Why is AI in pharma a challenge?
AI is no longer a future concept in pharma, it’s already shaping how processes, systems and decisions are managed. But while the potential is clear, many organizations still struggle with one key question: how to assess and control AI risk in a way that is both compliant and practical.
In this expert discussion, our SMEs address the most important aspects of AI risk in GxP environments. Drawing on our collaboration with BioPhorum on industry guidance for AI risk management, they explain why AI requires a different approach than traditional systems, how to evaluate risk at both system and feature level, and how to build validation strategies that are consistent, robust and audit-defensible.
What’s the difference between system-level risk vs feature-level risk for AI in pharma, and why does it matter for AI validation / Computer Software Assurance (CSA) in systems like QMS, CTMS, MES or LIMS?
Think of system‑level risk as the risk of the whole application (QMS, TMS, MES, LIMS), and feature‑level risk as the risk of a specific AI function inside it.
QA Validation Expert
System‑level risk (QMS / CTMS / MES / LIMS)
- Holistic view of the entire system: its role in GxP, data integrity, security, and impact on product quality / patient safety.
- Used to decide:
Is this system GxP‑relevant?
Is this system GxP‑relevant?
Example: A QMS used for deviation and CAPA management is high system‑level risk and needs full validation.
Feature‑level risk (the AI bits inside)
Zooms in on individual AI features within that system.
Each AI feature can have a different risk level depending on:
– Decision consequence (how bad if it’s wrong), and
– Model influence (how autonomous / adaptive it is).
Used to decide how deep AI‑specific validation, testing, bias checks, and monitoring must go.
Examples:
– In QMS: AI that suggests deviation severity = high feature‑level risk (affects compliance decisions).
– In LIMS: AI that recommends reports layout = low feature‑level risk (no direct GxP impact).
Why this matters for AI validation / CSA
- Prevents over‑ and under‑testing
High‑risk system doesn’t mean every AI feature is high risk.
Some AI features inside “moderate” systems can still be critical and need extra assurance.
- Aligns with CSA
System‑level risk: determines that the system needs validation.
Feature‑level risk: tells you where to focus the most rigorous testing and documentation.
- Makes your approach defensible
You can show auditors how each AI feature’s consequence + influence drove the depth of validation, instead of treating all AI as equal.
How do you assess AI risk in a way that is audit-defensible in GxP: what do decision consequence and model influence (autonomy + adaptiveness) mean, and how do they combine into a composite risk score?
e-Systems QA Key Expert
Imagine you’re doing a classic pharmaceutical quality risk assessment – the kind everyone recognizes from ICH Q9 – but this time the “system” you’re evaluating happens to be an AI model rather than a filling line or a cleaning process.
That’s essentially how the guide makes AI risk in GxP audit‑defensible: it doesn’t invent a completely new philosophy, it extends the familiar ICH Q9 quality risk management mindset and applies it to AI‑specific questions. You still follow a structured, documented process, you still justify your ratings, and you still connect risk levels to proportional controls.
The twist is that the traditional dimensions of risk are translated into AI language. Instead of just asking “How bad would a failure be?” and “How likely is it to happen?”, you ask:
- What are the consequences if this AI‑supported decision is wrong – could it affect product quality, patient safety, or regulatory compliance?
- How much influence does the model really have over that decision – how autonomous is it, and how much does it change or learn once it’s in use?
As AI adoption continues to evolve across the pharmaceutical industry, many of the principles discussed here are explored in broader industry initiatives and collaborative frameworks.
This discussion highlights key themes from our collaboration with BioPhorum on addressing AI risk management in GxP environments. For
the full guidance underpinning these concepts, including practical frameworks, risk assessment approaches and implementation considerations, explore the BioPhorum publication here:
