BrazilAgentic AI: Transforming the pharma lifecyle from R&D through to Commercialization
One of the most promising technologies for many industries, Agentic AI heralds the potential to radically transform how pharmaceutical companies run their business. Experts from Cognizant outline how Agentic AI can unlock benefits and efficiencies across the full lifecycle – from Research & Development through to Operations and Commercialization.
Agentic AI refers to artificial intelligence (AI) systems that can independently make decisions and take actions to achieve goals, often without extensive human intervention. Unlike traditional AI, which analyzes data for human-led action, Agentic AI systems are designed to operate autonomously, adapting to changing environments and learning from experience, making them more flexible than traditional AI systems.
Key characteristics of Agentic AI include: autonomy (making decisions without explicit human instructions), goal-oriented behavior (they pursue objectives and adapt their actions accordingly), and adaptable (they learn and adjust their behavior in response to new situations).
Across any part of the lifecycle, drug developers can leverage Agentic AI solution to enhance speed, improve efficiency, boost productivity, and lift the odds for success.
1) Research & Development
Agentic AI can accelerate and optimize various stages of pharmaceutical R&D. In drug discovery, it autonomously analyzes biological data to identify and validate targets. For analytical and formulation development, it designs in silico experiments and explores chemical spaces to optimize drug candidates.
In clinical research, agentic AI aids in preclinical analysis, trial management and safety monitoring. Furthermore, it streamlines the transfer to clinical manufacturing by identifying efficiencies and optimizing timelines. Strategically, it enhances real-world evidence sourcing for approvals and improves the quality and impact assessment of regulatory submissions.
Key benefits
For life sciences companies in particular, agentic AI can offer a number of benefits at the R&D stage, transforming efficiency and increasing the likelihood of drug research translating into clinical and commercial success:
- More efficient drug discovery processes
- Better-informed strategic decisions regarding assets
- Faster progression of drug candidates into pre-clinical and clinical trials
- Higher success rates for initial regulatory submissions
- More effective evaluation of the impact of regulatory changes.
2) Operations
In their manufacturing and laboratory operations, drug manufacturers face mounting complexity, including manual workflows that drain productivity and capacity constraints, and siloed systems and data.
By adopting Agentic AI, life science organizations can transform their operations from the ground up, helping to ensure that vital medicines reach the people who need them, when they need them most. An overview of notable benefits that Agentic AI can deliver across manufacturing and laboratory operations:
Accelerating new product introduction (NPI)
Bringing new therapies into a good manufacturing practice (GMP) production setting is often time-consuming and complex. Agentic AI streamlines this by autonomously configuring shopfloor systems, moving from manual coordination to checklist-driven setup and eventually, one-click deployment. This agility significantly reduces time to production, helping therapies reach patients faster.
Enhancing throughput and capacity
As demand intensifies, Agentic AI delivers role-specific insights that help operators, supervisors and plant managers make faster, better-informed decisions in drug manufacturing. Agentic AI can provide a real-time view of performance, enabling tighter process control, reduced variation and improved resource utilization.
Proactive predictive maintenance
Unexpected equipment failures can derail production. Agentic AI analyzes historical and real-time sensor data to forecast maintenance needs with precision. By identifying issues before they become problems, teams can schedule interventions proactively, reducing downtime, extending equipment life and avoiding costly delays.
Laboratory automation and orchestration
Labs remain a critical bottleneck in the manufacturing chain. Agentic AI can orchestrate high-throughput lab workflows, allocate tasks to instruments efficiently and monitor equipment health in real time. This minimizes human intervention and improves turnaround times while supporting seamless integration with downstream manufacturing.
Smarter quality control
Ensuring consistent product quality is non-negotiable for therapeutics. Agentic AI enhances quality control by detecting micro-deviations across data streams, such as temperature, pressure and visual anomalies, that may go unnoticed by human inspectors. Feeding these insights into process analytical technology (PAT) systems tightens control and reduces batch failures and waste.
Resilient supply chain optimization
Agentic AI brings end-to-end visibility to supply chains, enabling agents to monitor production, inventory and logistics in real time. When risks like shipment delays or inventory gaps arise, agents can autonomously reroute deliveries, adjust schedules and order supplies to keep operations on track.
3) Commercialization
The commercialization stage is a critical stage in the drug product lifecycle and a smooth transition through regulatory approvals to market launch is essential if the therapy is to be successful.
In this stage, Agentic AI systems represent a significant opportunity to overcome key barriers around data insights and human-led decision-making. The technology can enable greater virtual communication between different areas of the drug development and commercialization process, as well as augment decision-making and allowing execution at scale.
When used as part of a digital transformation project to integrate a life sciences company’s many disparate databases and systems, Agentic AI can combine and analyze a wider array of datasets faster than a human operative can on their own.
The more effective use of data can deliver a number of benefits at the commercialization stage, including:
- Smoother regulatory filing and compliance
- Enhanced client relationship management
- More efficient sales and marketing through automation.
Moreover, as Agentic AI can action many of its own recommendations, it can automate repetitive tasks, freeing up human workers to focus on more strategic and creative endeavors.
Applications across filing and market launch
Agentic AI can be harnessed in a number of ways during the commercialization process:
- Personalizing customer relationship management (CRM), harnessing vast datasets to better understand and engage high-potential target customers.
- Developing strategic insights and scenario visualizations pre- and post-launch so teams can explore different scenarios to make more informed decisions about marketing.
- Orchestrating sales and marketing orchestration, tailoring content and messaging to the specific needs of key HCPs, as well as automating many repetitive sales and marketing tasks.
- Compressing timelines for end-to-end commercial planning and execution for faster market launches.
- Improving regulatory and market access intelligence to align with diverse regulatory requirements across different regions.
- Enhancing pricing through better predictive modeling around reimbursement success.
Conclusion
Mounting pressures are pushing life sciences companies to urgently explore advanced technologies like artificial intelligence. Traditional drug development is slow, inefficient and has a 90% failure rate. Poor digital connectivity hinders data analysis for improving R&D and operations. And many companies struggle with siloed legacy systems.
Effectively capturing and utilizing data across the entire pharmaceutical process – from discovery to commercialization – is crucial for faster, more efficient medicine delivery. This is where Agentic AI can play a gamechanging role.
