5 Top Tips for Practical Strategies for Adopting AI in the Pharma Industry
Article By Michelle Yeomans, EPiC Operations Manager
Artificial Intelligence (AI) is rapidly reshaping the pharmaceutical landscape, from predictive maintenance and documentation streamlining to risk management and regulatory intelligence. Before embedding AI in daily processes, the regulatory expectations, risks, and implementation pathways need to be understood.
Drawing on regulatory guidance and MHRA and EMA insights, along with practical examples already observed across the industry, this EPiC Top Tips article outlines the essential steps organisations should take to adopt AI safely, ethically, and effectively.
Best practices to adopt AI safely, ethically, and effectively.
1. Strengthen Foundational Knowledge and Engage with AI
A successful AI strategy begins with understanding the fundamentals. Knowledge empowers teams to challenge outputs, recognise risks, and make informed decisions.
- Review available regulatory guidance and reflection papers published by the EU, MHRA and US FDA to improve awareness of common terminology, operating models, and the associated risks.
- Join AI focused discussions within the Pharma community on platforms such as LinkedIn and learn from relatable examples from other healthcare sectors including from medical devices and other GXP areas.
- Encourage teams to experiment with AI tools to build familiarity, practising prompting skills enables better sense checking.
Understanding terminology and the challenges and risks posed by static vs dynamic models will help you navigate the regulatory and operational environment with greater awareness and improved confidence.
2. Build Internal AI Governance from the Start
Ethical AI adoption cannot succeed without a structured governance framework designed to protect patient safety, data integrity, and intellectual property.
- Review and update existing data integrity and data governance procedures to include AI-specific considerations.
- Create an inventory of all AI systems, identifying their models, ownership, intended use, risk classifications, and controls.
- Ensure safeguards for sensitive or confidential information, for example only use a secure, managed organisational platform for inputting IP and never input sensitive information into an open chatbot browser as it will hoard the information.
Robust governance ensures alignment with EU GMP Chapter 4, Annex 11, and the new Annex 22 for AI, and supports transparent, explainable, and ethical system use.
The official link to the stakeholder consultation for future changes to Chapter 4, Annex 11 and the new Annex 22 is Stakeholders’ Consultation on EudraLex Volume 4 – Good Manufacturing Practice Guidelines: Chapter 4, Annex 11 and New Annex 22 – Public Health
3. Clearly Define AI Intended Use and Assess System Impact
AI systems must not be adopted without understanding exactly what they will do, how they will do it, and what risks they introduce.
- Define the system’s intended use, including whether it is static or dynamic, and whether it impacts patient safety, product quality, or data integrity.
- Use EU GMP Annex 15, Annex 11, GAMP, and ICH Q8 to perform a system impact assessment to understand the risks associated with the application and the impact on critical processes or quality attributes to inform the User Requirement Specification (URS) and validation requirements.
- Ensure models, especially those supporting decision making, are validated and maintained in a validated state throughout their lifecycle.
The new EU GMP Annex 22 reinforces that dynamic, adaptive, or generative models should not be used in critical GMP applications, highlighting the importance of understanding different types of AI models.
4. Appoint Skilled AI Subject Matter Experts (SMEs)
Human oversight or Human in the Loop (HITL) remains essential to monitor AI performance, interpret outputs, and challenge potential bias.
Your SME should:
- Understand the process, data flows, interfaces, and risk areas of the AI application.
- Be able to explain how the algorithm works and why outputs were generated.
- Support operator training, change evaluation, periodic review, and ongoing system performance monitoring.
In inspections, regulators expect SMEs to demonstrate system knowledge confidently, invest in developing their expertise early.
5. Apply Critical Thinking and Safeguards to Challenge AI Outputs
AI outputs must never be accepted at face value, hallucinations and biases are well documented risks.
To strengthen assurance:
- Ask key questions: What data trained the model? What biases might exist? How confident is the output? Can the system explain its reasoning?
- Establish peer review processes before acting on AI suggestions.
- Train teams to maintain critical thinking skills avoid over‑reliance on automated decisions.
AI cannot replace professional judgement. Humans should record why they agree or disagree with AI outputs so that the frequency of human overrides can be measured. This will provide monitoring data to help the SME challenge AI system performance.
Final Thoughts
AI offers enormous potential for efficiency, insight, and innovation across the pharmaceutical lifecycle. However, safe adoption requires a structured approach, grounded in regulatory expectations, risk management, governance, and strong human oversight.
Key takeaway: AI should be treated like any other GXP relevant system i.e. planned, validated, governed, monitored, and continually challenged.
How EPiC Can Support You
EPiC’s team of former MHRA Inspectors and industry experts can help your AI adoption through:
- AI readiness assessments
- Governance and policy development
- Training for SMEs and operational teams
- Validation and GAMPaligned support
- Data integrity and risk assessment consultancy
If you’re considering implementing AI or reviewing existing tools, speak to us to ensure compliance, patient safety, and business continuity remain central to your strategy.