Strategic Guide to Scaling AI

As margin pressure intensifies, workforce shortages persist, and patient expectations continue to rise, more organizations are turning to AI as a tool for growth and improved care. But many implementations fall short. Tools get layered on instead of embedded, for instance, or workflows become more complex instead of simpler, leaving teams wondering whether the promise of AI was overstated.

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At its best, AI doesn't replace roles. Instead, it makes the tools you already rely on even more effective, quietly working behind the scenes so that every task is accomplished more easily and efficiently. When intelligent automation takes on time-consuming tasks such as documentation and insurance follow-ups, clinicians and staff can refocus on patient care and outcomes.

AI experimentation is easy. Making it work across a dental organization? Not as much. That's why we've explored how leaders can turn AI into a practical advantage that eases daily friction, strengthens teams, and elevates patient care across their organization.

Designing an AI strategy for your organization

The three questions that come first

Before anything else, your team needs to answer three questions:

#1 What problem do you want the AI to solve?

AI can help solve a number of clinical and operational concerns in your organization, but if you start using it without an understanding of the workflow or task you're trying to fix, you're setting up your team for a potentially expensive failure.

"You really have to look at what you're trying to do in your business," said April Lowry, Executive Director, Clinical/AI Products and Operations, Henry Schein One. "Are you trying to fill appointments or increase treatment acceptance? Do you want your doctors to have a better quality of life where they're not typing up notes at 10:00 p.m.?"

And while we named AI as the trend of the year in our 2026 Trends Outlook, we'd be remiss to suggest AI can fix every issue.

So before you move forward, ask yourself what problem you need to fix. Only after you've determined that can you put the rest of your strategy in place.

#2 How will you measure success, and what outcomes are being measured to define success?

If you don't know what you're solving for and how you'll measure success, how will you gauge if the AI tool you chose was the right one?

When evaluating new solutions, don't just define what success looks like — make sure the tool can actually measure it. For example, if your goal is to increase case acceptance by 15%, but the platform you choose doesn't track treatment plan presentation, patient follow-up, or acceptance rates, you'll have no way to prove ROI. The ability to measure the outcomes you care about should be a core selection criterion, and vendors should be able to clearly articulate the results they aim to drive and exactly how those results will be tracked and reported.

#3 What's your operational plan?

This needs to include:

• The team members that need to be involved across your organization
• What top-down buy-in you need from your investors and C-suite
• How you'll ensure the team members doing the day-to-day care buy into the vision

The risk when you don't ask questions

Your choice of an AI solution impacts everything from workflow to team buy-in to liability risk. The organizations that start by understanding the problems they're solving show greater success than those that implement technology without thinking through the three essential questions.

"Sometimes people just get excited about a new technology, and they go buy it," said Lowry. "We saw this with 3D printers. I cannot tell you how many offices I've seen with a 3D printer that's still sitting in the box, literally years later. What is important is to choose AI that's embedded in your workflows."

Independence Dental Services, which now has nearly all 80 of their practices using AI, dealt with three failed pilots before realizing they needed to stop and reassess what they were trying to solve for in three categories:

• Efficiencies
• Revenue drivers
• Tech stack

"We were spending $500 here, $60 here, $75 there... By the time we looked at the efficiencies, we weren't offsetting costs and staff. We were actually becoming a lot more complex by trying to make the workflows work," said Amanda Fuentes, Director of Partner Support. "Once we started deciding which solutions we were going to look at, then it was a matter of deciding what the workflows were, what the pilots and processes looked like at a practice level, and how they best fit us."

Lay the groundwork for scalable AI

Identify the best initial use cases

For a smaller practice, that might be implementing one solution and then evaluating how to move forward. Larger organizations may have the bandwidth to do one per area. For example, you could implement an AI tool to help your front desk schedule appointments while your clinicians use AI imaging or voice notes.

Each organization will need to look at themselves and their priorities, then decide how many initiatives they can handle at one time.

Evaluate potential vendors

"The biggest red flag is an unproven technology. It's important to be selective, because you want to be sure the company is still around in the future. The last thing you want is to invest money, resources, time, and training into a technology that won't be available in a few months." — April Lowry

There are four key topics you need to talk to prospective vendors about:

The technology their AI is built on

When was the technology built? AI has changed a lot during even the past five years, and today's language models are faster, learn more quickly, and are much more adaptive, especially for a fast-paced dental environment.

