The ROI in Artificial Intelligence for Pharmaceutical Companies

The ROI in Artificial Intelligence for Pharmaceutical Companies
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The pharmaceutical industry is increasingly investing in Artificial Intelligence (AI) across its value chain, from research to manufacturing and commercialization. This strategic move aims to enhance efficiency, reduce costs, and accelerate drug development processes. This article explores the return on investment (ROI) that AI brings to the pharmaceutical sector, focusing on short-term, mid-term, and long-term investments and benefits, supported by real use cases from major pharmaceutical companies.

Key Highlights

AI investments in pharma are growing, with the potential to significantly increase EBITDA and drive double-digit gains across research, clinical trials, and commercial areas.

Major pharmaceutical companies like Sanofi, AstraZeneca, GSK, and Pfizer are leveraging AI for various purposes, including accelerating mRNA research, identifying drug targets for rare diseases, and optimizing drug discovery tools.

The adoption of AI and machine learning (ML) in drug R&D is expected to grow by 106% over the next three to five years according to Cowen Group study, indicating a strong commitment to these technologies within the industry.

 AI adoption in preclinical and clinical phases steadily increases, with an anticipated growth rate exceeding 90%. The most immediate potential for AI lies in new candidate discovery, followed by identifying new drug biomarkers and targets., promising more targeted, safer, and cost-effective therapies delivered at a faster pace.

1. Short-term Investment and ROI

In the short term, pharmaceutical companies are focusing on integrating AI into drug discovery and development processes. AI’s ability to analyse vast datasets rapidly accelerates the identification of promising drug candidates, significantly reducing the time and cost associated with traditional drug discovery methods. For example, Insilico Medicine utilized AI to dose the first patients in a phase 2 trial, marking a significant milestone in AI-driven drug development[2]. Additionally, companies like Pfizer have adopted Google Cloud’s AI-powered drug discovery tools to optimize target and lead identification, showcasing the immediate benefits of AI in enhancing research productivity[2].

Investment Avenues:
  • Enhanced Data Management: AI application in managing and analysing voluminous data to pinpoint potential therapeutic candidates.
  • Robotic Laboratory Systems: Investment in robotics and AI for automating standard lab tasks, boosting throughput while minimizing errors.
  • AI-driven Discovery Tools: Deployment of AI algorithms for predicting drug-target interactions and lead optimization.
  • Clinical Trial Design and Execution: AI utilization for crafting efficient clinical trials, including patient selection and endpoint determination.
  • Streamlined Regulatory Navigation: Employing AI for more effectively meeting regulatory standards, thereby hastening market entry.

Short-term investments in AI can significantly accelerate the drug discovery process, reduce operational costs, and improve the efficiency of clinical trials. For instance, Pfizer’s adoption of Google Cloud’s AI-powered drug discovery tools showcases the immediate benefits of AI in enhancing research productivity[6].

2. Mid-term Investment and ROI:

Mid-term investments in AI are centered around clinical trials and the advancement of personalized medicine. AI technologies are being used to streamline patient recruitment and optimize trial designs, as seen in Sanofi’s deployment of its AI app, plai, to accelerate mRNA research and improve clinical trial site selection[2]. Furthermore, partnerships like the one between AstraZeneca’s Alexion and Verge Genomics focus on using machine learning for drug target identification in rare diseases, demonstrating AI’s potential to unlock new therapeutic opportunities and revenue streams in specialized medicine.

Areas to Invest:
  • Precision Medicine: Developing AI models to analyse genetic data for personalized drug development.
  • Patient Recruitment for Clinical Trials: Using AI to analyse health records and identify suitable candidates for clinical trials.
  • Supply Chain Optimization: Implementing AI to forecast demand, manage inventory, and optimize logistics.
  • Manufacturing Process Optimization: Applying AI for real-time monitoring and control of manufacturing processes.
  • Market Analysis and Forecasting: Utilizing AI for market research, competitor analysis, and sales forecasting.

Mid-term investments enable pharmaceutical companies to tailor treatments to individual patients, streamline clinical trials, and optimize supply chains. Sanofi’s AI app, plai, for accelerating mRNA research and improving clinical trial site selection exemplifies the mid-term benefits of AI.

3. Long-term Investment and Returns:

Over the long term, the ROI of AI in pharmaceuticals is expected to be transformative, with AI driving innovation across the entire value chain, from research to manufacturing and commercialization. The integration of AI in drug R&D is projected to grow significantly, with AI accounting for approximately 16% of drug discovery efforts and expected to increase by 106% in the next three to five years. This growth is indicative of the pharmaceutical industry’s commitment to leveraging AI for long-term strategic advantages, such as reducing the overall time and cost of bringing new drugs to market and enhancing patient care through more precise and effective therapies.

Areas to Invest:
  • Advanced Drug Repurposing: Using AI to find new indications for existing drugs, extending their market life.
  • Digital Health Solutions: Developing AI-powered apps and devices for disease monitoring and management.
  • Global Data Integration: Creating platforms for seamless data exchange and collaboration globally.
  • Sustainable Manufacturing Practices: Investing in AI to enhance energy efficiency and reduce waste in manufacturing.
  • Enhanced Patient Engagement: AI-driven personalized patient support and interaction through digital mediums.

Long-term investments in AI promise to transform the pharmaceutical industry by enabling the development of new business models, enhancing patient care, and fostering sustainability. Novartis’s investment in generative AI company Yseop to automate study documents across clinical trials is a testament to the long-term potential of AI to revolutionize the industry.


The investment in AI across the pharmaceutical value chain presents a promising avenue for enhancing efficiency, reducing costs, and accelerating the development of new drugs. From short-term gains in drug discovery to long-term transformations in patient care, AI is poised to deliver significant ROI for the pharmaceutical industry. As companies like Sanofi, AstraZeneca, and Pfizer continue to lead the way in AI adoption, the industry at large is set to benefit from more targeted, safer, and cost-effective therapies delivered at a faster pace to patients in need.

Peyman Moh, a seasoned leader with over 20 years of experience in digital health and innovation, excels in transforming foresight into impactful realities. As the former Director of Digital Health & Innovation at GSK and founder of Foretell Innovation Lab, he has spearheaded major projects, established innovation accelerators, and provided advisory services. Renowned for his strategic foresight and ability to foster ecosystem collaborations, Peyman's expertise in future-back thinking and innovation lifecycle management positions him as a pivotal figure in navigating the rapidly evolving innovation landscape.