The pharmaceutical industry is in the midst of a digital revolution; technology investments and data-driven decision-making are constantly on the lips of business decision-makers. A recent survey of midsize life sciences companies found over half ranked digital transformation as their top business priority.[1] And central to these conversations is AI. Leading firms are leveraging AI to fortify supply chains and streamline operations in an increasingly crowded marketplace.
While only months ago deploying AI solutions offered a competitive edge in this marketplace, it is increasingly becoming a fundamental part of day-to-day activities. From drug discovery to patient recruitment and supply chain optimization, the imperative to introduce AI is piling new pressure on every link in the pharmaceutical value chain. GlobalData’s “State of the Biopharmaceutical Industry” report for 2025 surveyed over 100 business decision-makers in top global pharma firms, and found that nearly a fifth identified AI as the most impactful industry trend for the coming year.[2]
Pharmaceutical companies are integrating AI, big data and automation across their operations. They are doing so in the belief that efficiencies, slashed costs and improved trial outcomes are inevitable. With more than 3,000 drugs already developed or repurposed using AI, the technology’s impact on the industry is only set to grow.
AI: Answering long-standing challenges
Amid this upheaval, clinical trials are in the spotlight. Globally, it is estimated that more than 80% of trials fail to enroll patients on time. The costly delays and need for additional site activations that result can cause headaches for organizers.[3] Recruitment is time-consuming, trial complexity is intensifying as novel therapies enter the market, and companies are confronted with vast volumes of data to manage and regulatory requirements to navigate. AI can make it easier for mid-sized life science organizations to break into the industry. Businesses can use the latest technologies and connect to the broader ecosystem to deliver clinical trials faster and more efficient.
AI is addressing these pain points. First, it is speeding up patient recruitment by analyzing large medical databases to match eligible participants with trial criteria. This both reduces the time needed to fill trial slots and increases the likelihood of success. At the same time, AI can optimize site selection, identifying locations most likely to enroll participants efficiently. Studies show that AI-driven site selection improves the identification of top-enrolling locations by between 30% and 50%, and can accelerate trial enrollment by up to 15%.[4]
Data management is another challenge AI is tackling head-on. With refined techniques meaning clinical trials organizers can collect more data points than ever before, AI-powered systems can be used to monitor real-time trial data, identify anomalies and predict missing values. Automating these processes reduces human error and ensures trials run more smoothly, ultimately improving patient outcomes.
By analyzing real-time data on enrollment and drug consumption, AI can improve production planning, reduce wastage and ensure medications reach trial sites on time – meeting efficiency and sustainability targets all at once. “AI is addressing long-standing challenges afflicting the clinical trial process, but the opportunities presented by AI in clinical trials are only just beginning,” says Sonnika Lamont, MRes, GlobalData Senior Analyst at Trials Intelligence. “GlobalData’s clinical trials database shows that the number of clinical trials utilizing AI technology has increased yet again.”
A new paradigm in clinical trials
There is little doubt that AI is providing new solutions to old quandaries. But where it stands out from other technological advances is in how far it equips firms with tools that fundamentally overhaul how they approach clinical trials. The best AI solutions are proactive as well as reactive, putting researchers in the driving seat. Several advances are worthy of specific mention.
One is demand accuracy. Inefficiencies in drug supply forecasting are a common problem faced by clinicians, often resulting in either excessive overproduction or critical shortages at trial sites. AI is helping to eliminate these inefficiencies by analyzing real-time enrollment rates, patient dropout trends and historical data from similar trials to generate highly precise demand forecasts. As well as cutting costs, this significantly lowers the environmental footprint of clinical trials by reducing the waste of expensive investigational drugs. AI’s ability to simulate multiple supply scenarios based on a range of projected trial variables allows firms to make rapid adjustments in response to unforeseen changes. This ensures that the correct product quantities reach the correct trial sites precisely when needed.
Another area where AI is making a significant impact is in predictive patient enrollment. Patient recruitment, as pointed out above, represents a stubborn bottleneck in clinical trials. AI-driven recruitment tools are transforming this process by analyzing large-scale electronic health records, social media discussions and even genetic data to identify eligible patient populations with unprecedented speed and precision. In a recent case study at Mount Sinai Medical Center, AI-powered topological data analysis helped researchers identify three previously unrecognized subgroups of type 2 diabetes patients by examining clinical and genetic data.[5] This level of specificity allows for more targeted trial designs, ensuring that the right patients are recruited from the outset. As well as helping recruit patients once a trial is underway, AI can identify the best candidates before trials have even begun – offering huge efficiencies to trial organizers.
