David Staunton, industry transformation leader at Cognizant Life Science Manufacturing shares how life sciences companies are under immense pressure to deliver new treatments to a global population faster.
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Surging demand for therapies in areas like cancer, weight loss, and immune disorders is placing enormous pressure on development and manufacturing facilities to be faster and more agile.
Laboratory operations are a critical bottleneck. Many processes in the lab remain manual and siloed, hindering throughput. Common issues like equipment downtime, poor scheduling, and underutilised resources create significant challenges.
Manufacturing teams face similar issues. Manufacturing capacity for in-demand medicines is frequently under pressure, particularly given the increased market for innovative therapies. Life science companies must meet this increased demand swiftly, efficiently and with their proven consistent quality.
Optimising these operations hinges on accessible manufacturing and lab data. However, valuable information often remains trapped in fragmented or legacy systems, hindering timely insights and scalable workflow optimisations. Consequently, life sciences companies are turning to Artificial Intelligence (AI). While traditional AI can analyse data and recommend improvements, human operators are still largely responsible for decision making and workflow implementation.
In this article, David Staunton, head of transformation at Cognizant Life Sciences Manufacturing Group will explore the issues facing companies in harnessing their manufacturing and lab data to break down production line efficiency barriers. He will explain how the next step in AI technology, Agentic AI, could hold the key to more effective pharmaceutical manufacturing.
Challenges across the life sciences manufacturing and lab operations lifecycle
Life sciences enterprises are navigating multiple challenges when seeking to deliver medicines as efficiently as possible. Key obstacles include:
- Accelerated drug launches: Algorithmic AI is boosting successful new drug development, yet fast-tracking these to patients strains New Product Introduction (NPI) processes and existing GMP manufacturing and supply capacity.
- Growing manufacturing complexity: Operations are more intricate due to diverse market regulations and complex therapies. Human oversight struggles to optimise production for efficiency.
- Demand surges, capacity constraints: High demand for new drugs in areas like cancer, weight loss, and immune diseases means companies must boost output. Increasing production from current facilities is a major hurdle; failure risks critical shortages.
- Inefficient lab operations: Manual, human-intensive lab tasks are time-consuming and limit GMP manufacturing capacity.
- Geopolitical supply chain risks: A volatile global landscape heightens supply chain disruptions, potentially delaying vital raw materials and finished drug product.
Customer expectations, escalating costs, and the demand for real-time agility mean that traditional workflows, static automation and standard generative AI tools are no longer sufficient.
Understanding Agentic AI?
Agentic AI is a next-generation approach to AI that combines data insights with autonomous or semi-autonomous execution. Agentic AI uses a suite of specialised agents that interpret data, make decisions and act on them automatically, without waiting for human intervention. If GenAI was like having an intern permanently available at your desk… Agentic AI is like having Einstein at your desk!
These AI agents understand goals, interpret context and execute specific tasks across complex digital systems. This ability to analyse, decide and act in real time makes Agentic AI uniquely suited to solving some of the most pressing operational challenges in labs and manufacturing.
Multiple applications
Agentic AI has broad applications across the drug development and manufacturing lifecycle. For laboratory operations, it reduces batch release times by enabling predictive maintenance by monitoring data to anticipate equipment failures, automatically scheduling interventions, and cutting downtime and costs. It also facilitates intelligent scheduling, dynamically assigning tasks to optimise throughput and reduce idle time, while resource optimisation ensures efficient use of equipment and personnel, boosting lab productivity.
In GMP manufacturing, Agentic AI accelerates processes. For new product introduction (NPI), it analyses development data to streamline transfers, ensuring faster, more consistent therapy launches. Capacity optimisation benefits from agents' system-wide visibility and real-time data, allowing them to autonomously reallocate resources or trigger line changes to reduce variability and improve throughput. Finally, Agentic AI strengthens supply chain resilience; agents continuously monitor logistics, inventory, and schedules, adjusting orders, rerouting deliveries, or updating production plans to prevent disruptions if shortfalls or delays are detected.
Considerations when implementing Agentic AI
While the potential of Agentic AI is significant, its implementation in life sciences must account for several technical and ethical considerations:
- Data integrity: Agentic AI requires structured and reliable data. Organizations may need to invest in data preparation, standardization and governance before scaling Agentic AI. However, on the plus side, Agentic AI can help improve your data structures and reliability.
- Interoperability: Agents must be able to work across software and hardware systems. Modular architectures and application programming interfaces (APIs) are key to successful integration.
- Bias, model oversight and model performance: AI systems trained on biased or incomplete data can produce flawed outputs. Human oversight and diverse training data are essential to build trustworthy models. Agentic AI systems are now architected to transparently measure their own performance.
- Security and privacy: AI systems must comply with strict security and data protection regulations, such as the General Data Protection Regulation (GDPR) and the EU Artificial Intelligence Act. Strong encryption and role-based access control are essential.
- Accountability and transparency: When agents take autonomous action, companies must ensure those actions are auditable and traceable. Transparent governance and decision-logging are critical in GMP-regulated environments.
To successfully navigate these requirements, life sciences companies should consider working with digital transformation partners who specialise in Agentic AI and regulated environments. These partners can provide the technical expertise and governance frameworks needed to design, deploy and scale Agentic AI responsibly.
Agentic AI and the future for life science manufacturing
The complexity of modern life sciences production is outpacing traditional automation. Agentic AI offers a way to rethink workflows, enabling systems to self-correct, self-optimise, and scale intelligently across the product lifecycle.
As Agentic AI evolves, its integration with robotics, cloud infrastructure, and real-time analytics will create a more personalised, adaptive, and efficient production environment. People will remain central, however; Agentic AI is designed to support human decision-makers, freeing them for higher-value problem-solving and innovation, not replacing them.
To unlock this potential, companies must build a strong digital foundation, identify high-value use cases, and embed responsible AI practices from day one. Agentic AI is more than an upgrade; it's a foundational change in how life sciences companies approach speed, quality, and scalability in the era of intelligent automation.