From Experimentation to Strategic Advantage in Pharma & Biotech
Authors
Lauren Grant
Managing Director,
Commercial Strategy
Ben Rivitz
Associate Principal,
Commercial Strategy
How AI improves New Product Planning decisions in pharma and biotech
New Product Planning and Commercial Strategy teams are being asked to call differentiation, value, and portfolio fit earlier than ever, often before Phase 3 and with limited data. The cost of a wrong call is high, from misjudged opportunities to value stories that arrive too late or competitors who move first.
For years, bespoke analyses and manual synthesis were enough. Today, teams spend too much time finding information and too little time debating what it means for forecasts, valuations, and portfolio choices. AI is already in use across more than 90% of teams surveyed, with clear benefits in speed and effectiveness but constrained by data quality, compliance, and uncertainty about where AI should be applied. AI can rebalance that work, but only when it is applied to the right use cases with clear governance and human oversight.
Use this advisory brief if you:
- Lead or support New Product Planning or Commercial Strategy for pharma or biotech assets.
- Need faster, higher-confidence calls on differentiation, value, and portfolio fit with constrained data and time.
- Want to understand where AI already delivers value for peers in NPP and where adoption is stalling.
About this advisory brief
This advisory brief shares findings from a survey of 25 New Product Planning and Commercial Strategy leaders across pharma and biotech. It maps where teams lose time and miss opportunities, and where AI is already delivering better speed and effectiveness in NPP workflows.
More than 90% of leaders in the study report using AI in some form today. Most see faster insight generation and stronger effectiveness, while keeping forecasts, valuations, and portfolio calls firmly human-led. The brief highlights where AI adds the most value, where trust and governance are limiting progress, and what leading teams prioritize first.
What you will learn
- Where manual work slows NPP and how slow synthesis and scattered inputs delay key portfolio decisions
- Which opportunities go unseen and how AI can surface adjacencies and value story gaps earlier
- How peers stay ahead of competitors when point-in-time research lags fast-moving markets
- What outcomes NPP teams see from AI including faster insights, greater effectiveness, and more time for strategic debate
- What is holding NPP teams back when trust, governance, and unclear priorities slow adoption
- How Trinity applies AI in NPP through ForecastEDGE™, InsightsEDGE™, and digital twin capabilities for value testing, research retrieval, and stakeholder simulation
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