How Pharma Companies Build AI Strategies That Pass FDA Scrutiny

Last updated: 2026-03-30

Something significant is shifting inside pharmaceutical boardrooms right now. The question is no longer whether to adopt artificial intelligence — it's how to do it in a way that a regulator won't dismantle. Google Trends data shows 'FDA artificial intelligence' sitting at 72 out of 100 in search momentum, and that number reflects a real anxiety playing out in compliance departments, R&D teams, and C-suites across the industry.

Having worked with 200+ clients across regulated industries over the past eight-plus years — including pharmaceutical manufacturers, biologics companies, and medical device firms — I've watched the AI governance conversation evolve from theoretical to urgent. The companies that are winning regulatory acceptance aren't the ones deploying the most sophisticated models. They're the ones that built the right structure around their AI before anyone from the FDA walked through the door.

This article breaks down exactly how they're doing it.

Why FDA Scrutiny of AI Is Intensifying in 2025 and Beyond

The FDA's interest in AI is not new, but its teeth are sharper than they've ever been. The agency's 2023 Discussion Paper on AI/ML in Drug Development signaled a clear directional shift: the FDA expects sponsors to demonstrate not just that an AI model works, but that they understand why it works, when it might fail, and who is accountable when it does.

Several regulatory signals define the current landscape:

• The FDA has received over 500 AI/ML-enabled medical product submissions since 2016, with the annual rate accelerating sharply after 2021.
• The agency's Predetermined Change Control Plan (PCCP) framework, codified in the 2022 Food and Drug Omnibus Reform Act (FDORA), requires developers to document anticipated AI model changes before deployment — a fundamentally new compliance discipline.
• FDA's Good Machine Learning Practice (GMLP) guiding principles, developed jointly with Health Canada and the UK's MHRA, now establish a de facto international baseline for AI model validation in regulated healthcare contexts.
• According to a 2024 Deloitte Life Sciences survey, 78% of pharmaceutical executives cited regulatory uncertainty as their top barrier to scaling AI initiatives — up from 61% just two years prior.

The momentum is real. So is the regulatory risk for companies that mistake speed for strategy.

The Foundational Mistake Most Pharma AI Programs Make

The single most common error I see when pharmaceutical companies engage Certify Consulting is this: they build the AI first and try to retrofit the governance afterward.

This approach fails for a predictable reason. FDA reviewers — particularly those examining submissions involving AI-assisted clinical trial design, pharmacovigilance signal detection, or manufacturing quality control — are trained to look for evidence of design-stage intent. They want to see that risk was assessed before the model was trained, not after it was deployed.

Building a pharma AI strategy that passes FDA scrutiny means inverting the typical technology adoption sequence. Governance architecture must precede model selection.

The Five Pillars of an FDA-Acceptable Pharma AI Strategy

1. Establish a Documented AI Governance Framework

The FDA does not currently mandate a specific AI management system standard for pharmaceutical companies, but ISO 42001:2023 — the international standard for AI management systems — is rapidly becoming the reference architecture that sophisticated sponsors cite in their submissions.

ISO 42001:2023 clause 6.1.2 specifically requires organizations to conduct an AI risk assessment that considers the probability of AI-specific harms, their severity, and the organization's ability to detect and respond to failures. For a pharma company, this means mapping each AI use case (adverse event detection, formulation optimization, clinical data analysis) to a documented risk tier before any model development begins.

At minimum, your governance framework should address:

• Organizational accountability: Who owns AI decisions? This needs to be a named role, not a department.
• Use case classification: Not all AI is equal. A scheduling optimization tool carries different risk than an AI system flagging pharmacovigilance signals.
• Model lifecycle policy: From training data sourcing through decommissioning, every phase needs a documented owner and approval gate.
• Escalation pathways: What triggers human review? What triggers regulatory notification?

2. Align AI Validation to Existing Quality System Architecture

Pharmaceutical companies already operate under some of the most rigorous quality management system requirements in any industry — 21 CFR Part 11, 21 CFR Parts 210/211, ICH Q10. The companies building AI strategies that pass FDA scrutiny are not creating parallel governance structures. They are integrating AI validation into existing QMS workflows.

This matters practically because FDA investigators expect to see AI-related documentation in the places they already know to look. A standalone AI policy document that lives outside your quality system is a red flag, not a green one.

