Key Takeaways
- AI solved call‑volume, scheduling, and no‑show challenges that staffing alone couldn’t address.
- Starting with measurable workflows like collections and confirmations delivered fast, proven ROI.
- Change management — early adopters, patient communication, and small wins — was essential to overcoming resistance.
- Integrated AI platforms outperformed point solutions by executing workflows directly inside the EHR/PMS and eliminating manual follow‑up.
Over 120,000 calls a month. Fewer than half answered. No-show rates eating 12–15% of scheduled visits. These aren’t projections or benchmarks from a whitepaper — they’re the numbers two healthcare executives were staring at before they deployed AI at scale.
At a recent Becker’s Healthcare webinar, Ajay Chadha, former CFO of 42 North Dental, and Clayton Lawrence, COO of UNIO Health Partners, shared what healthcare AI deployment actually looks like from the inside: the ROI, the resistance, and the decisions that made the difference.
The problem every operator recognizes
Ajay Chadha ran New England’s largest dental service organizations — 42 North Dental, with over 100 practices across nine states. The breaking point looked familiar.
“Not all inbound calls were being answered, the appointments were not being fully confirmed, therefore that was creating gaps or no-shows in our schedules.”
The reflex for most organizations is to hire. Build a call center. Add staff. Chadha ran the math.
“We’re going to have to hire more people, we’re going to have to train these people, there’s going to be turnover.”
Clayton Lawrence, COO of UNIO Health Partners — a 50-plus location multi-specialty practice across California covering gastroenterology, urology, and radiation oncology — reached the same wall. His organization was fielding over 120,000 calls a month and answering fewer than half.
“We knew that we just couldn’t throw more people at this and solve the problem by throwing more bodies at it.”
Both leaders made the same call: stop waiting for a better solution and deploy AI directly against the workflows bleeding the most revenue — starting now, with the tools that exist today.
Start with what’s measurable, not what’s exciting
The most important insight from both leaders runs counter to how most organizations approach healthcare AI for the first time.
Don’t start with the most ambitious use case. Start with the most provable one.
For 42 North Dental, that meant outbound collections first — not AI scheduling, not automated intake. Collections, because if AI could recover aging patient receivables the front desk never had time to chase, the ROI would be clear and fast.
It was.
“In the first nine months, just the outbound calls, we collected about $8 million of receivables. Almost $2 million of that was from amounts over six months old. In the dental business, once the balance gets to be more than six months old, it is highly unlikely that you’re going to collect it. So, this is likely bad debt that I’ve just collected.”
The downstream math was equally compelling.
“For every 1% reduction in the no-show rate, that 1% has a seven-figure impact in terms of the bottom line, even after all other costs.”
At UNIO Health Partners, Lawrence focused first on appointment confirmation and no-show reduction, directly improving access for the 11,000 to 12,000 patients his organization sees each week.
“We saw a no-show rate drop from 12 to 15% on average down to three to 3.5%, and it sustained that on a go-forward basis.”
Schedule utilization at UNIO climbed from 67% to consistently above 82%. Their AI-powered waitlist management is now tracking toward $100,000 a month in recaptured revenue.
Neither organization waited for a perfect deployment plan. Both started narrow, proved it fast, and scaled from there.
Want to hear it directly from Ajay and Clayton? Access the full webinar recording here →
The resistance is real — here’s how they beat it
Neither leader pretended the rollout was smooth. Change management is where most healthcare AI deployments succeed or fail — not in the technology itself.
Lawrence was candid about his biggest early miss.
“The biggest lesson I learned was engaging with the patients. Let them know what’s happening, why it’s happening, and what it means to them.”
His advice for organizations facing internal resistance from physicians and staff: find your early adopters, prove the value in a small cohort, then bring your skeptics into the process — rather than trying to overpower them.
“Find ways to engage them frequently and often in the discussion about what we are trying to accomplish as an organization.”
Chadha reinforced the point that waiting for full organizational buy-in before starting is itself a form of costly delay.
“If you’re able to get that early-stage buy-in through the return on that investment, it is far easier to build on that and snowball it — because you’ve converted the naysayers.”
He was also direct on the fear that most often stalls healthcare organizations before they even begin.
“We did not lay off a single individual as a result of this. What we were focused on is if I can enhance my utilization of my existing providers, that is actually going to provide additional buffer.”
This is the reframe that unlocks internal momentum: AI-powered digital staff in healthcare is not about reducing the workforce. It is about redirecting it — freeing clinical and administrative staff from high-volume, repetitive tasks so they can focus on the work that genuinely requires a human.
Platform or point solution: the decision that defines your ceiling
As both organizations scaled, one strategic question emerged as the most consequential: are you building on a platform, or accumulating disconnected tools?
Lawrence was direct.