Whether the technology can be fully embedded into your practice management solution (PMS)

A fully embedded workflow is critical to driving adoption and consistent use. When AI is embedded within the PMS your team already relies on, it eliminates extra logins, manual handoffs, and system switching, helping teams work faster and stay focused on patient care. That level of consistency is possible only when AI is embedded directly in the PMS or delivered through secure, approved integrations with the PMS provider.

Which outcomes and measurements are built in

You don't want to guess the ROI of your AI solution. Dashboards should be built right into the platform so that all users and leaders can access them and understand how any new technology is — or isn't — working.

What vendor support looks like

The best vendors support your team before a contract is even signed. Their entire team should be engaged in taking action, and there should be a point person for each part of the rollout to help ensure the pilot's success. Post-implementation, you should also have a dedicated team that supports you and is available immediately — and not just by email — if an issue arises.

Want to learn more about how to pick the right AI vendor? Check out our Guide to Buying AI.

Create a project plan

During implementation and go-live, you need to have a documented project plan that includes:

• How you're going to go live
• Who will be involved
• How training will be done
• The rollout process and schedule
• Who will be accountable for each step
• What you're doing up until the point of go-live and any required follow-up
• How you'll check in with team members to make sure they're continuing to use the tools

This should be created with your vendor, who should support you throughout implementation to help ensure a smooth rollout and adoption. Both organizations should work together to:

• Develop joint project plans
• Hold weekly operational meetings
• Create go-live schedules
• Define desired, measurable outcomes
• Align all training and learning content
• Ensure appropriate support is in place during the adoption phase

The human side of success

Address the major concerns

Let's be really clear here: The AI solution you choose doesn't matter if your team won't embrace it. And a lot of your team members may be concerned that AI will replace them or aren't interested in changing their workflows. Those are major hurdles to overcome.

Before you begin rollout, you need to assess your organization as a whole and get a feel for where your team members stand. If they're already resistant to change or new technology, you've got some work to do before implementing a new tool.

"Even with the best technology and all the training in the world, if the end users aren't ready or don't have the desire to add a new tool to their workflows, you're not going to get their buy-in," said Jamie Westfall, Senior Onboarding Consultant, Henry Schein One.

When Independence Dental moved forward with AI implementation before ensuring everyone was on board, they realized mid-rollout that while their doctors were open to the change, their hygienists weren't.

The practice tackled the challenges head-on by inviting a small group of hygienists to pilot the tool themselves, with the commitment that whatever they chose would integrate seamlessly into their existing imaging software. What followed was seven focused weeks of training, accountability, and hands-on use, with dashboards that clearly showed the impact: increased SRPs, higher case acceptance, and measurable production growth.

Soon, hygienists were calling doctors in to review findings chairside, creating a healthy, contagious enthusiasm that drove organic adoption across the practice.

"Once hygienists were fully bought in, doctors followed organically. Hygienists were calling doctors in to look at findings, and it became a good kind of obsession," said Fuentes. "From there, adoption spread by word of mouth. Eventually we had 50 practices calling in asking to get it installed."

Create alignment across the organization

A project's success depends on how well your team aligns around shared goals and strategy.

"One of the first steps needs to be getting clear on why the organization is making a change and how it defines what success should look like," said Westfall.

If you're not building awareness and desire from the very beginning, moving your organization through the next stages of change becomes significantly harder. When leaders are open and honest about the process, the tools being introduced, and the goals behind the change, teams are far more likely to engage.

That early clarity creates a shared foundation, so when doubts or learning blocks inevitably surface later, you can reconnect people to the original purpose and remind them why the change matters in the first place.

With that foundation in place, teams are less likely to default to thinking "This just makes my job harder." Instead, they remember what's in it for them. Clear goals and a strong vision give people something to work toward, which fuels their willingness to invest the time and effort to learn.

Scale what works

There's a general framework you should consider when rolling out new technology over a multi-location organization.

1. Build a cross-functional task force — Your key people need to be in the room when you're demoing products and having discussions with potential vendors. This team needs to include people involved in adoption, implementation, and daily use, with leaders from clinical, ops, revenue cycle management, IT, and training.
2. Determine the pilot — Pick three offices — a high performer, a mid-performer, and a struggling site — so that you can get different outcome measurements. If you're a multi-specialty DSO, also consider adding a specialty office.
3. Determine your outcomes early — What is your success rate? What are you measuring? How are you going to get those measurements so that they don't become an afterthought?
4. Standardize the workflows before scaling — What are the current operational and business process workflows that need to be adjusted before or as this new AI technology rolls out?
5. Roll out the technology in waves — Pick a plan that allows you to manage training and support. Set up office hours with your vendor to ensure there are always people available, and establish internal power users.
6. Continuously optimize your workflows — This should not stop at go-live. Keep looking at adoption and outcomes and getting feedback from your staff that you also relay to your vendor.

