“Why should we care what the government’s doing with AI?”
That’s the question we often hear from healthtech operators—especially those heads-down on go-to-market or regulatory pathways for a new AI-driven tool.
The answer? Because the U.S. government just laid out a comprehensive blueprint for responsible AI development and acquisition—and it’s full of lessons for the private sector.
Whether you're building AI, selling to hospitals or payers, or evaluating vendors, the White House’s April 2025 AI memos (M-25-21 & M-25-22) offer a framework likely to be recognized by federal regulators—it’s a publicly endorsed standard for ethical, scalable AI that indicate what regulators might accept in terms of AI quality, safety, and implementation.
Let’s break it down.
What Happened? The Quick Context
In January 2025, the Trump administration revoked Biden’s AI executive order and replaced it with a new one: EO 14179. That move was followed by two key OMB memos on April 3:
M-25-21: "Accelerating Federal Use of AI"
Sets internal rules for how federal agencies govern, deploy, and monitor AI—with a focus on innovation, risk management, and public trust.
M-25-22: "Driving Efficient Acquisition of AI in Government"
Lays out detailed procurement standards—from risk assessments and transparency to privacy protections, IP rights, and vendor accountability.
Together, they form a playbook for building and buying AI responsibly—and one that private healthcare companies would be smart to borrow from, especially if they’re interested in selling into government. In my experience, these federal standards have a trickle-down effect to states and private standards organizations, so I recommend companies would pay close attention even if they aren't involved in federal procurement.
5 Lessons Digital Health Companies Should Steal from M-25-22
Even though these memos are directed at federal agencies, they’re likely to become a regulatory baseline—a set of expectations other agencies (and eventually private sector regulators) will refer to. They’re built on established principles for responsible AI deployment—like transparency, fairness, and risk management—and now come with the weight of federal endorsement.
1. Form a Cross-Functional AI Acquisition Team
The memo requires agencies to include legal, IT, cybersecurity, privacy, clinical, and operational voices before issuing RFPs or purchasing AI.
Private sector takeaway:
Don’t let procurement or engineering run the show in isolation. Bring in compliance, legal, clinical, and equity experts early.
2. Define High-Impact AI Use Cases Upfront
Agencies must identify whether a system is “high-impact”—i.e., it influences rights, healthcare access, safety, or critical infrastructure.
Private sector takeaway:
If your AI affects care decisions, diagnosis, patient triage, or payer approvals, treat it as “high-impact”—and build in appropriate risk management.
3. Bake in Portability & IP Rights to Avoid Vendor Lock-In
The guidance pushes agencies to negotiate data portability, model interoperability, and clear IP terms—so they don’t get stuck with closed systems.
Private sector takeaway:
Whether you're buying or selling AI, address this now. Can the model be retrained with your data? Do you own outputs? Can you switch vendors without starting from scratch?
4. Use Performance-Based Contracts
Agencies are told to use metrics-based contracting and real-world testing pre- and post-purchase.
Private sector takeaway:
If you're selling AI, expect scrutiny on performance claims. If you're buying, demand transparency: How is the model validated? How will success be measured over time?
5. Plan for Sunset or Replacement of Underperforming AI
The memos urge agencies to create sunset criteria—triggers for when to retire or replace underperforming tools.
Private sector takeaway:
Don’t let your AI tools run indefinitely on autopilot. Set clear thresholds for decommissioning, retraining, or escalation if the tech stops performing safely or fairly.
How M-25-21 Shapes Internal Governance
While M-25-22 focuses on procurement, M-25-21 handles the inside game: how agencies govern AI use from the inside out.
It requires:
- Appointing Chief AI Officers (CAIOs)
- Standing up AI governance boards
- Maintaining public AI inventories
- Monitoring and remediating high-risk tools
- Conducting impact assessments and ongoing audits
For healthtech companies, this mirrors what you’ll need to show to partners, regulators, and future investors. If your team can’t articulate how you're managing model drift, bias, or unintended use, you’re not ready for prime time.
Why It Matters for the Private Sector—Especially in Healthcare
You might not be a government agency—but if you’re:
- Selling AI into hospitals, payers, or public health orgs
- Building AI tools used in triage, diagnosis, or benefit decisions
- Evaluating vendors offering AI-enhanced solutions
…then this framework is your surrogate compliance guide.
It offers a market-tested, regulator-aligned structure that:
- Increases buyer confidence
- Protects patient trust
- Anticipates future regulation
- Reduces long-term vendor risk
And remember: while the White House guidance isn’t binding on you, the FTC, FDA, and HHS are watching. Borrowing from these memos won’t just keep you compliant—it will help you build a stronger product.
Final Thoughts: This Is the AI Procurement Playbook Healthcare Needs
Digital health companies can’t afford to treat compliance as an afterthought. Whether you're building the next diagnostic algorithm or deploying an AI-powered intake system, M-25-21 and M-25-22 offer a roadmap worth copying.
If you're looking for:
- A checklist for AI vendor evaluations
- Contract templates with privacy & IP protections
- Internal governance frameworks based on the NIST AI RMF
We’ve got you covered.
Let’s talk about future-proofing your AI roadmap »
Let’s build smarter, safer, and more trusted healthcare AI—together.