Rethinking Pharma Commercial Targeting with AI - with Philip Poulidis of ODAIA

2026-03-04 · Source: The AI in Business Podcast · Field: Business & Management — Sales & Commercial Development, Corporate Strategy & Leadership, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

ODAIA CEO Philip Poulidis discusses how AI can bridge the gap between biopharma brand strategy and real-world execution, addressing issues like misaligned workflows and static targeting. He explains that while brand strategies and data are robust, the disconnect occurs during execution, leading to teams interpreting plans differently and market shifts outpacing insights. AI enables real-time prioritization of healthcare professionals (HCPs), orchestration of omnichannel engagement, and measurement of impact through patient outcomes rather than activity metrics. Poulidis highlights the necessity of embedding AI into existing workflows, fostering cross-functional adoption, and conducting focused pilots to demonstrate value quickly. He notes that AI can accelerate decision-making, reduce wasted spend (citing $5 billion in the U.S. alone), and make field calls and digital engagement more purposeful.

Key takeaway

For Directors of AI/ML in life sciences seeking to enhance commercial effectiveness, prioritize AI initiatives that solve clear commercial problems rather than starting with technology. Focus on embedding AI directly into existing workflows like CRM and campaign tools to ensure seamless adoption and measure success by patient outcomes and internal efficiencies, not just engagement metrics. This approach will accelerate decision-making and optimize resource allocation.

Key insights

AI can bridge the gap between biopharma brand strategy and real-time execution by enabling dynamic targeting and outcome-based measurement.

Principles

• Embed AI directly into existing workflows.
• Prioritize outcomes over activity metrics.
• Address commercial problems, not just technology.

Method

Integrate AI into CRM, planning, and campaign tools to provide real-time, data-driven recommendations for HCP targeting and omnichannel engagement, shifting focus from activity to patient outcomes.

In practice

• Run focused pilots with measurable KPIs.
• Build cross-functional AI adoption teams.
• Utilize feature stores for domain-specific data engineering.

Topics

• Pharma Commercial Targeting
• AI Adoption Strategy
• Predictive Commercial Intelligence
• Omnichannel Engagement
• Attribution Modeling

Best for: Executive, Director of AI/ML, Marketing Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.