Join Yan Kugel and Geri Studebaker, Chief Commercial Officer at Aizon (LinkedIn), as they dive straight into the real-world challenges and next-step solutions for AI-driven pharmaceutical manufacturing. Geri sheds light on the five most common barriers to AI readiness: paper records, data silos, lack of contextualization, regulatory hurdles, and talent gaps.
Why AI Readiness Is Suddenly a Boardroom Topic
AI in pharma manufacturing has moved from hype to a daily need. Plants want better first-time quality, faster release, and fewer deviations. Many are stuck because their data and systems are not ready. In an insightful interview, Geri shared a grounded path that matches how real plants work. His advice is to start with an honest look at your current data state, choose a first step that delivers value, and keep GMP expectations front and center. This approach avoids the trap of pilot projects that never scale. It builds trust with QA and regulators while delivering practical wins to operations. If you lead manufacturing, quality, or digital programs, this is the kind of plan that gets traction without disrupting production.
What Keeps Teams from Getting AI to Work
Five barriers show up again and again. The first is paper-first operations. Batch records, logbooks, and SOPs on paper slow reviews and make investigations painful. Small errors creep in. Trends hide in binders. The second is fragmented digital systems. Even after digitization, data lives in MES, LIMS, QMS, historians, and ERP as disconnected islands. Pulling a full view of a batch means manual exports and late nights with spreadsheets. The third barrier is missing context. Parameters need to be tied to product, recipe, step, equipment, and process state. Without that map, AI sees numbers but not meaning. The fourth is regulatory pressure. Insight alone is not enough. You need traceability, audit trails, validation, and inspection-ready records. If a model can’t be explained, it won’t be accepted. The fifth is a talent gap. You need people who understand both data science and pharma processes. Teams that lean only one way either build brittle models or ignore compliance. The fix is a plan that meets the site where it stands today, sets a clean scope, and builds toward AI with context and governance baked in.
How to Use AI for Faster Root Cause and Better Predictions
Batch variability and yield loss are the daily pain points. AI can help when it sits on solid data and knows the process. Start by digitizing the essentials if you are still on paper. If you already have digital systems, pull data together and layer process context over it. Map critical parameters to each step and equipment state. Tie them to known quality outcomes. Then focus on real-time signals. Insights that show up days after a batch won’t save product already out of spec. Real-time data lets the team spot drifts early and correct before a deviation lands. Design models to be process-aware. If a model doesn’t know where it is in the recipe, it will miss what matters. Feed insights into operator screens, and make alerts actionable with clear thresholds and guidance. Share the same view with QA so they can track the evidence line from parameter change to impact on quality. Done right, this closes the loop. Operators catch issues, engineers see patterns, and QA can support decisions with clean data. Deviations fall. First-time quality climbs. Investigations take hours, not weeks.
What Inspection-Ready AI Looks Like
AI in GxP must stand up in audits. That means models are managed like controlled systems. Keep version control for every model. Track changes, approvals, reasons, and results. Maintain full lineage back to data sources so you can show what data the model saw and why it made a call. Log each prediction and automated action with the timestamp, model version, inputs, confidence score, and outcome. Use risk-based validation that matches impact. Define performance thresholds, stress test over expected operating ranges, and re-validate after retraining or data shifts. Assign owners for each stage—development, testing, release, monitoring, and retirement—and document the lifecycle. Build monitoring that flags drift and triggers review before performance slips. When this discipline is in place, QA has confidence, and inspectors can follow the trail without surprises. It keeps the system honest and avoids painful rework after an audit.
Why Batch Records Are the Best Place to Start
Going digital is not only about systems. It is about changing habits. People trust what they can hold and what they have used for years. The way through is to pick the record that matters most: the batch record. It drives product release, connects process steps to quality outcomes, and anchors deviation investigations. When you digitize batch records, benefits show up fast. Reviews speed up. Comparisons across batches become simple. Trends become clear without manual hunting. Engineering can filter by parameter, equipment, shift, or operator and spot weak links. This opens the door to real-time monitoring where the current batch’s key indicators are visible and alerts are timely. After the batch record, move logbooks to electronic form so equipment events, cleaning, and calibrations tie directly to outcomes. Then digitize SOPs to align procedures with steps and keep training records tidy. This sequence builds a digital thread that supports AI and avoids overwhelming teams with too many changes at once.
