The biopharmaceutical industry is launching new products at an accelerating rate. Projections suggest that companies will launch more new drugs over the course of the next five years than in the entire previous decade. In one sense, this progress represents a huge vindication of the industry’s powerful research and development (R&D) engines.
Biopharma innovation is advancing on multiple fronts, from commercial success stories like the GLP-1 therapies–so-called “everything drugs” that are proving effective against everything from obesity to addiction to the revolutions in biology that are generating entirely new platforms for treating disease, from gene editing to personalized cellular therapies.
Creating any effective new medicine is an enormous scientific achievement, yet it is only the beginning of the battle for biopharma companies. These multiple waves of new product launches are fighting for space and recognition in what is inevitably becoming an increasingly crowded and competitive marketplace. Across multiple therapeutic areas, more newly launched drugs are falling short of their revenue expectations.
This commercial underperformance is despite the significant investments in go-to-market strategy. In recent years, costs have spiraled for pharma companies across the board, as the price of all inputs from energy to staffing soar and economic volatility piles pressure on margins.
One of the heaviest costs is sales, general and administrative (SG&A) expenditure, driven largely by the expense of selling products. On average, companies spend up to 20% to 25% of their revenue on SG&A. This is even higher than the share swallowed by the industry’s R&D engines, which eat up an average of 18% to 19% of revenues.
![Daniel Mathews [EY Global Life Sciences]](https://www.genengnews.com/wp-content/uploads/2025/05/Dan-Mathews-300x300.jpg "Daniel Mathews [EY Global Life Sciences]")
Daniel Mathews [EY Global Life Sciences]
As the industry seeks to contain these escalating costs by improving operational efficiency, artificial intelligence (AI) has become an increasingly important lever. Biopharma is doing more deals with AI startups than ever before, with R&D the major focus of activity. Companies are investing in AI platforms that can explore chemical space, generate, model and test compounds rapidly in silico, accelerate clinical trial recruitment, and a thousand other approaches to making the drug development process more efficient.
From its enormous cost to its huge attrition rate, drug development expense is a fundamental issue for pharma. Anything AI can do to address that challenge would be a major gain for the industry, and since R&D is driven by data and analytics, the huge increase in computational power offered by AI has clear potential to help, whether by accelerating clinical development or positively inflecting the cost curve.
The effort to launch products successfully is increasingly becoming a pain point just as important for the industry as the high cost of R&D. So, can we expect AI to move the needle on launch strategy as well?
Where launch processes depend on data and analytics, AI can offer the same level of process industrialization that is brought to R&D. AI can help companies understand the market, digesting huge amounts of information on brand performances and market trends and turbocharging the process of turning data into insights. These AI-driven insights can help refine strategies and shape training programs for sales teams.
When the product is launched, AI can step up again to help improve and optimize interactions with providers, provide digital platforms to support education and administration of complex therapies, and capture and relay real-world data and insights that can improve and customize messaging around the product.
But beyond the data and the analytics, selling pharmaceuticals has always depended on human relationships. The existing model depends on the expertise of sales representatives pushing content to health care providers (HCPs). At first, this relationship-driven model might seem like a tough area for AI to add value to.
However, as AI evolves beyond mere algorithms to open up new frontiers in generative AI and agentic AI, the art of the possible is shifting rapidly.
Consider the basics of sales engagement. A sales rep today is primed to deliver three to five core messages and is versed in a few key clinical papers that provide material for discussions with clinicians. They must deliver these messages to an audience which is time-poor, already besieged by marketing, and highly discriminating and specific in their needs and demands. To get access to this audience in the first place, the rep needs to leverage their existing relationships with clinicians and improvise with their own interpersonal skills as much as possible to build their relationships and strengthen connections.
The effort to launch products successfully is increasingly becoming a pain point just as important for the industry as the high cost of R&D. [Kobzev3179/Getty Images]
An AI-driven sales agent would have none of the same constraints around subject area knowledge or messaging: AI can comfortably digest all the clinical literature and turn it into compelling messages conveyed with whatever AI-generated multimedia content and experiences, from video onward, it calculates to have the best chance of resonating with the audience.