Key integration points include:

• Change control: All AI model updates, including retraining events, should route through your existing change control procedure. FDORA's PCCP framework essentially mandates this thinking at the regulatory submission level.
• CAPA system: Model performance degradation — drift — should trigger a corrective and preventive action, just as an out-of-specification laboratory result would.
• Document control: Training data versioning, model architecture documentation, and validation protocols belong in your controlled document system.
• Training records: Employees who interact with AI-generated outputs need documented competency assessments, not just click-through acknowledgments.

3. Build a Predetermined Change Control Plan From Day One

The Predetermined Change Control Plan is the FDA's most consequential AI-specific regulatory innovation in recent years. Authorized under FDORA Section 3308 and elaborated in FDA's 2024 draft guidance on PCCPs for AI/ML-enabled devices, the framework requires developers to articulate what changes they anticipate making to an AI model after approval, and how they will validate those changes without seeking new regulatory clearance for each update.

For pharmaceutical applications, a PCCP should document:

• Performance specifications: What metrics define acceptable model performance? What thresholds trigger mandatory revalidation?
• Data management protocols: How will training data be updated? What quality controls govern new data ingestion?
• Verification and validation protocols: What testing regiment will confirm that an updated model meets its original design intent?
• Impact assessment criteria: How will the organization determine whether a proposed change exceeds the PCCP's scope and therefore requires a new submission?

Pharma companies that draft PCCPs retroactively — after their AI is already in use — face a harder conversation with the FDA than those that submit one proactively. The document signals regulatory maturity. Its absence signals the opposite.

4. Implement Algorithmic Transparency and Explainability Standards

The FDA has been explicit that it considers algorithmic transparency a patient safety issue, not merely a technical nicety. FDA's 2021 Action Plan for AI/ML-Based Software as a Medical Device calls for the establishment of "good machine learning practices" that include transparent reporting of model performance across demographically diverse subgroups.

For pharmaceutical companies, this has several operational implications:

For clinical AI applications (trial design, endpoint analysis, biomarker identification): Sponsors must be prepared to explain how their AI reached conclusions that informed regulatory decisions. Black-box models — even highly accurate ones — face increasing resistance in submission reviews.

For manufacturing AI applications (process analytical technology, real-time release testing): The FDA's guidance on PAT (Process Analytical Technology) already establishes a framework for continuous process verification. AI systems embedded in manufacturing must demonstrate that their outputs are interpretable by qualified personnel and auditable after the fact.

For pharmacovigilance AI (signal detection, case processing): The FDA expects sponsors to document false negative rates, not just overall accuracy. A model that is 95% accurate overall but systematically misses signals in rare disease populations is not an acceptable pharmacovigilance tool regardless of its aggregate performance.

5. Create Audit-Ready Documentation Before You Need It

The 100% first-time audit pass rate I've maintained across Certify Consulting's client engagements over eight-plus years comes down to one discipline above all others: documentation that tells a coherent story before an investigator asks the first question.

For pharma AI programs, audit-ready documentation means:

• A master AI inventory that lists every AI system in use, its risk classification, its validation status, and its last review date
• Training data provenance records that document where data came from, how it was cleaned, and who approved it for use
• Model validation reports that follow a format parallel to your existing analytical method validation reports — protocol, execution, results, conclusion, approval signatures
• Bias assessment documentation that demonstrates you actively looked for and addressed performance disparities across relevant patient subgroups
• Incident logs for any AI-related events where model output was questioned, overridden, or contributed to a quality event

FDA AI Scrutiny by Pharma Use Case: A Comparative Overview

What Separates Companies That Pass From Companies That Struggle

After eight-plus years and hundreds of engagements in regulated industries, the differentiator is rarely technical capability. The companies that sail through FDA scrutiny of their AI programs share three characteristics that struggling companies lack:

First, they involve regulatory affairs in AI decisions before IT does. Technology selection should follow risk classification, not precede it. A RA professional who understands the regulatory context for a given AI application is more valuable in the early stages than a data scientist who understands the algorithm.

Second, they treat AI governance as a quality function, not a legal function. Legal teams are essential for contracts, liability, and IP — but quality professionals who understand GMP, validation, and audit methodology are the ones who build AI programs that survive inspection.

Third, they build for the second review, not just the first. FDA reviewers return. Inspections recur. The companies that think about how their AI documentation will read to an investigator three years from now — after model updates, personnel changes, and additional data — build fundamentally more durable programs.

