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How leading pharma companies use AI to reduce the cost of R&D



Every pharmaceutical company wants to reduce the cost of R&D without sacrificing results, and artificial intelligence (AI) is increasingly crucial to this effort. The companies that figure out how to wield AI will see transformative changes both in their own costs and in the affordability of medical treatments, drugs and therapies.

Over 90 AI companies focused on drug discovery were established in 2022 and 2023. Many of them are partnering with pharma giants, who are busy developing in-house capabilities in their quest to accelerate drug discovery, streamline R&D and find cost efficiencies. 

Read on to learn how AI is improving efficiency and reducing costs at every stage of pharma R&D. See real-life examples to inspire your organization’s quest to use AI to tackle historically high R&D costs. 

[STRM] Interior image_4 roles for AI in pharma R&D

What’s the role of AI in pharma and biotech today? 

Companies across the pharma and biotech industries are finding use cases for AI. Here are some of the most promising applications.

Developing safe and effective drugs

AI for pharma R&D accelerates drug discovery by quickly analyzing data, predicting drug-target interactions and identifying promising compounds. This saves time and money in the earliest stages of R&D, allowing researchers to focus on the most viable candidates. 

AI tools can also simulate the behavior of molecules and predict their properties, helping researchers identify risks and challenges earlier and faster than prior methods. 

Accelerating clinical trials

AI-powered algorithms can streamline patient selection for clinical trials, including screenings and predicting how patients will respond to treatments. By improving trial efficiency with AI, companies spend less time and money finding candidates, conducting trials and delaying other activities — all while maintaining safety and patient privacy.

Tailoring treatment

AI in pharma is effective at analyzing large sets of patient data, which contributes to tailored treatment approaches based on common characteristics. Algorithms can integrate patient-specific data, including genetic information, medical records and treatment outcomes, to identify patterns and predict personalized interventions. When patients receive timely care that meets their needs, they enjoy better outcomes, and the health care system incurs reduced costs. Unnecessary or ineffective treatments are avoided, as medical professionals can optimize their recommendations.

Improving knowledge access

When AI tools are applied to internal knowledge bases and other platforms, they can help employees access and contribute crucial knowledge that might otherwise remain undocumented. 

For example, organizations leverage Starmind’s ability to provide instant human expert verification to drive faster and smarter decision-making and streamline critical processes across your organization. 

Starmind AI in action: Dräger 

Dräger uses Starmind’s AI-powered expertise directory to provide its teams with quick access to detailed specialist expertise from across the company. As a result, salespeople now spend substantially less time searching for answers, which has led to a 12-percentage-point increase in their productive working time. 

The Starmind platform has reduced the redundancy of questions by Dräger employees, with a 64% decrease in queries needing multiple answers within just five months.

[STRM] Interior image_4 cost benefits of AI in pharma

4 ways AI in pharma creates cost savings

Here are common ways AI tools can help companies offset the rising costs of R&D in the pharmaceutical industry.

Greater efficiency

AI-powered systems can automate processes, free up labor and improve operational efficiency. Pharma companies, in particular, can leverage AI for tasks like data analysis, documentation and compliance to save money on resource allocation and operational inefficiencies. 

For example, Starmind's AI-powered expertise directory connects employees with internal experts, enabling efficient knowledge sharing and collaboration. Starmind reduces the time spent searching for information, keeps projects moving and avoids duplication of effort by providing instant access to expert-verified information. 

Faster innovation

AI can help uncover valuable insights and identify new opportunities for drug development, repurposing and recombination. With these insights, pharma companies can focus on the most promising areas, reducing the time and cost involved in fruitless R&D efforts. For instance, AI platforms can analyze vast amounts of scientific literature, clinical trial data and molecular structures to accelerate the discovery of potential drug candidates.

Easier issue resolution

A major drain on enterprise productivity is the inability of workers to find answers to questions when they don’t know who to ask or where to look. AI-powered systems provide access to large knowledge bases while also connecting employees with relevant experts. The result is employees self-resolving issues in less time and with greater success, reducing the time and costs associated with R&D delays. 

Improved collaboration

Large organizations must communicate effectively across locations, borders and even languages — finding and sharing knowledge either in real time or asynchronously. AI-powered platforms can help by making information and expertise more accessible, eliminating silos and connecting employees to relevant subject matter experts to foster innovation and problem-solving. 

For example, Roche uses Starmind’s translation feature to connect its global team across six languages. This collaborative approach reduces duplication of efforts, saves time spent searching for information and increases productivity — all of which can be connected to costs.

4 companies using AI to lower pharmaceutical R&D costs

Here are examples of leading companies that are reducing pharmaceutical R&D costs with AI.


