The pharma industry is accustomed to operating in a highly regulated and risk-conscious environment. While companies continue to invest in scientific innovation, they have been slower to adopt technology.
Long cycle times and uncertainty around new platforms have made organizations hesitant to disrupt entrenched workflows. That hesitation has led to many leaders falling into a “watch and wait mentality,” missed opportunities to lower costs, shorten timelines and deliver new treatments to the patients in need faster.
Pharma has a habit of implementing “point solutions”, or solving smaller problems incrementally, rather than integrating technologies capable of reimagining workflows from beginning to end. While point solutions can improve specific tasks and address pain points without impeding existing processes, they limit overall improvements in efficiency and speed. Then, when another pain point emerges, the process becomes a rinse and repeat exercise.
Real-world data and AI are examples of how this habit is beginning to make a U-turn. In a recent Tufts CSDD study, 10 out of 14 companies said they expect the ROI from real-world data to increase over the next one to two years, showing how organizations are moving beyond isolated use cases and applying these tools across the entire drug development lifecycle. Real-world data can help monitor safety signals and identify eligible patient populations, while also improving visibility into these populations, minimizing recruitment challenges, and helping to design trials that mirror how patients are being treated in real world settings.
Real-world data continues to be valuable even after a drug has reached the market by providing a better understanding of treatment adoption, unmet needs and how therapies are being administered. The ability to support decisions from design to commercialization is what makes real-world data more than a single use tool, it’s an asset that can reinvigorate how drugs are developed, evaluated and commercialized.
However, it’s important to note that more data does not mean better insights; pharma already has access to an enormous amount of healthcare data. The real benefit comes from turning high-quality and representative data into applicable evidence as quickly as possible.
Its value materializes when it is integrated into an organization’s workflows. AI agents are the next step to this integration, as they turn real-world data from being a resource that must be analyzed manually into an interactive interface providing teams with quick and insightful answers in a fraction of the time. Rather than research teams manually pulling data and running and reviewing multiple analyses, an AI agent can carry out parts of that workflow by querying relevant data, organizing results and surfacing patterns through a simple request.
An AI agent can also help clinical teams identify patients who meet specific trial requirements and find where those patients are receiving care, as well as the best recruitment site. It can help researchers understand how patients are being treated in real world settings, compare outcomes across patient cohorts, or quickly surface patterns that may inform trial feasibility or future clinical trial design.
An important acknowledgement is that AI agents cannot and should not be the sole decision maker for organizations. To reap the most benefit, teams must collaborate with these tools to move through and examine large amounts of information faster, reducing some of the work needed to organize and identify the most useful insights, while still prioritizing human oversight and high-quality representative data.
Pharma doesn’t need to be first in line every time a new technology reaches the market, but excessive caution driven by uncertainty risks organizations falling behind their competitors.
Real-world data has already evolved from an emerging concept into an evidence-based resource across the drug development lifecycle, and AI agents can make it even easier to access and utilize.
An organization’s competitive edge will not come from having the latest technology, but from knowing when technologies offer clear benefits and adapting workflows to get the most from investments.
About Sujay Jadhav
Sujay Jadhav is the Chief Executive Officer at Verana Health where he is helping to accelerate the company’s growth and sustainability by advancing clinical trial capabilities, data-as-a-service offerings, medical society partnerships, and data enrichment. Sujay joins Verana Health with more than 20 years of experience as a seasoned executive, entrepreneur, and global business leader.
Most recently, Sujay was the Global Vice President, Health Sciences Business Unit at Oracle, where he ran the organization’s entire product and engineering teams. Before Oracle, Sujay was the CEO of cloud-based clinical research platform goBalto, where he oversaw the acquisition of the company by Oracle. Sujay is also a former executive for the life sciences technology company Model N, where he helped to oversee its transition to a public company. Sujay holds an MBA from Harvard University and a bachelor’s degree in electronic engineering from the University of South Australia.
