AI Governance Consulting for Medical Device Manufacturers

AI governance consulting for medical device manufacturers is the practice of building Human-in-the-Loop (HITL) controls, audit trails, and compliance frameworks that let regulated manufacturers deploy AI under FDA 21 CFR Part 11, ISO 13485, and EU MDR requirements. Our team at QServices closes the gap between your current AI deployment and what your quality system actually requires. See our regulated industry solutions to understand how we work across sectors.

Why medical device manufacturers need AI governance right now

The FDA published its AI/ML-based Software as a Medical Device (SaMD) action plan and has since moved toward mandatory predetermined change control plans for any AI system that updates post-deployment. EU MDR, fully applicable since May 2021 for new devices, requires manufacturers to maintain a quality management system covering software lifecycle under Article 10. Notified bodies are asking about AI systems during audits. This is not a future compliance question.

Add to that the four operational problems every VP of Quality and Head of Regulatory in this industry is already managing: validation and documentation overhead that slows every deployment, post-market surveillance data scattered across disconnected systems, regulatory submissions that routinely take months to assemble, and legacy ERP platforms like SAP and Oracle EBS that cannot exchange data with QMS tools like MasterControl or Veeva Vault. Deploying AI without a governance framework on top of that situation compounds all four problems.

ISO 13485:2016 Section 7.5.6 requires documented validation for software used in production and service provision. AI systems are not exempt. If your team cannot produce a validation record for the AI running in your quality workflows, you have a finding waiting to happen.

What we build for medical device clients

Every engagement produces operational artifacts your quality team can use directly, not a policy document to file and forget.

• Compliance-mapped AI policy framework: A working policy that maps each AI system to specific requirements under FDA 21 CFR Part 11, ISO 13485, and EU MDR, covering data integrity, software validation, and change control. Directly reduces the documentation overhead your team faces before each regulatory submission.
• Human-in-the-Loop checkpoint design: We design the review gates between AI output and downstream action. For high-stakes decisions — flagging a nonconformance, recommending a CAPA, or triggering a post-market alert — a human approves before the system acts. We design these checkpoints so they scale with your team's actual capacity, not against it.
• Audit trail architecture: Every AI decision gets a timestamped log covering input data, model version, confidence score, reviewer identity, and final disposition. Built to satisfy FDA 21 CFR Part 11 electronic records requirements. Integrates with Veeva Vault or MasterControl as your single audit record.
• Post-market surveillance pipeline: We help consolidate fragmented surveillance data so AI can actually monitor it in one place. This addresses the real problem directly: signals sitting in three separate systems with no automated aggregation and no audit trail connecting them.
• Drift monitoring on Azure AI Foundry: Production-grade evaluation that detects model drift before performance degrades past your validated threshold. Alert thresholds are set and owned by your quality team, not our engineers.

How an AI governance consulting engagement actually works (step by step)

Most engagements run 4 to 12 weeks depending on the number of AI systems in scope and how complete your existing QMS documentation is. The phase structure below applies to a mid-range project covering one to three AI systems.

1. Week 1-2: Regulatory and system audit. We review your existing AI systems, QMS documentation, and data flows. We map each system to its obligations under FDA 21 CFR Part 11, ISO 13485, and EU MDR. HITL checkpoint: findings are presented to your VP of Quality and Head of Regulatory before any design work begins. Nothing gets designed until you agree on scope.
2. Week 2-4: Governance framework design. We draft the policy framework, HITL checkpoint specifications, and audit log schema. Document-level work; no code yet. HITL checkpoint: your legal and quality teams review and approve the policy draft before implementation starts.
3. Week 4-8: Technical implementation. We build the audit trail architecture, HITL review interfaces, and drift monitoring on Azure AI Foundry. We integrate with your existing stack: SAP, Oracle EBS, Veeva Vault, or MasterControl, depending on your environment.
4. Week 8-10: Validation protocols. We write the IQ/OQ/PQ validation protocols for the governance system itself. ISO 13485 requires it, and we deliver the validation package as part of the engagement, not as a separate project later.
5. Week 10-12: Handover and training. Your QA team is trained on the HITL review interfaces and audit trail processes. We deliver the complete documentation package formatted for regulatory submission. HITL checkpoint: final sign-off from your VP of Quality before the engagement closes.

