Effective AI Deployment in Drug Manufacturing Lessons from a cross-functional roundtable at PDA Week 2025

The pharmaceutical industry stands at a pivotal moment as advanced digital technologies, particularly artificial intelligence (AI), are increasingly integrated into manufacturing environments.

During PDA Week 2025, a dedicated roundtable titled “Effective AI Deployment in Drug Manufacturing” gathered professionals from diverse operational areas to explore the implications of AI implementation in drug production.

A Collaborative Scenario-Based Approach

Facilitated by Peter Makowenskyj, Ryan Murray and Toni Manzano, the session invited participants to engage in a collaborative exercise built around a practical case study: deploying a new, fully digitalized, cloud-connected, AI-managed advanced filling line. Teams assumed functional roles representing engineering and IT, production, quality control and regulatory and compliance, and they were tasked with evaluating the potential implications, benefits and risks of this digital transformation.

The session format included a role-play exercise followed by a structured strengths, weakness, opportunities and threat (SWOT) analysis, culminating in open group discussions. This structure highlighted how AI integration transcends technical deployment, touching every operational and compliance area within a pharmaceutical manufacturing organization.

Cross-Functional Insights: Strengths and Challenges

The roundtable teams identified several compelling strengths associated with AI-managed manufacturing systems: improved efficiency and consistency, predictive analytics for proactive risk management, enhanced technology transfer and the potential for real-time product release. Integrating digital twin technology and centralized cloud-based control were also recognized as major enablers of operational scalability and flexibility.

At the same time, participants acknowledged significant challenges. Foremost among these was the current lack of clear validation methodologies for AI systems, particularly those capable of learning and evolving over time. The complexity of multi-agent system architectures, high infrastructure costs, GMP-compliant data management and the need for specialized workforce training were also noted as key concerns.

Opportunities for Industry Progress

The session provided opportunities for the pharmaceutical sector, including replicable manufacturing systems across global sites, streamlined standard operations and procedures (SOPs), and enhanced environmental safety by reducing manual interventions. Teams emphasized the value of real-time quality monitoring, predictive risk assessment and the collaborative potential between manufacturers and regulators to develop AI-specific guidelines.

Notably, digital twin applications were viewed not only as tools for process optimization but also as mechanisms to redefine how validation and continuous verification might be approached in AI-enabled production environments.

Risks and Industry-Wide Themes

Alongside opportunities, participants identified critical risks, including cybersecurity threats, operational disruptions from cloud outages, regulatory inconsistencies between jurisdictions and the risk of over-reliance on AI-driven decisions without adequate human oversight. A consistent theme emerged: the pharmaceutical industry must rethink its approach to validation, workforce training and regulatory engagement to harness AI’s potential responsibly.

Consolidated Cross-Functional SWOT Analysis

Drawing from the conclusions of all four roundtable teams, the following consolidated SWOT analysis (see Table 1) summarizes both the shared themes and the unique insights contributed by each functional area.

Strengths

Common themes:

• Improved efficiency and faster time to production
• Continuous optimization and reduced human error
• Faster turnover and process analysis
• Cloud-enabled scale-up capacity
• Increased data availability for enhanced process understanding
• Use of predictive analytics to reduce waste, defects, downtime and non-compliance

Unique insights:

• Remote access to data from any location
• Staying current with technological advancements
• Deployment of multi-agent systems for expanded functionality

Opportunities

Common themes:

• Stable, reliable productivity
• Real-time product release potential
• Implementation of digital twin technology
• Support for scale-up and distributed manufacturing
• Standardized SOPs and operational practices
• Use of AI to optimize manufacturing conditions, batch records and processes
• Real-time quality monitoring
• Infrastructure upgrades
• Improved cross-functional collaboration

Unique insights:

• Preventative, predictive risk analysis through AI
• Easier integration with existing systems
• Simplified future approvals once platform tools are established
• Opportunity for early engagement with regulators to shape new technology guidelines

Weaknesses

Common themes:

• Uncertainty in AI validation; the need for robust, lifecycle-focused validation frameworks
• Significant training demands; workforce digital readiness is a concern
• Defining a clear human-in-the-loop strategy
• Cybersecurity risks and data integrity safeguards
• Challenges in ensuring data traceability and regulatory compliance
• Complexity of multi-agent system architectures
• Intellectual property and algorithm transparency concerns with regulators
• Difficulty in sharing AI-generated data with inspectors

Unique insights:

• Reliance on high-quality, accurate data
• GMP-compliant data storage challenges
• High start-up and infrastructure costs
• The potential need for a dedicated AI quality oversight function
• Managing boundary conditions and unforeseen system behaviors

Threats

Common themes:

• Global regulatory convergence challenges
• Persistent IT security and cybersecurity risks
• Resource limitations for AI management, security and validation
• Workforce training gaps and potential displacement concerns
• Slow regulatory evolution hindering AI adoption
• Cloud-based system continuity risks

Unique insights:

• Risk of unauthorized data use if third-party access is poorly managed
• Gaps in upholding ALCOA++ principles in AI environments
• The danger of over-relying on or mistrusting AI decision-making

Shared Conclusions and Industry Recommendations

Across all teams, recurring themes emerged: AI validation remains the greatest challenge, along with shared concerns over cybersecurity, regulatory alignment and workforce readiness. Participants agreed that while AI offers transformative potential, its responsible integration requires deliberate strategy, caution and continuous collaboration between manufacturers, regulators and technology providers.

Key recommendations included:

• Developing AI-specific validation frameworks addressing model drift, lifecycle management and decision transparency
• Building cross-functional digital training programs to foster AI readiness across technical, quality and regulatory functions
• Strengthening infrastructure resilience through cybersecurity protections, redundant systems and business continuity planning
• Proactively engaging regulators to co-develop pilot programs and future frameworks
• Maintaining multi-disciplinary forums to anticipate and address emerging AI challenges

A Path forward

The roundtable reaffirmed AI’s promise for pharmaceutical manufacturing while underscoring the importance of a cross-functional, collaborative approach. By addressing challenges in validation, data governance, workforce development and regulatory expectations, the industry can advance toward a future where AI-enabled operations strengthen both compliance and competitiveness.

Closing Reflections

This session demonstrated the value of collaborative dialogue in navigating the evolving landscape of AI in drug manufacturing. As AI capabilities progress, so must industry approaches to validation, regulatory engagement and operational integration. PDA’s continued leadership in convening expert discussions ensures that the pharmaceutical sector is well-positioned to responsibly adopt and manage AI-driven technologies for enhanced manufacturing efficiency, product quality and patient safety.

As PDA continues to champion forums for practical discussion and knowledge exchange, sessions like this offer invaluable insights into the road ahead for AI-enabled drug manufacturing.