"AI-enabled operations" shows up in a lot of healthcare investment memos. Fewer memos explain how much EBITDA AI adds, by when, and how the sponsor will know. This post is about underwriting AI for healthcare private equity with the same rigor as pricing or procurement.
Step 1: Diligence the Front Office
Before sizing AI, measure the problem. Questions to answer during diligence:
- What share of inbound calls go unanswered, by location and by hour?
- What is the no-show rate, and how many cancelled slots are refilled?
- What share of patients due for recall actually book?
- What does front-office labor cost per visit, and how much does it vary by site?
- Which EHR and PMS systems are in use, and how many?
Location-level variation is the most useful signal. The gap between the best and worst sites is the clearest estimate of what a standard playbook can recover.
Step 2: Size the Opportunity
| Lever | Diligence metric | How to size it |
|---|---|---|
| Recovered calls | Unanswered call rate | Missed calls x booking rate x visit value |
| Filled schedule | Unfilled cancellations | Open slots x fill rate x visit value |
| Recall | Recall completion rate | Patients due x lift in completion x visit value |
| Labor efficiency | Front-office cost per visit | Hours absorbed by AI x loaded hourly cost |
| Integration speed | Months to platform standard | Months saved x add-on EBITDA gap per month |
Use conservative assumptions and size per location. A plan built on the median location is more credible than one built on the platform total.
Step 3: Assign an Owner
Most AI value creation plans that slip have no single owner. Decide in the 100-day plan who owns AI adoption: an operating partner, the platform COO, or an AI-native execution partner measured on the same KPIs.
Step 4: Track It Like Any Other Lever
- Lock the baseline per location before go-live.
- Report lift monthly by location and workflow.
- Put AI on the EBITDA bridge as its own line, not inside "operations".
See mapping AI to the EBITDA bridge for a full breakdown.
Step 5: Build the Exit Story
Buyers pay for earnings they believe will continue. Location-level data showing a repeatable lift at each new add-on is far more persuasive than a platform-wide average. Read how an AI OS supports healthcare PE exits.
Common Underwriting Mistakes
- Counting AI savings that depend on headcount cuts the operating team hasn't agreed to.
- Assuming every add-on goes live on day one.
- Budgeting for software but not for deployment and change management.
Samara AI Teams (AIT) runs calls, scheduling, intake, confirmations, recall and reviews across multi-location groups. Samara AI Platform (AIP) adds enterprise controls, portfolio-level reporting and add-on onboarding for larger platforms. See how it fits a sponsor plan on our private equity page, or book a demo.
Frequently Asked Questions
How should healthcare PE firms underwrite AI?
Measure the front-office baseline in diligence, size each lever per location with conservative assumptions, assign an owner, and track AI as its own line on the EBITDA bridge.
What diligence metrics matter for AI in healthcare?
Unanswered call rate, no-show rate, unfilled cancellations, recall completion, front-office cost per visit, and the number of EHR/PMS systems in use.
Who should own AI value creation in a healthcare portfolio?
One named owner, such as an operating partner, the platform COO, or an execution partner measured on the same KPIs.
Does AI help at exit?
Yes, when the platform can show location-level data proving a repeatable lift at every site, including recent add-ons.