In life sciences, patient safety, data integrity, and regulatory readiness aren’t optional. They’re non-negotiable. As organizations embrace AI and digital transformation, it’s essential to ground innovation in a framework that respects GMP principles and adheres to required regulations and best practices, including 21 CFR Part 11, Annex 11, Annex 22, GAMP5, GAMP Artificial Intelligence Guide and ALCOA+.
But here’s the challenge: AI is new. It’s not a traditional software platform, and unlike validated spreadsheets or legacy (Laboratory Information Management Systems) LIMS, it often comes with unknowns. That’s why AI literacy (understanding what AI is, what it does, and how it impacts GxP systems) is emerging as a mission-critical competency for IT, Business and Quality leaders.
At ERA Sciences, we believe AI maturity starts with organizational alignment and this includes a harmonized and socialized understanding of key AI terms. Whether you’re a specialty pharma deploying your first risk-based system, a clinical-stage company transitioning to manufacturing, or implementing serialization across Europe, here are five best practices to elevate your AI literacy without compromising GxP.
Misunderstandings about terms like “model,” “training data, “static and dynamic models” or “algorithmic output” don’t just create confusion. They can delay validation timelines and kill momentum in digital initiatives. One department’s “AI solution” may be another’s “automated script.”
Establish a cross-functional AI glossary that is incorporated into:
Standardized AI language will set the foundation for consistent understanding, whether you’re presenting to auditors, onboarding a new engineer, or mapping AI usage in GxP systems.
AI must be purpose-built. Without clear processes for identifying where, why, and how AI is used, projects may drift, validation will become harder and unknown risks may become a downstream issue lacking appropriate mitigation, and ultimately end users may lose trust.
In our previous article, “AI Meets GxP: Model Cards for Trust, Transparency and Compliance”, we introduced the idea of model cards: structured, GAMP5-aligned documentation of AI purpose, inputs, outputs, and limitations. Model cards can be powerful tools to drive transparency, especially during cross-functional reviews.
Not every employee needs to understand the inner workings of a random forest model, but every employee needs to understand how AI affects their role. Blanket training wastes time and can lead to disengagement.
Instead, segment training into levels:
AI implementation isn’t a one-and-done event. It evolves. Models may drift. Teams experiment. If there’s no way to flag emerging concerns, small issues become major compliance blockers.
The earlier you build real-time communication pathways, the more proactive you can be when addressing risk and ensuring ALCOA+ principles are upheld.
AI’s black-box reputation can make people nervous, especially in highly regulated environments. The worst outcome? Teams pretending to understand something they don’t, leading to gaps in validation, oversight, or procedural coverage.
Encouraging curiosity doesn’t weaken your compliance posture, it strengthens it by uncovering risks early and fostering psychological safety.
In a GxP environment, AI literacy isn’t just about technical understanding. It’s a shared language of safety, consistency, and audit readiness. Whether you’re adopting AI for predictive maintenance, process automation, or clinical forecasting, you need a structured foundation rooted in best practices and fit-for-purpose tools.
Phanero was built with this vision in mind. As a modern, AI-enabled GxP computerized systems inventory solution, it simplifies how life sciences companies track systems, manage metadata, and capture audit trail reviews; all while supporting your digital journey from Phase 3 to commercial scale.
Ready to elevate your AI maturity?
Sign up for a Phanero demo today and see how digital transformation can be audit-ready from day one.