AI Is Only As Good As The Data Behind Your Clinical Trial Supply Chain
By Laura Hay, senior director of global program management, Trax Group | 2025 Winner, everywoman Customer/Passenger (Leader) Award
Artificial intelligence has become one of the most discussed topics in clinical trial supply chains. Organizations are investing heavily in predictive analytics, machine learning, and automation with the expectation that these technologies will improve forecasting accuracy, optimize inventory, and reduce operational risk.
The conversation is often centered around one question: How can we use AI?
In my experience, there is a more important question supply chain leaders should ask first: Is our data ready for AI?
Artificial intelligence is not a replacement for poor operational processes or disconnected systems. It is an accelerator. When organizations have accurate, timely, and trusted data, AI can uncover patterns that improve planning and decision-making. But when the underlying data is incomplete, delayed, or inconsistent, AI simply produces faster versions of the same flawed decisions. The effectiveness of artificial intelligence will always depend on the quality of the information that powers it.
For clinical trial supply chains, where every decision has the potential to affect patient dosing, inventory availability, and study timelines, that distinction is critical.
Clinical supply operations rely on information flowing continuously between RTSM or IRT platforms, ERP systems, warehouse management systems, transportation providers, depots, and investigative sites. Each platform plays an important role in the supply chain, yet they often operate on different update cycles, use different data structures, and serve different operational priorities.
When these systems are not synchronized, AI cannot establish a complete picture of supply chain performance. Instead of identifying opportunities, it begins making recommendations based on partial information.
Imagine an enrollment surge that is immediately reflected within the RTSM platform. Inventory forecasts update almost instantly, signaling increased demand across several clinical sites. Meanwhile, the ERP system has not yet processed recent depot receipts, and shipment confirmations from regional logistics providers are still pending.
An AI forecasting engine reviewing those systems simultaneously is no longer evaluating one version of the truth. It is evaluating multiple versions of reality. Rather than improving planning, it may recommend unnecessary production, additional packaging activities, or expedited transportation that increases costs without improving patient service.
The technology did exactly what it was designed to do. The data simply did not provide an accurate foundation. This is why organizations sometimes struggle to realize the value they expected from artificial intelligence. The issue is rarely the algorithm itself. More often, it is the operational environment surrounding it. Disconnected data creates disconnected decisions.
One of the most common examples appears in inventory management. Clinical supply teams depend on accurate inventory visibility across manufacturing facilities, regional depots, and clinical sites. When inventory balances differ between systems, planners frequently compensate by increasing safety stock or accelerating production schedules. These actions reduce uncertainty in the short term, but they also increase working capital, storage costs, packaging complexity, and the potential for product expiry.
If artificial intelligence is introduced into this environment without first resolving those inconsistencies, the technology simply reinforces existing behaviors. Rather than recommending optimal inventory levels, it learns from historical decisions that were themselves responses to unreliable information. In effect, AI begins optimizing around operational workarounds instead of operational excellence. That distinction has significant long-term consequences.
Organizations often view automation as the first step toward digital transformation. In reality, automation should be one of the final steps. Before predictive analytics, machine learning, or autonomous planning can deliver measurable value, organizations must establish confidence in the information flowing across their supply chain network. That starts with governance.
Supply leaders should clearly define ownership of critical data elements, establish consistent standards across operational systems, and reduce manual intervention wherever possible. Synchronization between RTSM, ERP, depot management, and logistics platforms should be viewed as a strategic capability rather than a technical integration project.
Equally important is ensuring that teams trust the information available to them. When planners routinely validate system outputs using spreadsheets, email chains, or offline reports, artificial intelligence loses access to the organization's most valuable asset — trusted operational knowledge.
The objective is not simply to deploy more technology. It is to create an environment where every system contributes to a consistent and reliable operational picture that enables technology to support better decisions.
About The Author:
Laura Hay is a global supply chain leader specializing in program management, customer success, and account strategy. She has a proven track record of leading cross-functional teams to deliver complex, high-impact initiatives on time and within budget. Laura is known for building strong stakeholder relationships, driving operational excellence, and managing multimillion-dollar programs. She is passionate about connecting people, processes, and technology to build scalable, resilient supply chain solutions that deliver measurable business impact.