AI Is Moving Clinical Supply From Forecasting To Real-Time Decision-Making

By Laura Hay, senior director of global program management, Trax Group | 2025 Winner, everywoman Customer/Passenger (Leader) Award

Artificial intelligence is becoming one of the most discussed technologies in clinical supply chain management. But the conversation is beginning to change.

For years, much of the focus was on whether AI could improve forecasting. Could it predict enrollment? Could it identify changes in demand? Could it help teams determine how much inventory would be needed?

Those capabilities are important, but they represent only part of the opportunity. The bigger opportunity may be using AI to help clinical supply teams move from identifying what might happen to deciding what to do about it.

Clinical trials are dynamic environments. Enrollment changes. Sites activate at different speeds. Protocol amendments alter requirements. Manufacturing timelines move. Transportation conditions shift. A forecast that was accurate several weeks ago may no longer reflect what is happening today. That creates a fundamental challenge for clinical supply teams: having information is not the same as being able to act on it.

From Forecasting To Decision Support

Traditional planning processes often operate on scheduled cycles. Teams review forecasts, assess inventory, evaluate manufacturing requirements, and make adjustments based on the latest available information. That model can work when conditions are relatively stable, but clinical trials are rarely stable.

AI and predictive analytics can continuously evaluate multiple sources of information, including enrollment patterns, inventory consumption, manufacturing timelines, logistics performance, and regional demand. The value is not simply producing another forecast. It is identifying where the current plan may be heading off course early enough for the team to respond.

That might mean flagging that a depot will need additional stock, identifying that packaging capacity may become a constraint, or showing that a planned manufacturing run no longer aligns with expected demand. The point is not simply to see the change. It is to understand which supply decisions the change may require.

When those signals are connected, teams can evaluate whether to move inventory, adjust production timing, change shipment plans, or accept a different level of supply risk before the situation becomes urgent. For example, if enrollment begins accelerating in one region, the impact may extend well beyond that region's inventory. It could affect packaging requirements, manufacturing schedules, depot allocations, and transportation capacity.

The earlier that impact is visible, the more options the supply team has. A team that sees the change weeks in advance may be able to rebalance inventory between depots or adjust a packaging or manufacturing plan. A team that sees it only after inventory is already committed has fewer choices and potentially higher costs. A system that identifies that connection earlier gives supply leaders more time to evaluate options.

The Human Decision Still Matters

There is sometimes an assumption that AI will eventually make supply chain decisions without human involvement. I don't believe that should be the goal.

Clinical supply decisions often involve considerations that cannot be reduced to a single data point. Patient continuity, regulatory requirements, product characteristics, manufacturing constraints, and study priorities all need to be considered.

AI can help teams see the possibilities faster. Experienced supply professionals still need to determine the right course of action.

That distinction is important. The future of clinical supply will not be about replacing expertise with technology. It will be about giving experts better information at the moment they need it.

Breaking Down The Data Silos

One of the biggest challenges to AI adoption is not necessarily the technology itself. It is the data.

Clinical supply information is often spread across multiple systems, partners, and functions. Enrollment information may sit in one environment. Inventory information may exist somewhere else. Manufacturing, logistics, and depot information may be managed by different organizations. If those systems cannot communicate effectively, AI has limited visibility into the complete picture.

For example, an enrollment signal has limited value if the team cannot connect it to current depot inventory, open orders, manufacturing lead times, and shipments already in transit. The decision depends on seeing those pieces together, not simply having more data available. Connecting the data therefore becomes just as important as implementing the AI solution. A sophisticated model cannot compensate for incomplete or unreliable information.

Moving From Reactive To Anticipatory

The most valuable change AI can bring to clinical supply is a shift in mindset. Instead of asking, "What happened?", teams can increasingly ask, "What is changing?" And then, "What could happen next?" And finally, "What should we do now?"

That progression moves clinical supply from reactive management toward anticipatory decision-making. That matters because supply disruptions become more expensive the later they are discovered.

A potential shortage identified early may be manageable. The same shortage identified after manufacturing capacity has been committed, transportation has been booked, and patient demand has increased can become much more difficult to resolve.

This is where decision timing becomes critical. The earlier a potential problem is identified, the more choices remain available to the supply team. Those choices may include reallocating inventory, changing production quantities or timing, expediting a shipment, or revisiting the assumptions behind the current forecast.

AI Needs Governance Alongside Innovation

There is also an important responsibility that comes with greater use of AI. Clinical supply operates within a highly regulated environment. Decisions need to be explainable, data needs to be trusted, and accountability cannot disappear simply because technology is involved. Organizations should therefore think about AI governance at the same time they think about implementation.

The question should not simply be, "Can AI do this?" It should also be, "How will we validate the information, who owns the decision, and how will the organization respond when the model is wrong?" Those questions will become increasingly important as AI moves closer to execution.

The Next Stage Of Clinical Supply AI

I believe we are moving beyond the idea of AI as simply a forecasting tool. The next stage is about connecting signals to decisions. Clinical supply teams need to know where risk is developing, understand the potential impact, and have enough time to respond.

For clinical supply leaders, the question is ultimately not whether AI can predict a shortage or enrollment change. It is whether that insight arrives early enough to change the decision.

AI can help create that visibility. But the real advantage will come when technology, data, and human expertise work together as part of the same decision-making process.

The goal isn't to make clinical supply more automated for the sake of automation. It is to make it more responsive, more informed, and ultimately more resilient. And in an environment where supply continuity can directly affect patients and trial timelines, that is where the real value of AI lies.

About The Author:

Laura Hay is senior director of global program management at Trax Group and the 2025 winner of the everywoman Customer/Passenger (Leader) Award. With extensive experience in clinical supply chain and program management, Laura focuses on helping organizations navigate complex global supply environments, improve operational visibility, and build more resilient approaches to clinical trial supply. Her expertise spans clinical supply planning, forecasting, inventory agility, global logistics, and the evolving challenges facing life sciences organizations.