The AI Governance Gap: What MedTech Boards Aren't Asking

Your employees disclosed proprietary information to an AI system last week. You don't know who, you don't know what, and they had no idea it was a problem.

Meanwhile, your competitors are operationalizing AI to move faster with smaller teams. They're compressing cycle times by up to 55% while your team works like it's 2024.

The question isn't whether to use AI. That's already happening, whether you like it or not. Google's search results increasingly include AI-generated summaries and answers. Free ChatGPT is a browser tab away. Code assistants are embedded in every modern developer's IDE. Suggested text auto-completes emails and documents. The question is whether AI is being used with governance or without it.

AI collides with three forces that lie in permanent tension: Acceleration, Assurance, and IP. Every MedTech organization now navigates this triangle, even if they haven't recognized their predicament. The challenge is surviving the interaction of these three forces without breaking regulatory trust, destroying defensible intellectual property, or falling behind the competition.

Acceleration: The Business Cannot Wait

AI’s promise in MedTech is not speculative. It is immediate and operational.

Teams are using AI to:

• Compress design and documentation cycles
• Write and review software—including embedded software
• Draft SOPs, CAPAs, and audit responses
• Summarize regulatory guidance and prior submissions
• Generate marketing materials and competitive analysis

These businesses aren't blindly "moving fast and breaking things" using generative AI. They're optimizing time-to-artifact and time-to-decision in an industry where delays are existential. Choosing not to accelerate no longer looks prudent; it looks like strategic negligence.

But acceleration creates pressure. When work moves faster than governance structures, employees will follow a path of least resistance. They will paste proprietary code into public tools. They will generate documentation that cannot be traced. They will rely on outputs whose ownership is legally ambiguous. Acceleration without constraint does not merely increase business risk—it opens new risk frontiers.

Assurance: Regulation Is Not the Bottleneck—Confidence Is

MedTech is not regulated because regulators dislike innovation. It is regulated because the cost of error is human harm. The real currency of the industry is not speed, but confidence: confidence that systems work as intended, that decisions are traceable, and that outcomes can be defended under inspection, litigation, or recall.

This is why Assurance matters more than "compliance." You must demonstrate control, not just claim it.

AI strains assurance not because it is new, but because it is opaque. When AI contributes to requirements, code, or documentation, organizations must answer uncomfortable questions:

• Who is responsible for this output?
• How was it reviewed?
• What assumptions did it encode?
• Can we reproduce or explain it six months later?

When assurance frameworks lag behind reality, a predictable failure mode emerges: shadow AI, where your employees use AI unsupervised and outside approved systems. This can happen through ignorance, because they've had no training, or because governance processes are perceived as blockers rather than enablers.

An engineer pastes error messages into free ChatGPT. A regulatory writer asks Google—now powered by AI summaries—to clarify guidance language. A quality manager uses an AI assistant to draft a CAPA response. None of these actions is malicious. All of them are ungoverned. At that point, assurance collapses silently, not visibly.

IP: The Hidden Failure Mode

Acceleration and assurance create visible tensions. IP risk is the quiet one.

Under current U.S. law, much of what AI produces is not automatically protectable. Copyright requires human authorship. Patents require human inventorship. If AI generates expressive or inventive material with insufficient human contribution, that output has no owner under copyright law.

This creates a paradox most companies have not confronted:

• The more AI does, the less IP you actually own.
• The faster you move, the more likely you are to create assets you cannot defend.

Trade secrets are your only remaining defense for AI-generated IP, but only if secrecy is actively maintained. Public AI tools, permissive vendor terms, and casual employee behavior can destroy trade secret protection instantly. Once proprietary information is disclosed without adequate controls, ownership does not degrade—it evaporates.

IP risk in AI is not confined to the legal department. It is operational:

• Which tools are approved?
• What data can be entered?
• What are the processes to identify and protect critical IP?

Without deliberate processes and policies, AI converts intellectual property from a durable asset into collateral damage of productivity. If you paste IP developed at the cost of tens of millions of dollars into an unsecured AI, you may as well print it in a textbook.

The Core Tension: No Force Can Win Alone

MedTech organizations do not fail at AI because they choose the wrong force. They fail because they optimize one while ignoring the others. Speed without governance invites regulators. Governance without speed drives AI underground or opens room for competition. Neither approach protects your IP.

The Board-Level Questions

The right question is not "Do we allow AI?" It is: "Have we deliberately balanced Acceleration, Assurance, and IP—or are we letting employees do it accidentally?"

Four questions your board should answer before the next meeting:

1. Which AI tools are approved, and what data can enter them?
2. How do we establish human contribution sufficient for IP defensibility?
3. What is our assurance framework for AI-assisted outputs?
4. How can we train all employees to use AI effectively, given these limitations?

If you can't answer these, your employees are answering them for you—inconsistently, invisibly, and without your interests in mind.

The companies that solve this first gain a durable advantage: faster iteration inside a defensible IP moat, with audit trails and processes that satisfy regulators and acquirers alike. The companies that don't will learn what ungoverned AI costs—in a courtroom, an FDA inspection, or an acquisition that falls apart in diligence.