As you highlighted in your recent TradeTalks interview, AI is projected to generate between $350 billion and $410 billion annually for the pharmaceutical sector by 2025, driven by innovations in drug development. How is AI supporting drug discovery and other areas of pharma?
- Drug Discovery & Design: AI accelerates identification of new targets and designs novel molecules, predicting protein structures and drug-likeness with high accuracy.
- Preclinical & Repurposing: Machine learning enables virtual screening, predictive toxicology, and discovery of new uses for existing drugs, cutting lab time and costs.
- Clinical Development: AI enhances trial design, patient stratification, and monitoring via digital biomarkers, boosting success rates.
- Data Integration & Surveillance: Multi-omics integration, knowledge graphs, and pharmacovigilance tools improve insights, compliance, and safety monitoring.
- Impact: Shorter timelines, reduced costs, higher R&D success, and potential for personalized therapies.
You specifically called out recent innovations with generative AI — can you elaborate on how the pharma industry is leveraging Gen AI?
In discovery, Gen AI designs novel molecules, predicts protein structures, and accelerates target validation. In clinical development, it streamlines trial protocols, patient recruitment, and generates synthetic control arms. For Medical and Regulatory, GenAI drafts compliant safety reports, medical information, and submissions. Within Commercial Operations, HCP engagement teams use it to create personalized, MLR-approved content across digital channels, boosting reach and credibility.
Based on your work at ValueDo, how do you see AI impacting pharma beyond 2025?
AI and generative AI are already well adopted in pharma research and development (36%). However, adoption and scaling rates are much lower within pharma commercial operations. This gap is driven by multiple challenges: cultural elements, such as legacy CRM systems and reliance on human representatives, as well as compliance and credibility issues, as pharma is a highly regulated industry where AI wrappers or AI agents cannot function as freely as in other sectors, and, finally, scaling and integration barriers that risk creating silos. Our humanized-AI Pharma-HCP platform, Jawaab (jawaab.ai), is a step in addressing these challenges.
You also noted that commercial pharma has been slow to adopt AI because of the lack of compliance. From your perspective, what compliance and regulations need to be in place to help drive adoption?
This is the core of AI adoption within pharma commercial space. Here are some core compliance and regulatory pillars that are a must-have:
- MLR (Medical, Legal, Regulatory) Review: Zero tolerance for AI hallucinations, so AI outputs must align with promotional regulations, approved label content, and fair balance standards set up by Pharma cross-functional teams to meet U.S. FDA and guideline organization regulations.
- Patient Safety & Pharmacovigilance: Systems must capture, escalate, and document adverse events or product complaints flagged in AI interactions.
- Data Privacy & Security: HIPAA, GDPR, and local data laws require strict control of HCP and patient information, with audit-ready logs.
- Audit & Governance: Automated real-time audits (SOC2), clear human oversight, documentation of AI outputs, and traceability of decision-making are expected by regulators and internal compliance.
What can pharma companies do to prepare for the next wave of AI innovation?
Here are some areas of opportunity, specially within pharma commercial, that will see some interesting transformations and innovative experiments:
- Personalized Engagement: Tailored, compliant AI conversations for HCPs and patients.
- Omnichannel Scale: Consistent messaging across reps, MSLs, and digital.
- Field Productivity: Dynamic training, call briefs, and instant follow-ups.
- Faster Approvals: Draft-ready content speeds MLR review and execution.
Actionable Insights: Analytics drive next-best actions and stronger outcomes.
