A Design Qualification Blueprint for AI in a Regulated Industry
In the rapidly evolving landscape of pharmaceutical manufacturing, the integration of Artificial Intelligence (AI) offers transformative potential—but only if it can be deployed with speed, trust, and regulatory confidence. Last week, the Oxenham Group hosted industry expert Jason Dow for a 1-hour session on how to apply Design Qualification (DQ) principles to AI models. This webinar introduces a Design Qualification (DQ) Blueprint tailored for AI models in Good Manufacturing Practice (GMP) environments, enabling organizations to innovate faster while maintaining quality and compliance.
Key Takeaways
Speed to Innovation
Design Qualification (DQ) acts as a stage-gate between AI model development and process validation, allowing teams to accelerate deployment without compromising on quality. By qualifying the AI model itself—independent of the full process—organizations can reduce time-to-value and iterate faster on AI-driven solutions.
Regulatory Confidence
DQ provides a GMP-compliant record that verifies the AI model was developed using robust engineering standards across five critical lifecycle stages: problem definition, data preparation, design, training, and evaluation. This approach aligns with existing regulatory expectations and builds trust with agencies by demonstrating a structured, auditable path to AI readiness.
Shift in Mindset
Traditional validation focuses on the process. DQ shifts the focus to the AI model as a qualified technology component, enabling modular validation and reuse across applications. This separation allows for parallel development of AI models and the systems that use them, streamlining innovation pipelines.
Governance and Assurance
DQ is embedded within a broader AI governance framework that includes ethical standards, data integrity, model monitoring, and change control. It supports cross-functional alignment between engineering, quality, and compliance teams, ensuring AI is deployed responsibly and effectively.
Strategic Value
By adopting a DQ approach, organizations can:
Accelerate AI adoption in GMP-regulated environments.
Reduce validation bottlenecks and increase agility.
Enhance transparency and trust with regulators and stakeholders.
Enable scalable, repeatable AI innovation across the enterprise.
Watch the full webinar here and reach out to Bill McGuckin at bmcguckin@oxenhamgroup.com to learn more about how the Oxenham Group is helping our clients navigate this ever-changing landscape.