AI not only has far greater freedom of movement within information space than any human rep but also has effectively unlimited time; while a rep will typically try to see an HCP in surgery at specific times (perhaps once every six weeks), AI platforms can effectively target the HCP with highly tailored messaging throughout the day, every day.
Unlike a human sales rep, AI can also create and customize its own persona from scratch in its efforts to delight and engage the HCP audience. An AI solution can in principle take on whatever avatar with deep knowledge of whatever subjects, on- or off-topic, will help capture and retain the attention of the HCP.
Just listing out the AI-enabled possibilities for launch strategies immediately highlights the ethical challenges that emerge when AI enters the picture. If an AI has trained itself on huge clinical data sets, how do you ensure it hasn’t absorbed errors and biases embedded in the material? Is it acceptable for companies to use the capacities of AI in an all-out push to forcibly capture the attention of HCPs?
Moreover, what ethical constraints need to be placed around AI’s ability to customize interactions and messages to its audience? Minimally, messaging should be based on genuine information, but how can the standard of accuracy be monitored if the content is continually adapting to its audience? In today’s commercial environment, content is approved by experienced medical and other personnel to ensure that compliance is maintained. But if content becomes dynamic rather than static, regulation becomes a vastly more complex challenge.
Biopharma launches today are so complex and so important to get right that ultimately companies will inevitably deploy full-scale AI to address the barriers to successful launch. But as these emerging ethical challenges make clear, the raw power of AI needs to be safely managed. That means deploying AI in a business context where it is united both with existing launch capabilities and with human-centered expertise to manage the inherent risks of the technology and unlock the opportunities it presents.
The need to unite these capabilities is discussed in detail in the recent EY report, “How biopharma can get the right mix of people and tech for launch success.” As we have seen, the industry is winning at R&D, with more and more products making it through clinical trials to commercial launch but it is not winning at launch strategy. More of these products come up short when they actually reach the market. There is no simple technological fix for this challenge.
The industry will need not only the tech platform but also partners who can bring with them all the supporting capabilities needed to overcome the many hurdles and ensure future biopharma launches can be executed with confidence.
Daniel Mathews serves as EY Global Life Sciences leader and EMEIA Life Sciences sector market lead.
The post Why the Right Launch Strategy Can Get Biopharma from Clinical Breakthroughs to Commercial Blockbusters appeared first on GEN - Genetic Engineering and Biotechnology News.
Biopharma innovation is advancing on multiple fronts, from commercial success stories like the GLP-1 therapies–so-called “everything drugs” that are proving effective against everything from obesity to addiction to the revolutions in biology that are generating entirely new platforms for treating disease, from gene editing to personalized cellular therapies.
Creating any effective new medicine is an enormous scientific achievement, yet it is only the beginning of the battle for biopharma companies. These multiple waves of new product launches are fighting for space and recognition in what is inevitably becoming an increasingly crowded and competitive marketplace. Across multiple therapeutic areas, more newly launched drugs are falling short of their revenue expectations.
Commercial underperformance
This commercial underperformance is despite the significant investments in go-to-market strategy. In recent years, costs have spiraled for pharma companies across the board, as the price of all inputs from energy to staffing soar and economic volatility piles pressure on margins.
One of the heaviest costs is sales, general and administrative (SG&A) expenditure, driven largely by the expense of selling products. On average, companies spend up to 20% to 25% of their revenue on SG&A. This is even higher than the share swallowed by the industry’s R&D engines, which eat up an average of 18% to 19% of revenues.
Daniel Mathews [EY Global Life Sciences]
As the industry seeks to contain these escalating costs by improving operational efficiency, artificial intelligence (AI) has become an increasingly important lever. Biopharma is doing more deals with AI startups than ever before, with R&D the major focus of activity. Companies are investing in AI platforms that can explore chemical space, generate, model and test compounds rapidly in silico, accelerate clinical trial recruitment, and a thousand other approaches to making the drug development process more efficient.
From its enormous cost to its huge attrition rate, drug development expense is a fundamental issue for pharma. Anything AI can do to address that challenge would be a major gain for the industry, and since R&D is driven by data and analytics, the huge increase in computational power offered by AI has clear potential to help, whether by accelerating clinical development or positively inflecting the cost curve.
The effort to launch products successfully is increasingly becoming a pain point just as important for the industry as the high cost of R&D. So, can we expect AI to move the needle on launch strategy as well?
