification was reduced significantly as the system took on more of the workload.

Roughly fifteen FTEs that had previously been responsible for verification work were eliminated as the automation matured and the remaining staff now focus primarily on exceptions, edge cases, and insurance issues that require human review.

The examples mentioned are real and they’re happening, but that’s not everyone's experience.

Because for every operator seeing results like those mentioned, there are others testing AI systems and coming away with a very different conclusion.

In one conversation recently an operator put it pretty simply.

A lot of the platforms entering the market today are very good at handling specific tasks inside a workflow, but that doesn’t automatically mean a full role disappears.

If someone’s job includes ten different responsibilities and automation handles two or three of them, the person doesn’t go away, the work just gets shifted around.

In many organizations that means the same team is still required unless leadership intentionally redesigns how the work gets done.

There’s also a financial reality behind this conversation that operators are paying attention to.

Several people I’ve spoken with recently made the point that the economics only start to become interesting when a system can replace multiple roles or prevent those hires from happening in the first place.

Replacing one employee with a piece of software that costs roughly the same amount doesn’t really change the equation. Where the math starts to shift is when automation allows a team to handle significantly more work without adding staff.

And that’s where a lot of the experimentation in dentistry is and should be happening.

In conclusion, some organizations are seeing meaningful operational impact while others are still trying to figure out whether AI tools are ready for prime time or whether they build them on their own.

Another interesting perspective that came up in conversations recently with a mid-market, hyper growth mode DSO had less to do with replacing employees and more to do with how organizations are built in the first place.

Several operators pointed out that implementing AI into an existing organization can be much harder than people expect.

Most DSOs today were built around traditional staffing models. Departments were formed slowly over time. Teams grew to handle scheduling, insurance verification, billing, patient communication, and operational support across locations.

Introducing automation into that environment doesn’t automatically remove those roles because the workflows, responsibilities, and expectations were designed around people doing that work.

In many cases AI simply ends up assisting those teams rather than replacing them.

But the conversation changes when you think about organizations that are built differently from the start. One operator I spoke with described what they believe the next generation of organizations might look like.

Instead of hiring a team of thirty people to handle operational functions, the organization might hire eight people and design the entire workflow assuming automation will handle a large portion of the work.

Those teams would still oversee operations, make decisions, and handle complex situations, but the baseline structure of the organization would be much smaller. It’s about never building the larger workforce in the first place.

That idea raises an interesting question for the industry. Operational impact from AI may not only come from replacing existing roles inside DSOs, it may come from the organizations that are built differently from day one.

The reality is we are still very early in understanding what AI actually changes inside a dental organization.

Some groups are already seeing measurable impact in areas like scheduling, call handling, and parts of revenue cycle management.

Others are testing tools and deciding to go in a different direction.

Part of the confusion comes from how quickly the technology itself is evolving. Tools that felt experimental twelve months ago are now fully operational.

Leadership has to decide where AI automation fits and where people still add the most value.

And in many cases the biggest advantage may not come from replacing existing roles at all, but from building organizations that assume automation will handle more of the work from the start.

What a time to be alive!

Have questions about evaluating and implementing AI. Let’s talk about it. Schedule some time with me here.

Earlier this year I told Brian Colao I wanted to make a bigger impact around dental technology. After talking it through, he had a simple idea.

“We should do a podcast and you can be my cohost and technology guy.”

That conversation turned into The DSO Technology Show, which was just launched with the Dykema team. The goal is to provide a broad look at dental technology and talk about what drives growth inside DSOs.

Our first episode is live and talks about Diagnostic AI:

→ How diagnostic AI integrates with imaging and practice management systems
→ Why adoption, not installation, determines ROI
→ How AI supports ethical same store growth
→ The role AI plays in insurance documentation
→ What implementation looks like at scale inside DSOs

Listen here when you have a moment.

DSO Compass Off Script is still live as well, with a new episode coming soon with Clayton and I talking conferences and where the market is heading.

Check those episodes out here on this link.

Chicago Midwinter Meeting

Made a quick Friday/Saturday stop at Chicago Midwinter this year. Not a full conference run, but enough time to get a feel for things.

From that show an observation. It’s probably never been easier to start a tech company, but it may be one of the hardest times to scale one.

A few observations:

➝ Fewer “new” companies entering the market. Most vendors felt familiar, the phase-out cycle seems to be underway.
➝ Conversations shifting from acquisition to onboarding and implementation. DSOs are focused on making what they bought actually work.
➝ Conference strategy itself becoming a topic, teams are asking where their time is best spent.
➝ Strong appetite for peer learning and community exchange rather than just static content.

Short visit, but a productive one. Midwinter still feels like the strongest association conference in dentistry and is still great for new product launches.

Destination DSO - Costa Rica

Where do I start with this event. What an experience it was. Everyone needs to visit Costa Rica at least once.

What stood out most about the event was the setting and connection of attendees. Away from the traditional conference environment, the conversations were different. There was space to think, connect, and reflect in a way that rarely happens on a crowded event schedule.

A few things reinforced themselves for me:

➝ When the pressure to transact disappears, trust builds
➝ Shared experiences deepen conversations
➝ Personal connection creates impact

This time of year moves quickly with events, flights, and meetings. It’s worth pausing occasionally to recognize the moments that shape relationships.

I wrote a longer reflection about this dynamic in my February LinkedIn newsletter, “The Cheesecake Factory Menu of Dental Conferences.”

If you're interested in a deeper take on the event and the broader conference landscape, it’s worth a read.

Truly appreciate you being here and making it to the end. Have ideas, questions, or tech you want me to explore? Just reply to this or book some time with me to discuss.

- Matt