An AI Due-Diligence Checklist for Healthcare Deals

More and more healthcare investment theses now include a line about AI: we'll expand margin through automation, we'll modernize the revenue cycle, we'll use AI to do more with the same headcount. It's a reasonable thesis. But a thesis is an assumption, and diligence exists to test assumptions — not to admire them.

If your model assumes AI-driven upside, your diligence has to answer one question: can this platform actually absorb AI, or will the upside die in the same gap where most AI pilots die — the gap between "the technology works" and "the organization can use it at scale"? Here's the pre-close checklist we use to pressure-test that.

1. Data readiness

AI upside is a data story before it's a model story. Assess:

• Does the target actually have the data the thesis depends on — and is it accessible, or trapped in systems that don't talk to each other?
• How many source systems, and how much manual reconciliation happens today? Every integration seam is future cost and risk.
• Is the data quality good enough at the point of use, or only in a cleaned-up analytics environment?

2. The AI claims already in place

If the target already uses AI, separate what's real from what's roadmap:

• Which "AI" is in production and driving measured results, versus in pilot or in a deck?
• Is value being tracked against a baseline, or asserted?
• What's the vendor exposure — lock-in, per-transaction pricing that scales badly, contracts that constrain your value-creation plan?

3. Governance and compliance

In healthcare, weak AI governance isn't just risk — it's a ceiling on how far you can scale. Confirm:

• HIPAA and data-handling posture around any AI in use or planned.
• Whether there's clinical oversight where AI touches care decisions, and a monitoring process for drift and error.
• Whether governance exists as a real practice or only as a policy document. (We go deeper on this in our piece on why PE firms build AI governance before deployment.)

4. Operational absorption capacity

This is the one most diligence misses, and the one that most often determines whether the upside is real:

• Is there an operator who could own AI-driven change, or only an IT function that fields tickets?
• Has the organization successfully absorbed operational change before, or does every initiative stall at adoption?
• Is there internal talent to run and maintain what gets deployed, or is that an unbudgeted future hire?

5. The economics

Finally, make the upside concrete enough to underwrite:

• For each AI-driven line in the thesis, what's the realistic payback window — and does it survive integration and change-management cost?
• What's the ongoing run cost (licensing, compute, oversight), not just the implementation cost?
• If you removed the AI assumptions from the model, does the deal still clear your return threshold? If it doesn't, you're underwriting a pilot.

How to use this

Run this before close, not after. The goal isn't to talk yourself out of AI upside — it's to know which parts are real, which are conditional on work you'll have to fund, and which are optimism. A platform that scores poorly here isn't necessarily a bad deal; it may be a better one, because the AI-driven margin is still on the table and the path to it is now visible. But you want to know that going in, and price it accordingly.

Elevate Ventures supports PE deal teams and operating partners with AI diligence and post-close value creation for healthcare platforms — turning "AI upside" from a thesis line into a sequenced, measurable plan within 90 days. Book a diligence or value-creation call, or read more of our insights.