Every week we talk to practice owners who ask the same question in different words: "I get that missed calls cost me money. But what would an AI receptionist actually be worth to my practice, in dollars?"
It's a fair question, and it deserves a real answer — not a vague promise. So instead of telling you another success story, this post does something different: we're going to build the complete ROI model for a $1.2M-per-year dental practice, line by line, with every assumption out in the open.
One thing before we start, because we think transparency matters: the practice below is a modeled scenario, not a named client. The inputs come from published industry call-volume benchmarks, the patterns we see across the practices we serve, and our own public pricing. We're showing you the math precisely so you can swap in your own numbers and see what the result looks like for your practice — not ours.
Let's get into it.
The practice profile
Here's the practice we're modeling. If you run a two- to three-doctor general dentistry office, this should look familiar:
- Annual collections: $1.2M (roughly $100,000/month in production)
- Providers: 2 dentists, 3 hygienists
- Front desk: 2 staff members who answer phones and check patients in, verify insurance, handle checkout, and manage recall
- Inbound call volume: ~1,100 calls/month (industry studies consistently put a practice this size between 900 and 1,300)
- Hours: Monday–Thursday 8–5, Friday 8–1
- Market: a mid-sized Florida metro with a significant Spanish-speaking population — relevant, as you'll see
Nothing exotic. This is the median American dental practice, give or take.
The problem: where calls actually die
The uncomfortable industry consensus is that dental practices miss somewhere between 30% and 40% of their inbound calls. Not because front desks are lazy — because the job is structurally impossible. The phone rings while your coordinator is checking out a patient, explaining a treatment plan estimate, or on the other line with an insurance company. Lunch hour, when patients are free to call, is when your team eats. And every call that comes in after 5 PM Thursday or 1 PM Friday goes straight to voicemail until the next business day.
For our modeled practice, let's be moderately conservative and assume a 35% miss rate. That's 385 missed calls per month out of 1,100.
Now, not every missed call is a lost appointment. Some are vendors, some are wrong numbers, some patients call back. Industry data suggests:
- Roughly 40–50% of missed calls at a dental office are appointment-related (new patient inquiries, bookings, reschedules)
- Only about 20–30% of callers who hit voicemail leave a message; the majority simply call the next practice on their list
- New-patient callers are the least patient of all — they have no loyalty yet and three competitors one tap away
Apply that to 385 missed calls: about 170 of them were appointment-related, and the practice realistically recovers maybe 50 of those through voicemails and call-backs. That leaves roughly 120 appointment opportunities per month evaporating — most invisible, because nobody logs a call that was never answered.
You don't lose all 120, of course. Some were existing patients who eventually got through. But if even 15–20% of them were genuinely lost bookings, that's 18–24 lost appointments a month. Hold that number.
What the deployment looks like
In this model, the practice deploys a bilingual AI receptionist on our Pro tier ($450/month). The setup process — the same one outlined on our how it works page — takes five business days:
Days 1–2: The practice completes an intake covering services, insurance participation, scheduling rules (e.g., "new patient cleanings get 60 minutes, only book Dr. Reyes for implant consults"), and the FAQs the front desk answers fifty times a week.
Days 3–4: The AI is configured and connected to the practice's scheduling system, then tested against realistic call scripts — in English and Spanish — until the office manager signs off.
Day 5: The practice forwards calls. Most offices start with overflow-and-after-hours only: the front desk still answers what it can, and the AI catches everything that would have otherwise rung out. That's the configuration we model here, because it's the most common and the most conservative.
Nothing about the front desk changes. Nobody is replaced. The AI simply answers the calls that were previously going nowhere.
The results model: what the math says
Here's where we plug in numbers. Each assumption is stated so you can challenge it.
Call capture. With overflow and after-hours routing, the effective miss rate drops from 35% to the low single digits — the AI answers in two rings, around the clock. In our model, the practice goes from 385 unanswered calls to under 40 (a few hang-ups and abandoned dials will always exist). That's consistent with the 40–60% missed-call reductions we consider realistic to promise, and on the optimistic end of it because overflow routing catches nearly everything.