Third is real-time data management and anomaly detection within trials. The sheer volume of data points collected in modern clinical research means human supervision alone can lead to oversights in accuracy and compliance monitoring. AI-powered systems now continuously monitor trial data, flagging inconsistencies and proactively predicting missing values before they become problematic. This ensures the integrity of clinical trial data, reducing the time required to move from trial completion to regulatory approval. Large pharmaceutical firms such as Roche and AstraZeneca have already adopted AI-driven language models to generate optimized eligibility criteria, making the recruitment process more efficient and ensuring that trials enroll the right mix of participants.
AI is also making significant strides in streamlining document management for clinical trials, particularly in extracting and organizing pertinent information from unstructured sources. For instance, the process of creating and maintaining clinical study master data involves sifting through various documents like technical papers, study data concept sheets, and draft clinical protocols. This task is both time-consuming and prone to errors, given the vast amount of irrelevant information present. However, AI-driven solutions are transforming this landscape. By employing advanced algorithms, these tools can automatically extract relevant attributes from protocols and other documents, seamlessly transferring them to corresponding study attributes. This not only alleviates the burden on trial managers but also ensures consistency and accuracy. Moreover, AI enables natural language interaction, allowing users to ask directed queries to gain insights from documents and previous studies, significantly easing the navigation process. Additionally, the constant validation of study information against the latest document versions ensures alignment and minimizes discrepancies, thereby enhancing overall study integrity. This innovative approach will revolutionize how trial organizers manage and update their documents, increasing productivity and ensuring that study data remain accurate and up-to-date, across all systems.
These examples point to a paradigm shift underway in the clinical trial world. AI is equipping researchers with predictive powers that would have seemed like the stuff of sci-fi only a decade ago. As it continues to evolve, these powers will only become more pronounced, ensuring that life-saving treatments reach patients cleanly and efficiently.
Orchestration of advanced therapies in the age of AI
As clinical supply chains embrace the digital revolution, SAP Cell and Gene Therapy Orchestration is transforming treatment order management by integrating AI-enabled control towers and dynamic scheduling. In this high-stakes environment – from biospecimen collection to therapy administration – AI enhances scheduling accuracy by analyzing both historical and real-time data. AI agents recommend optimal time slots, adjust scheduling parameters on the fly, and during slot maintenance, flag data inconsistencies while suggesting corrective adjustments. Moreover, AI-driven analytics prioritize biospecimen pickup slots using predictive modeling, prevent double-booking through continuous monitoring, and forecast capacity needs to secure critical processes. Together, these capabilities ensure that clinical trial operations remain agile, cost-effective, and compliant with regulatory standards.
Where next?
For clinical trial organizers who have not yet done so, where should they turn when looking to roll out AI across their pipelines? Comprehensive solutions like SAP’s Intelligent Clinical Supply Management solution have set the standard over recent years. The solution integrates AI, machine learning and real-time analytics to optimize clinical trial supply chains. It enables companies to forecast demand more accurately, streamline logistics, and reduce supply cycle times. Large and mid-size companies using SAP Intelligent Clinical Supply Management have enhanced their visibility across clinical supply chains, helping them minimize wastage and ensure timely drug delivery.
The results speak for themselves. By adopting SAP’s AI-driven cloud ERP solutions, including machine learning and robotic process automation, one firm increased its supply chain efficiency by 30% and reduced compliance issues by 50%.[6] SAP has a true end-to-end solution, offering seamless collaboration among all health stakeholders – including pharmaceutical firms, contract manufacturers, and logistics providers – to create an industry-wide standard for clinical trial supply management. With companies rolling out AI already seeing tangible benefits, there is no time to delay. SAP’s Intelligent Clinical Supply Management solution offers a comprehensive AI-driven framework to tackle clinical trial challenges, maximize efficiency and enhance sustainability – and their experts are on hand to help you implement it.
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[1] https://www.forbes.com/sites/sap/2024/01/19/learn-how-ai-expands-life-sciences-growth-potential/?sh=4f0121997e08
[2] GlobalData Strategic Intelligence, “State of the Biopharmaceutical Industry 2025 Edition,” January 2025.
[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC7342339/#ref2
[4] https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinical-development-with-artificial-intelligence
[5] https://www.clinicaltrialsarena.com/features/ai-clinical-trial-recruitment/?cf-view
[6] https://www.sap.com/asset/dynamic/2023/03/c6c91398-687e-0010-bca6-c68f7e60039b.html
