The pharmaceutical industry is undergoing a major technological shift, and Soumyodeep Mukherjee is at the center of it. A data and AI leader with experience at McKinsey & Company and Boston Consulting Group (BCG), he has advised Fortune 500 healthcare and pharma organizations. Now, as Associate Director of Data & Analytics at Genmab, a biopharma company developing antibody therapies for cancer and other diseases, he focuses on data strategy, patient outcomes, and commercialization.
With over 14 years in data engineering, AI, and analytics, he is leading the charge on Agentic AI, an emerging technology set to reshape the life sciences industry by improving operations, engagement with healthcare professionals (HCPs), and business outcomes.
Beyond Generative AI: The Rise of Agentic AI in Pharma
Generative AI has captured serious attention, but Soumyodeep sees greater potential in Multi-modal Agentic AI — where AI-powered agents reason, plan, and execute tasks with minimal human intervention.
“Agentic AI goes beyond generating text or summarizing documents,” he explains. “It enables intelligent systems to orchestrate workflows, optimize decisions and deliver impact.”
These AI agents adapt to market conditions, scientific discoveries, and regulatory shifts, enhancing efficiency and accuracy across pharma’s commercial and R&D functions.
Revolutionizing Pharma Sales: AI-Powered Field Effectiveness
Pharma commercialization relies on engaging the right stakeholders at the right time. Traditional approaches use analytics from conventional structured data feeds, limiting agility. Soumyodeep believes Agentic AI can transform field effectiveness by delivering real-time insights from diverse data sources, including news feeds and websites.
AI-powered sales teams will analyze competitive landscapes, tracking drug launches, formulary shifts, and regulatory updates for proactive decision-making. AI will predict which HCPs and hospitals are most likely to adopt a product, focusing sales efforts where they matter most. It will also drive personalized engagement strategies, offering recommendations based on past interactions and prescribing patterns.
This transformation could increase commercial efficiency, speed up formulary access approvals, and reduce administrative burdens on field reps, allowing them to focus on high-value engagements.
AI in R&D: Accelerating Drug Discovery and Clinical Trials
Beyond commercial applications, Soumyodeep sees Agentic AI reshaping pharmaceutical R&D. AI-powered agents will enhance clinical trials by identifying patient cohorts through genomics, real-world evidence (RWE), and electronic health records (EHRs). Predictive models can optimize trial site selection, improving patient retention. Compliance will become more efficient through optimized regulatory workflows, reducing manual oversight while aligning with FDA and EMA regulations.
These advancements could potentially reduce clinical trial timelines, increase late-stage success rates, and accelerate regulatory submissions, expediting life-saving drug development. “AI will change how drugs are developed,” he says. “From biomarker discovery to trial execution, AI agents will help us move faster and make better data-driven decisions.”
Building a Secure, Scalable, and Ethical AI Framework
Soumyodeep emphasizes that as Agentic AI gains traction, pharmaceutical companies must prioritize governance, security, and compliance. He advocates for Zero-Trust AI security models with encrypted interactions, strict role-based access controls, and strong data privacy safeguards.
AI governance frameworks must ensure decision-making aligns with regulatory and ethical standards, preventing bias and compliance risks. Despite AI’s growing autonomy, a human-in-the-loop approach remains essential, particularly in patient care and regulatory oversight. “AI should augment human expertise, not replace it,” he states.
What’s Next: The AI-Driven Future of Pharma
Soumyodeep believes AI will define the next decade of pharmaceutical innovation, with Agentic AI transforming drug development, commercialization, and personalization. He envisions multi-agent AI ecosystems where AI assistants collaborate under human oversight, enabling real-time decision-making across the pharmaceutical value chain, delivering personalized patient insights, and improving treatment outcomes.
Today, he remains laser-focused on driving AI transformation in pharma as the industry stands at a pivotal moment — transitioning from AI as a support tool to AI as an active collaborator. From revolutionizing drug discovery and clinical trials to enhancing commercial operations and patient care, AI is poised to redefine life sciences.
In the coming decade, Agentic AI-powered ecosystems will accelerate innovation and drive smarter decision-making. Soumyodeep envisions Agentic AI seamlessly embedded in R&D, commercialization, and regulation — ensuring that scientific breakthroughs reach patients faster while optimizing operations.
The future of pharma belongs to AI Agents, and according to Soumyodeep, “The revolution is just beginning.”