Recovered bookings. Of the ~170 appointment-related calls that were previously missed each month, the AI now answers essentially all of them. It books directly into the schedule. Modeling a conservative booking conversion — not every answered call becomes a kept appointment — we land at 18 additional appointments per month, right in the middle of the 15–30 range we see across practices this size.
The value of those appointments. This is the input that swings the model most, so let's split it:
- Assume 11 of the 18 are existing patients (hygiene, restorative follow-up, reschedules that would otherwise have leaked). Average production per visit at a practice like this: ~$280. That's $3,080/month.
- Assume 7 are new patients. The first-visit value is modest, but a retained new dental patient is worth $400–$1,200 in year one and several thousand dollars over a multi-year relationship. Using a deliberately conservative $650 first-year value: $4,550/month in acquired patient value.
- The bilingual effect. Our modeled practice sits in a Florida metro where a meaningful share of inbound calls are Spanish-preferred. With an English-only front desk, many of those calls previously went badly even when answered. We've left this out of the headline numbers to keep the model honest — but practices in similar markets routinely see Spanish-language bookings become 15–25% of AI-handled appointments. Consider it upside.
Total modeled gain: roughly $7,600/month in combined production and first-year new-patient value.
The cost side
The Pro tier runs $450/month. Setup is included. There's no per-minute overage surprise at this call volume, and no additional staffing cost — remember, the front desk keeps doing exactly what it was doing.
Compare the alternatives for plugging the same gap:
- A third front-desk hire: $38,000–$45,000/year plus benefits, and they still can't answer at 9 PM or in two languages at once.
- A human answering service: at this volume, typically $800–$1,500/month — and most can only take messages, not book appointments into your calendar.
- Doing nothing: $7,600/month in modeled leakage, indefinitely.
Full tier details are on our pricing page.
The ROI calculation
Now the arithmetic:
- Monthly gain (modeled): ~$7,600
- Monthly cost: $450
- Net monthly benefit: ~$7,150
- ROI: roughly 16:1
- Payback period: the first two or three recovered appointments — typically inside the first week
Suppose you think our assumptions are too rosy. Fine — cut everything in half. Nine extra appointments instead of eighteen, $3,800 in monthly value instead of $7,600. The ROI is still better than 7:1, and the system still pays for itself with two hygiene visits. That's the point of building the model conservatively: it doesn't need optimistic inputs to clear the bar by a wide margin.
For a $1.2M practice, capturing $7,600/month in previously-lost value is a 7–8% revenue lift with zero additional chair time, zero marketing spend, and zero new hires. Most practices spend far more than $450/month on marketing to generate calls they then statistically fail to answer.
What the model doesn't capture
In the interest of the same transparency we started with, here's what the spreadsheet can't fully price:
- Recall leakage. Patients who call to reschedule a hygiene visit, hit voicemail, and silently fall out of recall. Recapturing even a few of these compounds for years.
- Review and referral effects. Patients consistently describe "they actually answer the phone" as a reason for five-star reviews. We can't honestly assign a dollar figure to that, so we didn't.
- Front-desk burnout. Two coordinators no longer apologizing to the patient in front of them while the phone rings behind them. Ask any office manager what that's worth in retention.
- The downside risks. An AI receptionist must be configured well. A poorly set-up agent that misquotes your insurance participation creates work instead of saving it — which is exactly why our setup process ends with your office manager signing off on test calls, not with a vendor flipping a switch.
Would this math work for your practice?
The honest answer: it depends on your inputs. If your phones are already answered 95%+ of the time including evenings, in every language your patients speak — this won't move your needle much, and we'll tell you so.
But if you're like the practice in this model — solid production, busy front desk, no after-hours coverage, and a call report you've maybe never pulled — the math above is probably operating on you right now, just in the wrong direction.
Here's the fastest way to check: call our demo line at (954) 475-6922 and have a conversation with the AI receptionist yourself, in English or Spanish. Then pull your own call report, plug your numbers into the model above, and see what your version of the spreadsheet says.
Five days from intake to live. No new hires. The phone just gets answered.