How to choose an AI validation partner in pharma

Somewhere in your organisation, AI is already inside a GxP process. A vendor slipped a model into your eQMS. A data science team built a deviation classifier that quality now quietly relies on. At some point the question lands on a desk like yours: who do we bring in to validate this properly? The market that answers is crowded, young, and uneven, and the wrong choice costs more than the fee. This is the guide I'd want if I were on the buying side.

Who can help validate AI in a pharma GxP environment?

Three kinds of partner exist: global consultancies for strategy at scale, validation platform vendors whose software carries the evidence, and specialist advisories built around senior practitioners. The right choice depends on where your risk sits. For AI touching GMP decisions, the deciding factor is whether the partner has taken AI through a real inspection.

The global firms, EY, Accenture, Deloitte and their peers, are strongest when the problem is enterprise-wide: an AI strategy across a portfolio, a target operating model, a transformation with a nine-figure budget. Their weakness is the delivery layer, where the person writing your validation rationale may be 3 years out of university and learning GAMP from the same PDF you have.

The platform vendors, ValGenesis, Kneat, and the newer AI-specific tools such as CIMCON's AIValidator, sell software that structures and speeds the validation work. Good platforms genuinely help, and I've deployed several at enterprise scale. What a platform cannot do is make the risk decision for you, and a vendor whose revenue depends on their tool will rarely tell you the tool is the wrong answer.

The specialist advisories, and Trust Bridge Compliance sits in this group alongside firms like ProPharma's quality practice and FIVE Validation, are built around practitioners who have run this work inside pharma rather than observed it from outside. The trade-off is capacity: a specialist won't staff a 40-person programme, and shouldn't pretend to.

What should you look for in an AI validation partner?

Four things: evidence their AI work has faced a health-authority inspection, fluency in GAMP 5 second edition and the draft Annex 22, independence from the tools they recommend, and senior people doing the actual work rather than selling it. A partner missing the first is asking you to be their test case.

The inspection test deserves expansion, because it separates the market cleanly. Plenty of firms can produce an AI validation framework; the slideware is easy. Very few can tell you what an inspector actually asked when they met a validated model, which claims held, and which evidence turned out to matter. Ask the question directly: has AI you validated been examined in a regulatory inspection, and what happened? A confident, specific answer is rare and worth paying for. In my own practice, that answer is 20+ AI tools deployed in live GxP, 4 validated applications, and 3 taken through health-authority inspection with zero compliance observations. Whoever you choose, hold them to that standard of specificity.

Regulatory fluency has a current-affairs component now. The draft Annex 22 went through public consultation in late 2025 and drew around 1,300 comments; the FDA finalised its Computer Software Assurance guidance in February 2026 and published draft guidance on AI in drug development in January 2025. A partner who can't discuss what changed between the drafts, and what it means for your systems specifically, is reciting last year's webinar.

What questions should you ask before you sign?

Six questions do most of the filtering. Who exactly will do the work, named, with their validation history? Which of your AI systems would they classify as high risk, and why? What would they put in front of an inspector for your highest-risk model, and in what order? How do they handle the static-versus-adaptive distinction that Annex 22 draws? What do they think of the tool you already own, and would they say so if the answer were unflattering? And what happens after go-live, since a model's assurance has to be maintained, and drift doesn't announce itself.

Listen for trade-offs in the answers. A partner who tells you some of your AI needs no more than vendor evidence and a risk note is thinking; a partner who quotes a full validation package for everything is billing.

Do you need a global consultancy or a specialist advisory?

Size the partner to the problem. An enterprise AI strategy across 50 systems suits a global firm's bench. Validating specific AI applications, writing the governance that survives an inspector, or moving a validation function from CSV to CSA is specialist work, where one senior practitioner outperforms a rotating project team.

The honest version of the hybrid answer: many organisations use both, a global firm for the programme shell and a specialist for the decisions that carry regulatory weight. That works when the specialist holds the pen on risk classification and validation rationale, and reports to your quality head rather than the programme office.

Sachin Bhandari runs Trust Bridge Compliance, a senior-led advisory for AI in GxP, CSV to CSA and digital validation in pharma, biotech and CDMOs. The thinking behind this guide is worked through fully in the book, Validating AI in GxP: A Practitioner's Guide, and the AI Governance Stack framework is free to download. If you're weighing this decision now, a short inspection-readiness review is a low-risk way to test how a specialist thinks before you commit to anyone, me included.