Getting Started: A Practical Roadmap for Pharma Leaders

If you're a pharmaceutical executive or quality leader beginning to formalize your AI strategy, here is a sequenced approach based on what works in practice:

1. Conduct an AI inventory audit — identify every AI system currently in use or in development, regardless of who procured it or what department owns it
2. Risk-stratify your AI portfolio — not every tool needs the same level of governance; reserve your most rigorous documentation for patient-facing and regulatory-decision-influencing applications
3. Map AI governance to your existing QMS — identify the specific procedures (change control, CAPA, document control, training) that need to be updated to cover AI
4. Develop or adopt an AI policy framework — ISO 42001:2023 provides the international reference architecture; align your internal policy to its structure
5. Draft PCCPs for any AI approaching regulatory submission — do this in parallel with model development, not after
6. Train key personnel — quality, regulatory affairs, clinical operations, and IT staff all need role-specific AI governance training
7. Conduct a pre-audit readiness assessment — before any FDA interaction involving AI, have an independent reviewer stress-test your documentation

For pharmaceutical companies looking to build a comprehensive AI governance program, understanding the connection between AI strategy and regulatory compliance is the essential starting point. And for organizations exploring how ISO 42001 certification strengthens their regulatory posture, our analysis of AI management system frameworks provides the operational detail you need.

Citation Hooks: Key Authoritative Statements

Pharmaceutical companies that integrate AI validation into existing QMS architecture — rather than building parallel governance structures — consistently achieve stronger FDA audit outcomes because investigators find AI documentation in the locations and formats they are already trained to evaluate.

The FDA's Predetermined Change Control Plan framework, authorized under FDORA Section 3308, represents the most significant structural shift in pharmaceutical AI regulatory compliance since the agency's 2021 AI/ML Action Plan, requiring sponsors to document anticipated model changes before deployment rather than seeking approval for each update post-market.

An AI governance program built on ISO 42001:2023's risk assessment requirements — particularly clause 6.1.2's mandate for harm probability, severity, and detectability analysis — provides pharmaceutical companies with a documented, internationally recognized basis for demonstrating regulatory intent to FDA reviewers.

Frequently Asked Questions

Does the FDA require pharmaceutical companies to certify their AI systems? The FDA does not currently require a specific AI certification for pharmaceutical manufacturers, though AI/ML-enabled software meeting the definition of a medical device must follow SaMD regulatory pathways. However, FDA investigators increasingly expect to see documentation aligned with recognized frameworks like ISO 42001:2023 and the agency's own Good Machine Learning Practice principles during inspections of AI-assisted processes.

What is a Predetermined Change Control Plan and does my pharma company need one? A PCCP is a document submitted to the FDA that describes anticipated changes to an AI/ML system after approval and the validation approach for each. It is currently required for AI/ML-enabled medical devices under FDORA Section 3308. Pharmaceutical companies using AI in regulatory-decision-influencing applications — including pharmacovigilance, CMC analysis, and clinical trial design — should develop PCCP-equivalent documentation as a matter of proactive compliance, even where not yet mandated.

How does ISO 42001:2023 relate to FDA AI requirements? ISO 42001:2023 is an international AI management system standard, not an FDA regulation. However, it provides a structured framework for AI risk assessment, governance, and lifecycle management that maps well to FDA expectations articulated in the agency's AI Action Plan and GMLP principles. Companies certified to ISO 42001 have a documented, third-party-validated governance architecture they can reference in regulatory submissions and inspection responses.

What are the most common FDA findings related to pharmaceutical AI programs? Based on industry experience, the most common deficiencies include: absence of a documented AI governance policy, failure to route AI model updates through change control, insufficient training data documentation and provenance records, lack of bias or subgroup performance assessment, and missing human oversight protocols for AI-generated outputs in regulated workflows.

How long does it take to build an FDA-compliant pharma AI governance program? For organizations with a mature QMS already in place, integrating AI governance typically requires three to six months of structured effort — including policy development, procedure updates, personnel training, and documentation of existing AI systems. Organizations starting from a lower QMS maturity baseline should plan for six to twelve months. Starting with an AI inventory and risk stratification assessment accelerates the process significantly.

Jared Clark, JD, MBA, PMP, CMQ-OE, CPGP, CFSQA, RAC is the Principal Consultant at Certify Consulting, where he has guided 200+ clients through regulatory compliance challenges with a 100% first-time audit pass rate. Learn more at certify.consulting.

Last updated: 2026-03-30