The application of generative artificial intelligence (Gen-AI) in the pharma and medical products industry could generate an economic value of between $60 billion and $110 billion per year. This is revealed in a study by McKinsey & Company entitled “Generative AI in the Pharmaceutical Industry: Moving from Hype to Reality” which explores the contribution of Gen-AI to different stages of technology transfer. Generative AI is the technology that can generate complex content such as text, images, molecular structures, and even operational strategies; the one, to be clear, that also underlies such now well-known products as ChatGPT, Copilot, Gemini, and Midjourney.
Thestudy analyzed 63 use cases of generative AI in the life sciences sector by calculating the potential economic impact on five industries: research and drug discovery, clinical trial efficiency, manufacturing operations, marketing and medical affairs.
Faster R&D
With an estimatedvalue between $15 billion and $28 billion, generative AI can accelerate the identification of new drug targets and improve compound design.
With this technology, the time required to manually analyze scientific publications and patents can be reduced by 30 percent. In addition, advanced in silico screening models improve the effectiveness of analysis by up to 2.5 times, allowing promising new candidates to be identified in weeks instead of months.
Even the design of complex molecules, such as proteins and drug vectors, can be accelerated by more than 3 times, facilitating the development of new drugs and vaccines. By speeding therapies to market, generative AI can also help pharmaceutical companies address the problem of “product lifecycle compression”-that is, the increasingly short time available to exploit the economic value of a new drug (which, according to McKinsey, has shrunk by nearly 18 months in 20 years, from 11.7 years to 9.8 years).
Optimization of clinical trials
It takes an average of $1.4 billion (plus ten years of work) to bring a drug to market, and 80 percent of these costs are attributable to the clinical development phases.
With a potential of between $13 billion and $25 billion, AI can make clinical trials faster and cheaper by improving patient enrollment and automating data management. For example, it can act as a digital assistant to monitor and optimize trial performance,improving operational costs by 20 percent and accelerating patient enrollment by 10-20 percent.
In addition, intelligent automation in clinical data management allows reducing management costs by 30 percent and halving the time needed to close trial databases. Response to requests from regulatory authorities can also be speeded up, reducing follow-up requests by 50%, thus promoting faster drug approval.
More efficient operations
For operational applications, the study estimates a value between $4 billion and $7 billion: AI solutions can optimize production, reduce maintenance costs, and improve supply chain efficiency.
AI-based virtual assistants can increase overall equipment effectiveness (OEE) by 10-15% and reduce the workload of maintenance technicians by up to 35%. In addition, real-time inventory optimization enables more effective forecasting of production needs, increasing forecast accuracy by 15% and reducing supply chain costs by 2-3%.
Faster and cheaper trading
Generative AI facilitates personalization of marketing materials and optimization of patient experiences, with an estimated value of $18-30 billion.
These solutions make it possible to reduce content production costs by 30-50% and speed up the creative and production process of marketing materials by more than 20%. In addition, through more targeted and personalized care, the patient experience can be improved, reducing treatment abandonment rates by 5-10%.This approach enables stronger patient relationships and better adherence to treatments.
Medical affairs
With an impact ofbetween $3 billion and $5 billion, AI can speed up the generation of medical records and improve engagement with health professionals.
For example, automated document generation can reduce medical writing costs by 20-30% (but it goes up to -70% once solutions are mature) and reduce the time required for medical-legal review by between 50 and 70%.
In addition, with the ability to quickly synthesize scientific literature, engagement with health care professionals can increase 2-3 times, providing them with personalized, high-quality information in a short time.