The world's largest pharmaceutical company has formed numerous partnerships with AI firms to improve clinical trials, drug discovery and patient stratification. Notably, Pfizer’s use of AI and supercomputing has shortened the development time of critical drugs like the COVID-19 treatment Paxlovid. AI not only reduced costs in bringing this drug to market quickly but also contributed to increased revenue and competitive advantage.


U.K.-based Exscientia is considered a pioneer in AI-driven drug design. The company developed the first functional precision oncology platform that has effectively guided treatment selections and improved patient outcomes in prospective interventional clinical studies. The platform was also the first to progress AI-designed small molecules into clinical trials. 

Exscientia's leading cancer drug candidate, GTAEXS617, is in trials for treating advanced cancers in the breast, non-small cell lung, and head and neck. 


Novartis has improved its R&D efficiency with Starmind’s AI-powered expertise directory. This collaboration has moved Novartis beyond traditional documentation to a dynamic community-driven approach, connecting over 20,000 employees across the enterprise with expert knowledge and resources needed for accelerated drug development and market delivery. 

Starmind’s platform helps Novartis employees quickly find the expertise they need, which is crucial in a field where project delays could have multimillion-dollar implications. This approach has improved internal collaboration, information flows and Novartis’ ability to capitalize on market exclusivity and first-mover advantages. 


Johnson & Johnson-owned Janssen Pharmaceuticals is using AI across multiple aspects of drug development, from discovery to clinical trials and manufacturing. The company uses advanced AI-driven methods such as protein structure prediction for more effective therapeutic design and cell painting techniques to predict drug toxicity earlier.

Its AI-powered platform uses machine learning to enhance site feasibility, increase site engagement and optimize patient recruitment strategies. This AI-powered platform improves the efficiency and effectiveness of clinical trials, as well as helping select candidates most likely to benefit from successful treatment.

5 AI and pharma cost-saving opportunities

Pharma companies can integrate AI to save time, encourage innovation, make better use of their resources and enable data-driven decision-making. Let’s take a look at some practical use cases of AI lowering the cost of pharma R&D.

Drug discovery and development

AI-powered platforms help companies analyze vast amounts of data more quickly than humans or other computing methods. This enables faster, more accurate predictions of drug-target interactions and the identification of promising compounds. This reduces time spent and overall R&D spend.

AI can also optimize drug development processes through predictive modeling and simulation techniques, which improve decision-making and reduce costs. Starmind's AI-powered expertise directory, for example, can connect R&D teams with internal experts for quick collaboration and problem-solving.

Supply chain management

AI can help pharma companies optimize supply chain management by analyzing historical data sets, market trends and external factors to predict demand, optimize inventory levels and streamline distribution processes. This leads to more efficient supply chain operations, reduced waste and corresponding cost savings in procurement and logistics. 

Clinical trials

AI-powered tools can optimize clinical trials by streamlining patient recruitment. This process identifies suitable trial participants based on specific criteria and predicts patient responses to different treatments. Companies save time and money by ensuring trials are conducted quickly and with greater efficacy.

Adverse-event detection and safety monitoring

AI-powered systems can analyze vast amounts of real-world data, including EHRs and social media, to detect safety signals and potential adverse events associated with medications. This can result in early warnings and proactive measures to ensure patient safety. By detecting any potential problems early, you can save money on quality control problems, manage compliance issues and reduce legal and reputational risk. 

Duplicate efforts

AI-powered platforms can connect researchers with in-house expertise and findings that may be otherwise unknown to them. For example, if employees don’t know whether specific research is available, they could leverage a large language model like StarGPT. Users can instantly access stored company data, ask questions and connect with internal subject matter experts to validate the findings.

AI-powered tools help teams avoid duplicating each other’s efforts. This especially saves time and money if those existing efforts didn't produce results. When researchers have access to the latest research data and expertise, they can focus on the most promising paths without losing time searching for information.

Drive AI cost savings in pharma R&D

We’ve only scratched the surface of AI in pharma for driving revenue and profit while reducing the cost of R&D. The key is to use AI tools that can handle enterprise-level needs and are designed with safety and security in mind.

Starmind bridges the gap between AI and human experts. It creates an AI-powered expertise directory that helps employees quickly find the specific expertise and answers they need. Starmind enables efficient knowledge sharing and collaboration while reducing the time spent searching for information. 

Don't miss out on the opportunity to leverage AI in your R&D teams. Contact us today and find out how you can safely incorporate enterprise AI solutions to transform how your team works.


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Revolutionizing pharmaceutical R&D:
Harnessing AI to combat escalating costs [DOWNLOAD REPORT]

Pharma companies are leveraging the power of AI to combat escalating costs in R&D. Download the report to take a deeper dive into:

  • The research cost conundrum and why it exists. 
  • How AI is offsetting drug development costs.
  • The financial benefits of an AI-powered expertise directory in drug discovery.
  • How Roche and Merck are using secure AI 

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