Pharma launch strategy in the age of technology and AI

The biopharma industry is at an inflection point. Scientific advances are yielding more targeted therapies and a proliferation of assets in late-stage development across nearly every therapeutic category. This hypercompetitive environment, combined with accelerated development timelines and the insurgence of technology across the entire healthcare system, is making it increasingly challenging to drive new product adoption and sustain market position over time.  

With 180 major drug patents expiring by 2030 and an expected loss of $236 billion in revenue, pharmaceutical leaders are rethinking how they manage their portfolios. As major pharma organizations work to advance more than 250 late-stage assets, unpredictable M&A activity and enhanced technical capabilities have resulted in a resurgence of small molecules and more nimble biotech companies launching on their own—changing the rules of the game. Brand teams no longer have the luxury of taking five years to reach peak sales; they need to find new ways to accelerate product adoption to drive sustainable revenues. 

This all comes at a time in which the larger healthcare ecosystem has become increasingly complex and leaders seek out new ways to drive efficiency and value through technology. For life sciences, payers, and providers alike, AI is making it easier for brands to put patients at the center and individualize their care. At the same time, agile competition and shifts in policy are fundamentally reshaping profitability models.    

Why traditional launch models are no longer enough 

In this environment, even the most promising new product innovations risk falling into “launch black holes,” in which products fail to gain rapid adoption and fall short of achieving full potential, despite having strong value for patients. The culprit? A launch model that has not kept pace with the speed of change and advances in technology in an increasingly connected healthcare ecosystem.   

Innovative leaders should ask themselves these questions: 

• Has the order-of-entry model become obsolete?  
• Are traditional go-to-market models more of a habit than a necessity?  
• Can we rely on our proven return-on-investment models in this new era? 

For those who dare to think differently, an opportunity exists to examine the role technology and AI play in the transformation of launch strategy, planning, and execution. In this new era, success will belong to those who can pivot from static planning to dynamic orchestration, from mass messaging to micro-targeting, and from siloed functions to agile, AI-augmented teams.   

Avoid the black hole—what you can do now 

For those looking to future-proof their launch and improve effectiveness, we offer the following tenets:  

• Reimagine new product planning and launch operating models: Identify processes, reporting, and decision tools that can be automated—and reallocate staff to critical strategic thinking, external market development, and stakeholder engagement. 
• Invest in data and AI infrastructure where it matters most: Integrate data across siloed departments to drive better insights and improved alignment across teams.  
• Scale real-world evidence early: Integrate real-world evidence into clinical and early go-to-market strategy prior to entering pivotal trials using large language models (LLMs) to achieve scale.
• Model value proposition and access scenarios: Leverage AI to simulate health technology assessments and payer response to potential clinical profiles and evidence beyond primary and secondary clinical trial endpoints. 
• Reinvent the traditional ROI model: Consider how AI can help predict launch performance relative to pre-launch investment scenarios to remove product and market infrastructure barriers and ensure optimal uptake in the context of the product, competition, and market development. 

Next-generation launches will be led by those who embrace technology, efficiency, and ecosystem-centricity at the core. The future of launch excellence is not just digital—it’s data-driven, dynamic, and decisively different. 

This article was originally published in Pharmaceutical Executive.

Contributing authors: Karla Anderson, Partner, Jacob Graham, Partner, and Cavay Ip, Director