AI helps companies understand the market
Where launch processes depend on data and analytics, AI can offer the same level of process industrialization that is brought to R&D. AI can help companies understand the market, digesting huge amounts of information on brand performances and market trends and turbocharging the process of turning data into insights. These AI-driven insights can help refine strategies and shape training programs for sales teams.
When the product is launched, AI can step up again to help improve and optimize interactions with providers, provide digital platforms to support education and administration of complex therapies, and capture and relay real-world data and insights that can improve and customize messaging around the product.
But beyond the data and the analytics, selling pharmaceuticals has always depended on human relationships. The existing model depends on the expertise of sales representatives pushing content to health care providers (HCPs). At first, this relationship-driven model might seem like a tough area for AI to add value to.
However, as AI evolves beyond mere algorithms to open up new frontiers in generative AI and agentic AI, the art of the possible is shifting rapidly.
Consider the basics of sales engagement. A sales rep today is primed to deliver three to five core messages and is versed in a few key clinical papers that provide material for discussions with clinicians. They must deliver these messages to an audience which is time-poor, already besieged by marketing, and highly discriminating and specific in their needs and demands. To get access to this audience in the first place, the rep needs to leverage their existing relationships with clinicians and improvise with their own interpersonal skills as much as possible to build their relationships and strengthen connections.
The effort to launch products successfully is increasingly becoming a pain point just as important for the industry as the high cost of R&D. [Kobzev3179/Getty Images]
An AI-driven sales agent would have none of the same constraints around subject area knowledge or messaging: AI can comfortably digest all the clinical literature and turn it into compelling messages conveyed with whatever AI-generated multimedia content and experiences, from video onward, it calculates to have the best chance of resonating with the audience.
AI not only has far greater freedom of movement within information space than any human rep but also has effectively unlimited time; while a rep will typically try to see an HCP in surgery at specific times (perhaps once every six weeks), AI platforms can effectively target the HCP with highly tailored messaging throughout the day, every day.
Unlike a human sales rep, AI can also create and customize its own persona from scratch in its efforts to delight and engage the HCP audience. An AI solution can in principle take on whatever avatar with deep knowledge of whatever subjects, on- or off-topic, will help capture and retain the attention of the HCP.
From technological progress to ethical problems: the future of AI in launch strategy
Just listing out the AI-enabled possibilities for launch strategies immediately highlights the ethical challenges that emerge when AI enters the picture. If an AI has trained itself on huge clinical data sets, how do you ensure it hasn’t absorbed errors and biases embedded in the material? Is it acceptable for companies to use the capacities of AI in an all-out push to forcibly capture the attention of HCPs?
Moreover, what ethical constraints need to be placed around AI’s ability to customize interactions and messages to its audience? Minimally, messaging should be based on genuine information, but how can the standard of accuracy be monitored if the content is continually adapting to its audience? In today’s commercial environment, content is approved by experienced medical and other personnel to ensure that compliance is maintained. But if content becomes dynamic rather than static, regulation becomes a vastly more complex challenge.
Biopharma launches today are so complex and so important to get right that ultimately companies will inevitably deploy full-scale AI to address the barriers to successful launch. But as these emerging ethical challenges make clear, the raw power of AI needs to be safely managed. That means deploying AI in a business context where it is united both with existing launch capabilities and with human-centered expertise to manage the inherent risks of the technology and unlock the opportunities it presents.
The need to unite these capabilities is discussed in detail in the recent EY report, “How biopharma can get the right mix of people and tech for launch success.” As we have seen, the industry is winning at R&D, with more and more products making it through clinical trials to commercial launch but it is not winning at launch strategy. More of these products come up short when they actually reach the market. There is no simple technological fix for this challenge.
The industry will need not only the tech platform but also partners who can bring with them all the supporting capabilities needed to overcome the many hurdles and ensure future biopharma launches can be executed with confidence.
Daniel Mathews serves as EY Global Life Sciences leader and EMEIA Life Sciences sector market lead.
The post Why the Right Launch Strategy Can Get Biopharma from Clinical Breakthroughs to Commercial Blockbusters appeared first on GEN - Genetic Engineering and Biotechnology News.