2025 Global Quality Outlook

The life sciences industry is undergoing a transformative shift toward smarter manufacturing, redefining the role of QA professionals. No longer focused solely on identifying problems after they arise, QA teams are stepping into proactive, strategic roles.

AI excels at root cause analysis, quickly identifying patterns in complaints and quality deviations that humans might miss. This capability accelerates the process, enabling teams to address issues at their source and prevent future problems. This is leading QA professionals to adopt AI for production optimization and outcome prediction.

By embracing these technologies, QA professionals are becoming key drivers of innovation, ensuring that pharmaceutical manufacturing is efficient, compliant, and resilient.

We recommend:

• Adopt Explainable AI (XAI) to ensure transparency and build trust, allowing QA professionals to confidently use AI for critical tasks.
• Centralize data storage and implement data cleaning tools to eliminate errors and ensure AI systems are trained on high-quality datasets.
• Develop validation protocols and conduct audits to identify and address biases, ensuring ethical transparency throughout the AI lifecycle.
• Launch training programs and pilot projects to demonstrate AI’s benefits, build confidence, and increase team buy-in.
• Redefine QA’s role as a driver of quality improvement and innovation, encouraging cross-department collaboration.

AI is enabling QA professionals to make faster, smarter decisions with data. By leveraging data-driven insights, QA teams can monitor product quality in real time, identify issues early, and keep production on track.

Additionally, AI helps to scan regulatory documents, internal procedures, and scientific literature to ensure compliance, flag potential risks, and provide real-time recommendations during audits or inspections.

As these tools evolve, they will make it easier to analyze data in real time, spot inefficiencies, and suggest improvements—ultimately reshaping QA to be more agile and future-ready.

We recommend:

• Adopt a phased approach to AI integration, starting with pilot projects, and gradually modernize legacy systems to ensure interoperability.
• Implement a centralized data hub (eQMS) to consolidate data, break down silos, and create a single source of truth.
• Leverage privacy-preserving techniques like federated learning to train AI models without sharing sensitive datasets.
• Implement robust data governance frameworks to standardize data practices and ensure regulatory compliance.
• Invest in explainable AI (XAI) to create transparent, auditable models and build trust in AI decision-making.
• Promote data accessibility by standardizing definitions, integrating semantic layers, and creating searchable metadata repositories to foster a data-driven culture and collaboration.

Traditionally, QA has relied on manual methods like Failure Mode and Effects Analysis (FMEA) and risk matrices, which can be slow and prone to errors. Now, predictive analytics can analyze large datasets in real time, identifying potential risks before they escalate and streamlining compliance processes.

By integrating AI into existing QA frameworks, companies can monitor risks continuously, prevent issues before they arise, and maintain a comprehensive view of operations. While AI enhances efficiency, QA professionals will remain essential in guiding these systems, interpreting results, and ensuring compliance with ethical and regulatory standards.

We recommend:

• Train AI models with diverse, representative datasets to minimize bias, and improve fairness and accuracy of AI algorithms.
• Use automated tools to clean and validate datasets, and conduct routine audits to maintain high-quality data standards.
• Implement Explainable AI (XAI) tools to provide transparency by breaking down complex algorithms, allowing QA professionals to interpret predictions and make informed decisions.
• Develop modular AI solutions that integrate smoothly into existing QA workflows, minimizing disruption while ensuring regulatory alignment.
• Foster adaptive learning through targeted training and collaboration among QA, IT, and regulatory teams.

Currently, AI regulations are mostly industry-specific, addressing concerns like data privacy, algorithmic transparency, and patient safety. Regulations such as the EU Medical Device Regulations (MDR) and the US FDA’s guidelines are vital, but they often evolve slowly, struggling to keep up with the rapid pace of AI advancements.

Looking ahead, the regulatory environment is shifting towards more holistic frameworks. In the U.S., the FDA is refining its AI and machine learning (ML) regulations, while the EU’s AI Act, which came into effect in 2024, provides a comprehensive set of rules for AI deployment across industries. This global trend signals a move toward flexible, innovation-friendly regulations that address the broader implications of AI on safety and ethics.

We recommend:

• Manufacturers must create global policies for ethical operations throughout the development and production of AI systems, ensuring all departments, production lines, suppliers, and distributors adhere to these standards.
• Invest in infrastructure to adapt to a constantly changing market, including personnel, equipment, tools, and training.
• Train regulatory and quality personnel to review AI documentation, understand its mechanisms, and advise on the best course of action.
• Hire AI specialists to mentor existing teams through the new AI paradigm.

Traditional QA processes often operate in silos, relying on reactive measures and manual workflows, which can slow things down and heighten compliance risks. AI presents a transformative opportunity to reshape this dynamic.

By integrating AI tools, teams can collaborate more effectively, anticipate and prevent quality issues, and make data-driven decisions that lead to improved outcomes. With AI handling routine tasks, quality professionals have more time to build relationships, engage in meaningful conversations, think creatively, and enhance soft skills.

We recommend:

• Overcome resistance to AI by implementing targeted training programs, workshops, and success stories to demystify AI’s functionality and highlight its benefits.
• Engage employees early in the AI implementation process to promote ownership and collaboration.
  Address poor data integration by investing in AI tools with strong API capabilities to connect existing systems.
• Implement data cleaning and standardized protocols to enhance integration and provide QA teams with a unified view of quality metrics.
• Overcome budget limitations by starting with pilot projects that demonstrate AI’s ROI.