My recent live webinar “AI and Process Validation” with our partner BW Design Group sparked some fantastic discussion about the role of artificial intelligence in modern validation practices. From model transparency to job security, we received a broad range of audience questions. If you missed it, the full session is now available to watch on demand.
Here’s a roundup of the top questions asked during the Q&A — and our expert answers.
Q1: Is neural network AI essentially a self-tuning PID control system?
A: While there are conceptual similarities — both learn and respond to data inputs — neural networks and PID control systems are quite different in design and scope. A PID controller operates using fixed logic to continuously adjust a process, whereas neural networks learn from historical data patterns and optimize performance over time. Neural networks can model much more complex, nonlinear relationships, making them more suitable for prediction, classification, and optimization tasks in validation and manufacturing. Since advanced manufacturing processes are more complicated than linear relationships, neural networks have a space within the process validation area to excel.
Q2: What would you say to people who fear that AI will replace them or take away their jobs?
A: AI is not about replacement — it’s about augmentation. In validation, AI tools can take on repetitive data handling or trend analysis tasks, freeing professionals to focus on critical thinking, decision-making, and innovation. The skill set is shifting, not disappearing. The most valuable professionals will be those who know how to work with AI, not against it.
Q3: How much data do you need to train a model, and what can you do if your process is novel and lacks a large data pool?
A: The data requirements depend on the complexity of the model and the variability of the process. For novel or small datasets, strategies like data augmentation, transfer learning, or synthetic data generation can be useful. You can also begin with simpler statistical models and evolve them as more data becomes available. Domain expertise plays a crucial role in ensuring the model is still meaningful, especially with limited data.
Q4: How do you handle model transparency in order to allow regulators to understand the model’s logic?
A: Explainability is essential. We advocate for models that are interpretable and support techniques like SHAP (SHapley Additive exPlanations) values or LIME (Local Interpretable Model-agnostic Explanations) to highlight which inputs influence outcomes. Documentation, audit trails, and validation protocols are critical for satisfying regulatory requirements and demonstrating that AI outputs are logical, consistent, and traceable. The most important aspect of model transparency is understanding how your model makes determinations and avoiding treating an AI like a ‘black box’.
Q5: Some of this regression testing looks very similar to Minitab output from characterizing data and Gage R&R studies; is this the big data equivalent?
A: That’s a great observation — yes, in many ways it is. Traditional tools like Minitab focus on smaller, structured datasets. AI extends those capabilities by handling larger, more complex datasets and uncovering patterns not easily visible through standard statistical methods. It’s not a replacement — it’s a next step in scale and sophistication.
Q6: What is Kneat’s approach to Pharma 4.0™ principles?
A: Kneat embraces Pharma 4.0™ by digitalizing and automating validation processes to align with the industry’s push toward real-time data, interconnected systems, and continuous improvement. Our platform, Kneat Gx, enables smart data capture, traceability, and analytics, which are key components of the Pharma 4.0 ecosystem. We’re also exploring how AI supports Pharma 5.0™ goals — where human-centric design and digital intelligence intersect.
Q7: How does an AI tool get validated to ensure it meets GDP and Data Integrity/GAMP requirements?
A: AI tools follow the same principles of software validation — with added considerations for model accuracy, reproducibility, and explainability. Validation includes model verification, traceable data lineage, and documented testing across scenarios. At Kneat, we integrate these validations into digital workflows to ensure compliance with GDP, GAMP 5®, and data integrity standards.
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