In the previous blog in this series, we looked into the manufacturing and laboratory operations challenges facing pharmaceutical companies that are being exacerbated by data silos. We explored where Agentic AI can transform data management and connectivity to help companies identify new ways to optimize efficiency and maximize the likelihood of drug product success.
For this final instalment, we will explore the obstacles obstructing effective commercialization and how they can be overcome through enhanced use of data enabled by Agentic AI.
Understanding what’s hindering commercialization success
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.
However, the current path to commercialization features a number of barriers that can slow and complicate the final launch of a new drug product.
Entering the EU and other European life sciences markets, for instance, presents regulatory hurdles that can delay crucial filing processes. Furthermore, the ongoing requirements of post-market drug efficacy monitoring and real-world evidence collection add significant complexity.
Traditional sales and marketing approaches drain resources and time, potentially hindering post-launch profitability.
Effectively managing customer relationships across diverse markets, with varying patient needs and local regulations, also poses a considerable challenge for companies expanding their European footprint.
Agentic AI presents a solution
Capturing and utilizing data from across the drug discovery, development, GMP manufacturing and commercialization journey is crucial to overcoming these commercialization challenges. Every step of the pharmaceutical process generates data that can be analyzed to help life sciences companies commercialize their treatments more quickly and efficiently. However, many pharma companies struggle to mine and use their data effectively due to disparate legacy systems and siloed data, all compounded by the limitations of human-only analysis and action.
Agentic AI systems represent a significant opportunity to overcome these barriers by enabling greater virtual communication between different areas of the drug development and commercialization process, as well as augmenting 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.
Agentic AI can support the more effective use of data to 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.
Implementing Agentic AI
For life science companies to achieve their commercialization efficiency goals with Agentic AI, they will need an effective AI agent system. This can be a challenge when implementing the technology into existing digital infrastructure.
Expert support is critical to ensuring Agentic AI can live up to its full potential at the commercialization stage, and elsewhere in the product lifecycle.
To learn more about how to implement Agentic AI effectively in commercialization, and to find out how Cognizant and Microsoft can help, download our latest eBook now https://www.cognizant.com/emea/en/cmp/agentic-ai-in-life-sciences.