The future of pharma launch is AI-powered

If it feels like the pace of the pharmaceutical industry is getting faster, that’s because it is. 

Drugs are launching faster into a market that is more competitive and driven, and that includes new drug launches. Given market volatility, it is difficult for pharma company launch teams to prepare properly—which means they often have to make refinements to their launch plan and execution tactics in real-time as demands and behavior shift.  

AI can make the launch process easier and more effective, but it needs to overcome a few hurdles first. First and foremost, firms are still working through legal and compliance concerns, including data privacy and security. That’s a bigger hurdle for commercial teams analyzing consumer data, as compared to R&D work sifting through potential drug compounds and analyzing de-identified medical datasets. Firms may also be cautious in moving forward due to the consequences of AI getting things wrong. R&D has always been comfortable with failure, as a minute percentage of scientific innovations end up being commercialized. AI is helping those teams more quickly determine what will succeed. On the contrary, commercial teams have a very low tolerance for failure and are hence more skeptical with broader, more expansive use of AI.   

Upwards of 95% of pharma companies are investing in AI, with these investments estimated to reach $25 billion by 2030, according to Mordor Research. A few early adopters have incorporated AI into their commercialization process. There is likely to be a big shift in the next few years as others realize how AI can be a differentiator in the launch process, once they move past the legal and compliance barriers. Here are 3 ways in which we foresee new use of AI in the next one to two years in commercialization:  

1. Driving greater personalization through micro-segmentation 

With AI, pharma can speed up launch planning and rethink market strategy in ways that human minds cannot. Take the example of physician segmentation at launch. Most brands today categorize physicians into three to four segments based on attitudes, treatment approach, and behavior. That assumes that every physician in each segment behaves identically and doesn’t permit much nuance. This is why most brands see variable adoption of their brand within each segment. This is where AI can flip the script.  

AI allows launch teams to hyper-segment the market, with the goal of truly personalizing engagement to each individual based on their specific situation and nuances. With current strategies, it is impossible to fathom that: individualized communications and marketing to each potential prescriber, at scale. But AI can expand the possibilities.  

This is not restricted to physician engagement alone. AI is increasingly being used to influence consumer health behavior. Over 40 million Americans use ChatGPT every day for their healthcare needs, according to OpenAI, ChatGPT’s parent company. Even in its infancy, AI is already having a profound impact on how patients learn about their health and how providers diagnose conditions and communicate with patients. Pharma is not far behind—Pfizer recently launched Health Answers by Pfizer, a GenAI experience that provides answers to consumers' health and wellness questions. It’s likely that other pharmaceutical companies will continue to explore AI solutions that help them gather data on patients’ unmet needs and identify ways to personalize engagement and meet patients where they are in the treatment journey.  

2. Digital Twins will become a mainstay 

Digital twins are virtual replicas of the physical world that allow users to simulate systems and processes, making experimentation fast and easy. With AI, digital twins will become a standard tool in the pharma pre-launch process, helping teams simulate the impact of launch strategies step by step. For example, instead of holding focus groups or asking analysts to make predictions about how the market might react to a certain discount amount, market access leaders can first simulate outcomes through digital twins. Analysts can feed the digital twins more data than what a human brain can comprehend in a workshop setting, and model endless scenarios with actual data to determine the best strategies. Launch teams can simulate impact of their launch strategies much faster if they model against digital twins, identifying likely issues with their messaging and content so they can be proactively improved. With advancements in spatial intelligence and AI, digital twins will become increasingly critical to launch teams seeking to fine tune their strategy and execution. That, along with human intuition and experience, can only drive better decisions and launch outcomes.  

3. Launch execution will become ‘hybrid’ 

With agentic AI, the industry is entering a new era––one where hybrid human-digital teams are reinventing business processes. Humans will work with AI agents side by side to accelerate their work and rapidly prototype new solutions.  

The traditional commercialization process is not designed to accommodate strategy changes—or at least change in a timely manner. It is designed to operate in regular cycles (often quarterly), with marketing and advertising agencies operating on annual planning cycles. Most teams aren’t set up to change resource deployments such as channel investments, direct-to-consumer programs, sales force deployments, and speaker programs on an as-needed basis. AI can’t necessarily address the resourcing and regulatory challenges that impact a team’s ability to move quickly, but it can give commercial and marketing teams real-time information that can help them make effective pivots.   

Pharma is beginning to reinvent other business processes with AI. As a result, pharma leaders will not just work with people, but also with digital coworkers, also known as AI agents. That’s especially true within it comes to content creation, an integral part of micro-segmentation efforts.  

4. Barriers to innovation and how to manage them 

In most pharma companies, legal and compliance concerns are preventing widespread adoption of AI in commercialization and launch. While risk must be taken seriously, commercial leaders need to work closely with their legal teams to find practical paths forward. The question for compliance teams should not be what can’t be done, but what can be. Without that collaboration, the greater risk is falling behind. 

Applying AI to product launches increasingly resembles computer programming. That changes the role of the human in that process. It removes data processing and analysis limitations, while giving marketing team members a different focus. The role of commercial team members shifts to quality assurance, feedback, and better prompt engineering. Executives, brand launch leaders, and marketers must be comfortable with that to move forward.  

The change management process for using AI in product launches extends beyond global strategy, brand, and marketing teams. It requires engagement from sales representatives, operations, and IT as well. Upskilling is essential, as many of these teams may not have prior AI experience. They need training that equips them to use AI effectively alongside their existing responsibilities. While AI already represents a significant technology investment, organizations must also budget for upskilling. Without it, AI tools risk becoming a roadblock rather than an accelerator. 

Patient and clinician trust in AI is rising, signaling a true inflection point for pharma commercialization. As early adopters demonstrate what’s possible, the rest of the industry is poised to accelerate—moving from experimentation to scaled impact far more quickly than before.