“You’ve got to decide what your AI strategy is and decide if your AI strategy requires a platform partner versus a narrow-scope vendor. For me, deciding to be with a platform partner is the right course for our organization.”
Dr. Stephanie Lahr, IntelePeer’s CMIO and former CIO and CMIO at Monument Health, pressed both leaders on where they were heading next. The answer pointed in the same direction: the era of accumulating disconnected point solutions is ending.
Miguel Barsenas, IntelePeer’s Director of Healthcare Product Management, offered the framework for pressure-testing that decision.
“If the AI can’t read from and write back to an EHR or PMS system, you built fancy automation that somebody has to act on manually. Always start with the workflow, not with the technology.”
The reason comes down to workflow integration. AI that can’t read from and write back to your EHR or practice management system doesn’t execute workflows — it creates manual follow-up work. The right question when evaluating any vendor is: does this AI act inside my existing systems, or does it produce outputs someone still has to act on?
IntelePeer’s SmartAgent is built to answer that question correctly. It executes patient communication workflows natively inside EHR and practice management systems — scheduling, collections, confirmation, and waitlist management — with full performance visibility through SmartAnalytics. The organizations realizing outcomes fastest are those who identified one high-volume, measurable workflow, proved ROI, and expanded from there.
Why waiting is the riskiest choice you can make
Lawrence left no ambiguity.
“Agentic AI is here to stay. It will be the new internet for us. Jump in the AI pool. If you don’t, you’re going to be left behind.”
The data is in. The playbook has been written by operators who have run it inside real health systems, with real patients, real staff, and real financial stakes. The only thing waiting accomplishes now is handing competitors a longer head start.
Start with one workflow. Prove it. Then scale. Watch the full Becker’s Healthcare webinar on demand →
FAQ’s
What is Agentic AI in healthcare?
Agentic AI in healthcare autonomously executes multi-step workflows — scheduling appointments, sending patient reminders, processing collections calls, managing waitlists — without requiring human intervention at each step. Unlike basic chatbots that answer questions, Agentic AI takes action inside real clinical and administrative systems like EHRs and practice management platforms. IntelePeer’s SmartAgent is purpose-built for this type of workflow execution across the patient communication journey.
How long does it take to see ROI from healthcare AI deployment?
Most organizations see measurable ROI within 45 to 90 days when starting with a focused, high-volume workflow like collections or appointment confirmation. 42 North Dental collected $8 million in patient receivables within nine months. UNIO Health Partners saw no-show rates drop from 12–15% to 3–3.5% within a few months of going live. Starting narrow and proving fast is the consistent pattern among organizations realizing outcomes quickly.
Will AI replace healthcare front desk staff?
No. Organizations that have deployed AI at scale consistently report that staff are redirected to higher-value work, not eliminated. As Ajay Chadha of 42 North Dental stated directly, his organization did not cut a single position as a result of AI deployment. AI handles repetitive, high-volume communication tasks so that front desk and clinical teams can focus on in-person patient interactions and care coordination.
What is the difference between conversational AI and Agentic AI in healthcare?
Conversational AI handles dialogue — answering questions, gathering information, routing calls, and communicating with patients in real time. Agentic AI goes further by acting on that conversation: scheduling the appointment, updating the EHR, sending a confirmation, processing a payment, or filling a cancellation slot from the waitlist. The most effective healthcare AI platforms combine both layers so that conversation and action happen in a single, seamless patient interaction.
How do healthcare organizations get physician and staff buy-in for AI?
Start with a use case that directly improves provider compensation or schedule utilization — collections and no-show reduction are proven paths. Identify early adopters, demonstrate results in a small cohort, then bring skeptics into the conversation early and often rather than informing them after the fact. Communicating changes to patients before going live also leads to smoother adoption across the organization.
What should healthcare organizations look for in an AI vendor?
Three things matter most: deep EHR and practice management integration (the AI must read from and write back to your systems natively), proven ROI data from comparable deployments (ask for specific numbers, not demos), and platform breadth (a vendor solving only one workflow will eventually require managing multiple disconnected tools). A platform partner that scales across use cases delivers compounding value and the governance needed to manage performance over time.
Is healthcare AI compliant with HIPAA and other standards?
Compliance varies by vendor — not all AI solutions are built for clinical environments where patient data is involved. Verify that any platform you deploy holds current HIPAA compliance, SOC 2 certification, HITRUST certification, and relevant ISO standards. These are the baseline for operating safely in a health system environment, not optional additions. IntelePeer’s architecture is built compliance-first.
IntelePeer delivers safety-first Agentic AI for healthcare — purpose-built digital staff that execute real operational workflows across the patient communication journey, from the first call to final payment, with the integrations, governance, and human escalation that health systems require. Learn more at intelepeer.ai