In our latest webinar, Aizon’s Toni Manzano (Co-founder & Chief Science Officer) and Kevin Baughman (VP of AI Consulting Services) make the case that the biggest barrier to AI adoption in pharma quality isn't the technology, it's access. Quality SMEs know exactly what they want to analyze. What slows them down is getting a batch comparison that takes an extended period of time to come back through IT.
The Dark Data Problem
Live polling confirmed what many quality professionals already know: most teams estimate that between 25% and 75% of their manufacturing data goes completely unused. Industry data puts it even higher: up to 80% in pharma manufacturing, much of it because it simply isn't accessible.
The root cause is a structural one. The default operating model in pharma is user-centric: IT is responsible for gathering the data and building every tool that consumes it (dashboards, reports, exports). That makes IT the bottleneck. The alternative is data-centric IT, where the data is always clean and ready, and purpose-built platforms handle consumption.
Three Things SMEs Can Build Today, No IT Required
Kevin walked through three live case studies in Aizon Agentic Studio, each built in natural language, no coding required:
- Batch comparison for CAPA: A cockpit that pulls historian, LIMS, and batch record data on demand comparing a suspect lot against reference lots side by side.
- SPC control charts: A real-time SPC application applying deterministic statistical rules across CPPs, with pass/fail logic surfaced to QA, all from a natural language prompt.
- Environmental monitoring: A deviation-reporting cockpit that classifies and aggregates environmental events by monitored zone.
From SME-Built to Validated
Getting to GxP involves four stages: the SME builds the tool, the team fine-tunes it in session, the documentation the agent generates feeds the validation process, and the validated app operates inside the platform with 21 CFR Part 11 controls and full audit trail. The webinar made the AI’s involvement role crystal clear: The agent is a developer, not a decision-maker.
The result is greater speed, autonomy, and insights to empower SMEs.
Watch the full webinar here:
This is the third in our webinar series on Agentic AI in pharma manufacturing. Catch up on the first two: Accelerating Batch Release with Agentic AI and APQR on Demand.