A Practical Path: From Batch Monitoring to Real-Time Release
Think of AI adoption as a series of steps that deliver value on the way to a bigger goal. Batch monitoring comes first. Track parameters with context across batches. See trends. Spot outliers. Identify drift. This often delivers clear process improvements and sharper decisions. Next, shift to data-driven batch release. Use monitored data with defined rules to support release decisions and cut manual review time. Then aim for real-time release where validated indicators and models support release as the batch completes. That shortens lead time and reduces inventory holding. The long-term target is continuous manufacturing with connected processes, steady control, and AI-supported adjustments across states and shifts. Each step increases trust in digital evidence. Teams learn how to work with models. QA gains a firm grasp of digital records. IT and OT build smoother data flows. You do not need to jump all the way at once. Make each step pay for the next.
How Manufacturing 5.0 Shows Up in the Next Two to Three Years
Roadmaps no longer stretch a decade. Change is happening in two to three years. The focus now is making GxP-grade AI part of daily work, not a lab demo. Expect stronger model lifecycle discipline with clear ownership and change control. Real-time data pipelines will pull signals from equipment, MES, and labs without manual exports. Process-aware data fabrics will link product, recipe, step, and equipment context so models “know” where they are. Insights will be tailored to operators and engineers, not just complex charts. QA will help set thresholds and validation plans for each model so trust grows from the start. Early wins will come from faster investigations, shorter release time, and better first-pass yield on priority products. Aizon’s work mirrors this push. Meet clients where they are, build batch monitoring, strengthen release decisions, and move toward real-time release and continuous manufacturing while keeping GMP expectations tight. This is how sites make steady progress without risking compliance.
Why Mindset Matters More Than Technology
Many teams stall because they treat AI as endless pilots. Shift to product thinking. Pick a clear user—an operator, process engineer, or QA reviewer—and design for their daily needs. Choose a narrow, painful use case. Predict blend uniformity issues. Catch sterilization cycle drifts. Flag granulation moisture trends before they fail a test. Get a working solution into production quickly. Monitor it. Gather feedback from the floor. Retrain when performance slides. Write SOPs, track versions, and keep validation tidy from day one. This builds momentum and proves value. Leaders see results they can measure. Auditors see order. Teams trust the system because it helps, not hinders, their work.
Next Steps You Can Take Today
Start by assessing your data readiness with a simple checklist. Are batch records still on paper? Are logbooks and SOPs digital? Do your systems connect or are you exporting to spreadsheets? If batch records are on paper, digitize them now for a pilot product. Pick one predictive use case that will show visible value in the next quarter. Define model governance—owners, change control, validation steps, and monitoring. Build operator screens that show what to do when an alert fires. These practical steps set the stage for scaling AI while keeping compliance and trust intact.
Where Will You Begin?
Getting AI to work is less about fancy models and more about clear focus, clean data, the right people in the room, and strong guardrails. Start with one or two high‑impact use cases and ship small pilots to prove value fast. Make data quality a habit with clear ownership and documented sources. Build cross‑functional squads that pair domain experts with data and IT. Bake privacy, security, and GxP into the design from day one. Support people through change with training, simple guidance, and incentives that nudge real adoption. Measure impact with a few concrete KPIs and share wins and lessons openly. When teams work this way, AI stops feeling stuck and starts delivering steady, trustworthy results.
Qualistery provides life‑science professionals with concise, practical GxP and cGMP learning through expert‑led webinars and tailored training, helping teams make safer, more compliant decisions. We deliver these sessions in partnership with trusted solution providers, combining real‑world insights and actionable guidance with targeted outreach to the right decision‑makers.
