PUBLISHER: Roots Analysis | PRODUCT CODE: 1919791
PUBLISHER: Roots Analysis | PRODUCT CODE: 1919791
As per Roots Analysis, the global AI in pharma manufacturing market size is estimated to grow from USD 1.20 billion in the current year to USD 34.7 billion by 2040, at a CAGR of 28% during the forecast period, till 2040. The new study provides market size, growth scenarios, industry trends and future forecasts.
Artificial Intelligence (AI) is a subdivision of computer science that enables computers to perform intricate tasks that usually require human intelligence, including learning, reasoning, and making decisions. In the healthcare sector, AI is already being applied in various areas such as drug discovery, clinical trials, diagnostics, personalized medicine, and data management. Within pharmaceutical manufacturing, AI utilizes technologies like computer vision, machine learning, generative AI, and deep learning to improve process monitoring, identify inefficiencies, lower production costs, and enhance product yield.
Pharmaceutical manufacturing faces numerous inefficiencies, including inefficient workflows, equipment downtime, quality control issues, and supply chain interruptions. These inefficiencies can result in higher costs, production delays, and variability in product quality. AI addresses these challenges by enabling process optimization, monitoring the performance of plants and equipment, anticipating equipment failures beforehand, managing supply chains, and automating quality control processes. Several pharmaceutical companies, including Pfizer, Moderna, Novartis, Merck, and Sanofi, are integrating AI into their manufacturing operations as the sector evolves towards Pharma 4.0.

Strategic Insights for Senior Leaders
What are the Use Cases of Artificial Intelligence in Pharmaceutical Manufacturing?
More than 60% of major pharmaceutical companies are utilizing AI to revolutionize their manufacturing processes, improving efficiency, quality, and flexibility. Prominent applications include real-time monitoring, automated quality inspections, predictive maintenance, and optimization of the supply chain.
For example, Sanofi applies AI to enhance production yield and process effectiveness; Novartis uses machine learning techniques for real-time monitoring of plants and AI-powered supply chain optimization in drug production; Merck utilizes AI to decrease false reject rates in quality assessments; and Moderna leverages AI-based tools to improve quality control systems. These technologies not only streamline processes but also lead to cost savings and a better regulatory environment for AI in the drug manufacturing sector.
As top pharmaceutical firms and AI solution providers continue to develop their capabilities, incorporating AI into drug manufacturing has become essential for achieving operational excellence and sustaining a competitive advantage in this swiftly changing industry.
Key Drivers Propelling Growth of AI in Pharma Manufacturing Market
The growth of AI in the drug manufacturing sector is driven by an increasing demand for enhanced process efficiency, lower production costs, and the maintenance of consistent product quality. Additionally, rising regulatory support and the ongoing digital transformation within the pharmaceutical industry further promotes the adoption of cutting-edge AI technologies.
It is worth noting that AI applications in drug manufacturing encompass a range of functions, including quality control, predictive maintenance, process development and optimization, monitoring of plant and equipment performance, as well as supply chain optimization. The broadening range of these applications continues to propel substantial market demand for AI solutions specifically designed for pharmaceutical manufacturing.
AI in Pharma Manufacturing Market: Competitive Landscape of Companies in this Industry
The present market environment consists of approximately 130 participants, including major, large, medium, and small enterprises. These organizations possess the necessary skills to deliver AI solutions for drug production across various geographical areas.
Importantly, over 95% of the companies involved in AI for drug production provide advanced software solutions. Further, nearly 80% of these firms are implementing machine learning to digitize various phases of the drug manufacturing process.
Regional Analysis: Asia-Pacific to Propel the market growth in the Coming Years
According to our projections, currently North America captures the majority of the market, and this trend is unlikely to change in the future as well. This is due to the presence of advanced pharma manufacturing infrastructure, early adoption of artificial intelligence (AI) in healthcare technologies and supportive regulatory framework across the region.
However, it is worth highlighting that the market in Asia-Pacific is expected to grow at a higher CAGR during the forecast period. This is driven by the lower implementation costs, supportive government policies fostering digitalization, and rapidly expanding pharmaceutical sector.
AI in Pharma Manufacturing Evolution: Emerging Trends in the Industry
AI is transforming pharmaceutical manufacturing by making processes smarter, faster, and more reliable. Emerging trends include predictive maintenance using machine learning to spot equipment issues early, cutting downtime and costs. Real-time quality control with AI vision detects defects like cracks or contamination instantly in production lines, ensuring consistent drug quality and regulatory compliance. Process optimization employs advanced controls and digital twins to fine-tune parameters such as temperature and mixing, boosting efficiency and reducing waste. AI combined with robotics and IoT enables automated labs of the future for continuous monitoring and adaptive production. These innovations help pharma companies to produce safer medicines quicker while saving money and meeting strict standards.
Key Market Challenges
The market for AI in pharma manufacturing faces significant challenges that slow its adoption. One of the primary challenges is data issues, with poor quality, biases, silos, and limited availability making AI models unreliable for precise production tasks. High costs for setup, integration with old systems, and ongoing maintenance strain budgets, especially for smaller firms hindering the adoption of such technologies. Additionally, strict regulations like GMP and FDA rules demand validation, transparency, and compliance, but AI's "black box" nature complicates approvals and ethics. Moreover, shortage of personnel skilled in both AI and pharmaceutical expertise hampers effective deployment of AI in the pharmaceutical manufacturing domain.
AI in Pharma Manufacturing Market: Key Market Segmentation
Type of Offering
Mode of Deployment
Type of AI Solution
Type of Technology
Application Area
Utility in Drug Manufacturing
Geographical Regions
AI in Pharma Manufacturing Market: Key Market Share Insights
Based on the type of offering, the global market is segmented into hardware, software, and services. According to our estimates, currently, software captures majority share of the market. This is driven by the increasing adoption of software-based solutions that integrate advanced techniques, such as predictive analytics, and process optimization, thereby improving operational efficiency and foster innovation in drug manufacturing.
Based on the type of AI solution, the global market is segmented into standard / off-the-shelf AI solutions and personalized AI solutions. According to our estimates, currently, standard / off-the-shelf AI solutions capture majority share of the market. This is primarily due to industry's preference towards pre-validated, compliant, and ready-to-deploy solutions that can be deployed and scaled rapidly.
AI in Pharma Manufacturing Market: Report Coverage
The report on the AI in pharma manufacturing market features insights on various sections, including:
Key Questions Answered in this Report
Reasons to Buy this Report
Additional Benefits