The adoption of artificial intelligence (AI) in the pharmaceutical and biotech sectors is expanding beyond drug discovery to encompass research and development (R&D) support, manufacturing, commercialization, and even regulatory strategies. Initially, AI was primarily used for identifying potential drug candidates and analyzing clinical data. Now, it is enhancing production efficiency, quality control, market entry strategies, regulatory compliance, and automating internal documentation, accelerating the trend of AI transformation (AX) across the value chain.
According to a report by Samil PwC titled 'Innovation in Pharmaceutical Companies Based on AI,' companies that fully integrate AI across their organizations could potentially double their operating profits by 2030. Specifically, if the level of AI integration in the pharmaceutical industry increases, it is projected to generate an additional annual operating profit of approximately $254 billion by 2030. The United States is expected to lead with $155 billion, followed by emerging markets at $52 billion and Europe at $33 billion.
The role of AI in the pharmaceutical industry has been most prominent in the R&D sector. AI enhances speed and accuracy in drug candidate discovery, preclinical and clinical trial design, site selection, and document automation.
For instance, global pharmaceutical giant Merck utilizes its AI-based drug development platform, AIDDISON, to screen 60 billion compounds and propose new synthesis methods to identify optimal candidates. Amgen has developed a machine learning-based clinical optimization platform, ATOMIC (Analytical Trial Optimization Module), which has more than doubled patient enrollment speeds in clinical trials.
The benefits of AI are also evident in operational areas. AI is increasingly used for optimizing production schedules, predictive maintenance, quality control, and demand forecasting in the pharmaceutical industry. A notable example is Sanofi, which recently launched an AI-based decision-making app called Plai, developed in collaboration with Aily Labs, for use in R&D, clinical trials, and manufacturing.
AI's role in regulatory strategies is also growing. New models are emerging that utilize large language databases to assist with inquiries from regulatory agencies and predict the likelihood of approval for submitted documents, evolving AI into a 'helper' for market entry and approval strategies.
In line with this trend, global pharmaceutical companies are ramping up their AI investments. Eli Lilly announced plans in January to establish an AI innovation lab in partnership with NVIDIA, with a potential investment of up to $1 billion over the next five years. Novo Nordisk is collaborating with OpenAI, while Bristol Myers Squibb (BMS) is integrating Anthropic's generative AI across its operations.
Domestic pharmaceutical companies are also quickly accelerating their AI integration. Celltrion recently announced plans to fully implement AI across three key areas: drug development, manufacturing, and administration. The company aims to apply AI in candidate discovery and validation, as well as throughout production and administrative tasks, to enhance development speed, production efficiency, and automate workflows.
SK Biopharm has established an AI and digital transformation center and is expanding its internal applications, including a customized news report automation system. Regulatory functions are also beginning to incorporate AI, as evidenced by GC Green Cross, which recently developed an AI-based regulatory affairs chatbot called RegulAItor. This chatbot is designed to assist regulatory strategists by utilizing datasets from FDA guidelines and internal approval documents.
In the pharmaceutical industry, AI is not only seen as a 'future investment' that increases the success rate of new drugs but also as a 'current cost-saving measure' that reduces inefficiencies in production, quality, sales, and support functions. An industry insider stated, "We view AX not merely as a digital transformation but as a structural reorganization aimed at both defending profitability and pursuing growth strategies. AI is no longer just an 'experimental tool' but has established itself as a management infrastructure that transforms profit structures and work processes."
According to a report by Samil PwC titled 'Innovation in Pharmaceutical Companies Based on AI,' companies that fully integrate AI across their organizations could potentially double their operating profits by 2030. Specifically, if the level of AI integration in the pharmaceutical industry increases, it is projected to generate an additional annual operating profit of approximately $254 billion by 2030. The United States is expected to lead with $155 billion, followed by emerging markets at $52 billion and Europe at $33 billion.
The role of AI in the pharmaceutical industry has been most prominent in the R&D sector. AI enhances speed and accuracy in drug candidate discovery, preclinical and clinical trial design, site selection, and document automation.
For instance, global pharmaceutical giant Merck utilizes its AI-based drug development platform, AIDDISON, to screen 60 billion compounds and propose new synthesis methods to identify optimal candidates. Amgen has developed a machine learning-based clinical optimization platform, ATOMIC (Analytical Trial Optimization Module), which has more than doubled patient enrollment speeds in clinical trials.
The benefits of AI are also evident in operational areas. AI is increasingly used for optimizing production schedules, predictive maintenance, quality control, and demand forecasting in the pharmaceutical industry. A notable example is Sanofi, which recently launched an AI-based decision-making app called Plai, developed in collaboration with Aily Labs, for use in R&D, clinical trials, and manufacturing.
AI's role in regulatory strategies is also growing. New models are emerging that utilize large language databases to assist with inquiries from regulatory agencies and predict the likelihood of approval for submitted documents, evolving AI into a 'helper' for market entry and approval strategies.
In line with this trend, global pharmaceutical companies are ramping up their AI investments. Eli Lilly announced plans in January to establish an AI innovation lab in partnership with NVIDIA, with a potential investment of up to $1 billion over the next five years. Novo Nordisk is collaborating with OpenAI, while Bristol Myers Squibb (BMS) is integrating Anthropic's generative AI across its operations.
Domestic pharmaceutical companies are also quickly accelerating their AI integration. Celltrion recently announced plans to fully implement AI across three key areas: drug development, manufacturing, and administration. The company aims to apply AI in candidate discovery and validation, as well as throughout production and administrative tasks, to enhance development speed, production efficiency, and automate workflows.
SK Biopharm has established an AI and digital transformation center and is expanding its internal applications, including a customized news report automation system. Regulatory functions are also beginning to incorporate AI, as evidenced by GC Green Cross, which recently developed an AI-based regulatory affairs chatbot called RegulAItor. This chatbot is designed to assist regulatory strategists by utilizing datasets from FDA guidelines and internal approval documents.
In the pharmaceutical industry, AI is not only seen as a 'future investment' that increases the success rate of new drugs but also as a 'current cost-saving measure' that reduces inefficiencies in production, quality, sales, and support functions. An industry insider stated, "We view AX not merely as a digital transformation but as a structural reorganization aimed at both defending profitability and pursuing growth strategies. AI is no longer just an 'experimental tool' but has established itself as a management infrastructure that transforms profit structures and work processes."
* This article has been translated by AI.
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