What this costs

AI governance consulting for a medical device manufacturer typically runs $15,000 to $90,000. The range is wide because scope varies significantly: a governance policy review for one AI system under a single regulator is a very different project from building full audit trail architecture, HITL interfaces, and drift monitoring for five systems under both FDA and EU MDR.

Drives cost up:

• Multiple AI systems in scope (each needs its own validation package)
• Dual-regulator scope covering both FDA 21 CFR Part 11 and EU MDR (add 15-25% for regulatory overhead)
• Integrations with SAP, Oracle EBS, or MasterControl (add $3,000-$12,000 per system)
• Production Azure AI Foundry drift monitoring setup (add $5,000-$15,000)
• Third-party compliance review by an external auditor (add $5,000-$20,000)

Keeps cost down:

• Mature QMS documentation already in place in MasterControl or Veeva Vault
• Single regulator scope (FDA only or EU MDR only)
• One AI system for the initial engagement
• Your team handles IQ/OQ/PQ writing using our templates

See our AI governance consulting cost guide for detailed breakdowns by project size. For our full service scope, visit the AI governance consulting service page.

Three things medical device buyers usually get wrong

1. Governance as paperwork instead of operational practice. The most common mistake is producing a governance policy document, filing it, and continuing to operate AI the same way as before. FDA inspectors and EU notified bodies look for evidence that HITL controls operate in practice, not just in writing. A policy that describes a process nobody follows does not reduce regulatory risk. It creates a documented gap between your written procedure and your actual practice. Governance has to live inside your QMS workflows, not alongside them.

2. Designing HITL that humans cannot actually scale. We have worked with systems where every AI output requires human review before proceeding. In practice, the quality team ends up approving 400 decisions a day in under two seconds each, because they do not have time to actually read them. That is not Human-in-the-Loop governance, it is checkbox theater. Effective HITL design matches review depth to decision stakes and decision frequency. High-stakes, low-frequency decisions get genuine review. High-frequency, low-stakes decisions get automated with spot-check auditing.

3. No drift monitoring after the validation package is filed. Both ISO 13485 and EU MDR require post-market surveillance. If your AI model drifts after launch, and all production models drift, you have a validation gap that grows wider over time. We regularly see manufacturers who built a solid validation package at launch but have no mechanism to detect when model performance degrades. Drift monitoring on Azure AI Foundry catches this before it becomes a post-market surveillance finding or a CAPA driver.

Recent work with medical device clients

We do not have a published case study for medical device AI governance available yet. Our governance and HITL work comes primarily from regulated industries with substantively overlapping requirements: financial services firms under FCA oversight, insurance carriers with state regulatory obligations, and healthcare data platforms operating under HIPAA. The validation documentation burden, audit trail architecture, and HITL design challenges are consistent across all of them.

If you want to discuss a specific scenario — an AI system recommending CAPAs inside Veeva Vault, or a post-market surveillance aggregation pipeline that needs a documented review process under FDA 21 CFR Part 11 — schedule a call with our team. We will walk through exactly how we would approach it.

How long does AI governance consulting take for a medical device manufacturer?

A focused engagement covering policy framework, HITL checkpoint design, and audit trail architecture for a single AI system runs 4 to 6 weeks. A full implementation covering multiple AI systems, IQ/OQ/PQ validation protocols, Azure AI Foundry drift monitoring, and integration with Veeva Vault or MasterControl runs 10 to 12 weeks. FDA and EU MDR validation requirements, not the technical build, are the primary driver of timeline.