Identify and empower power users

A successful rollout relies on employees championing the technology throughout the organization, whether those are project managers, heads of operations, or regional managers.

"Within each of those groups, we like to do a 'train the champion' process so that each region has a champion to drive it home on the day-to-day level for the specific offices they're in charge of," said Lowry. "We've seen a lot of success with that program, and it gives the offices a starting point of where to go if they do run into any concerns or challenges with the rollout and training."

Your C-suite leadership is also essential to driving adoption, ensuring new technology is part of the corporate vision and tied back to the organization's overall priorities. Your entire team should be aligned from an executive-level perspective, all the way down to the regional and office managers who will ensure the rollout is successful. Your vendor should have similar counterparts.

Ask for feedback

Feedback is essential at every stage of implementation, not just to drive success but also to surface when a solution isn't right for the organization. As Alan Rencher, Chief Technology Officer, Henry Schein One, put it, "Consistently poor feedback is a strong sign you probably have the wrong tools. Take this as an opportunity to learn from what happened, then take another run at it."

Fuentes echoed a similar sentiment when reflecting on Independence Dental's early AI pilots. When usage declined, the feedback was consistent: concerns about over- or under-diagnosing, poor imaging quality, and hygienists unwilling to adopt the tool. "That was the biggest signal we needed to adjust," Fuentes explained.

Feedback isn't valuable only at the organizational level. It's equally critical for the vendors building and refining the technology. "The hands-on product feedback is probably one of the most critical pieces of ensuring your AI technology is meeting your specific needs," said Lowry. At Henry Schein One, product teams spend time in customer offices every month to better understand real-world clinical and imaging workflows.

"That continuous feedback loop is probably one of the most important aspects of what we do," Rencher said, "because it ensures we're always considering the voice of the customer in everything we build and every change we make."

To be effective, feedback must also be measurable. That starts at the beginning of implementation, when organizations define success. Vague goals like "improve collections" aren't enough. Teams need clear, trackable outcomes that allow progress to be evaluated and shared across the organization.

Know when to reevaluate your process

Just as important as gathering feedback is knowing when to act on it. In some cases, that means slowing down or even pausing an implementation. "When adoption stalls or frustration builds, leaders may need to create space for honest conversations," said Westfall. "That pause allows teams to surface grievances, realign expectations, and determine whether adjustments or a complete reset are necessary."

Measuring outcomes, listening closely to feedback, and adjusting course when needed create momentum over time. Teams begin to recognize where gaps exist and can respond with targeted training or support. When leaders hear their teams using that shared language and hitting clearly defined goals, it signals that the process is working, and worth sticking with.

That's where real transformation happens: not by pushing forward at all costs but by staying responsive, intentional, and willing to pause when the feedback demands it.

And if you're not seeing ROI? It's important to remember that just because one tool didn't work, you shouldn't throw away the idea of AI altogether.

Fuentes pointed to one example where using AI to help manage scheduling wasn't the right solution for a practice predominantly serving an older population.

"AI is not always going to succeed. You may not always get the result you want just by throwing AI at a problem," she said. "We have to understand the root cause of the problem. AI can be a very helpful solution, but that doesn't necessarily mean it's solving the specific problem."

Leading with clarity in an AI-enabled future

AI has reached a turning point in dentistry. The technology available today is more capable than that of even a year or two ago, and organizations that delay adoption risk falling behind quickly. This is the moment to move from curiosity to action.

The most effective way to start is simple and intentional. Choose a specific problem AI can help solve. Define how success will be measured. Build a plan to roll it out in a manner that fits naturally into existing workflows. From there, learn, refine, and scale the efficiencies that follow.

More than anything, though, you need to make abundantly clear, in every action and decision, that you're all working toward a common goal. This needs to be at the forefront of any technological change.

Organizations that win with AI won't be the ones chasing trends. They'll be the ones that start now, stay focused, and treat AI as a strategic capability. The opportunity is here. The next step is getting started.