As you have highlighted in your recent tradetalk interviewis ai Estimate Generating between $350 billion to $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 and design: AI accelerates the identification of new targets and design of new molecules, predicting protein structures and drug-similarity with high accuracy.
- Preclinical and repurposing: Machine learning enables virtual screening, predictive toxicology and discovery of new uses for existing drugs, cutting laboratory time and costs.
- Clinical Development: AI enhances trial design, patient stratification and monitoring through digital biomarkers, thereby increasing success rates.
- Data Integration and Monitoring: Multi-omics integration, knowledge graphs and pharmacovigilance tools improve insights, compliance and safety monitoring.
- Effect: Shorter timelines, lower costs, higher R&D success, and the possibility of personalized treatments.
You specifically called out recent innovations with Generative AI – can you elaborate on how the pharma industry is leveraging Gen AI?
In discovery, General AI designs new 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 prepares compliance safety reports, medical information and presentations. Within commercial operations, HCP engagement teams use it to create personalized, MLR-approved content across digital channels, increasing reach and credibility.
Based on your work at Valuedu, how do you see AI impacting pharma beyond 2025?
AI and generative AI are already well adopted in pharma research and development (36%). However, rates of adoption and scaling into pharma commercial operations are very low. This gap is driven by several challenges: cultural elements, such as legacy CRM systems and reliance on human representatives, as well as compliance and reliability issues, as pharma is a highly regulated industry where AI wrappers or AI agents cannot act as independently 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 towards addressing these challenges.
You also noted that commercial pharma has been slow to adopt AI due to a lack of compliance. From your perspective, what compliances and regulations should be in place to aid adoption?
This is the core of the adoption of AI in the pharma commercial sector. Here are some of the key compliance and regulatory pillars that need to be in place:
- MLR (Medical, Legal, Regulatory) Review: Zero tolerance for AI hallucinations, so AI output must align with promotion rules, approved label content, and appropriate balance standards established by pharma cross-functional teams to meet US FDA and guideline organization regulations.
- Patient Safety and Pharmacovigilance: The system should capture, escalate, and document adverse events or product complaints identified in AI interactions.
- Data Privacy & Security: HIPAA, GDPR and local data laws require tight control of HCP and patient information with audit-ready logs.
- Audit and Governance: Automated real-time audits (SOC2), clear human oversight, documentation of AI outputs, and decision-making capabilities 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, especially in the pharma commercial sector, that will see some interesting changes and innovations:
- Personalized Engagement: Customized, tailored AI conversations for HCPs and patients.
- Omnichannel Scale: Consistent messaging across rep, MSL and digital.
- Field Productivity: Dynamic training, call briefs and quick follow up.
- Fast Approvals: Draft-ready content speeds up MLR review and execution.
Actionable Insights: Analytics drive next best actions and stronger results.
