How AI Can Solve Poor Communication Across Clinical Trial Supply Chains

By Rajat Mishra

Although drug discovery and developing new treatments sit at the core of revenue generation for biopharma companies, investments have been on the decline.

In fact, R&D spending at the top 16 pharmaceutical companies declined by 3.6% this past year. AbbVie had the steepest decline, with the company spending 29% less on research, while Johnson & Johnson’s research expenses fell by nearly 15% to $14.7 billion.

Although this figure still reaches the billions, the fact that the top companies in the space are all decreasing spend on this revenue-generating activity of R&D points to a broader need to trim costs.

Clinical trial supplies are a large yet important line in the expenses. Rather than trying to source cheaper materials that affect quality, companies can find ways to save overhead by looking at the overall supply chain.

Already complicated supply chain coordination challenges become even more complex when supply teams are managing fluctuating patient enrollment, country-specific labeling requirements, protocol amendments, depot inventory levels, and time-sensitive site resupply needs across global studies.

AI-powered communication offers a way to improve the operational management of these complex global supply chains to discover new treatments while keeping overheads down.

Intelligent Demand Forecasting

One of the most immediate and apparent sources of waste associated with the clinical trial supply chain can be traced back to unpredictable demand.

Enrolling patients onto trials and coordinating medical teams is a major effort. If supplies aren’t available at the right time, it not only delays overall timelines, it also wastes resources.

However, fragmented and siloed data visibility between researchers, clinical sites, manufacturing sites, and logistics companies means that demand is unpredictable. Supplies often fail to arrive on time and in sufficient quantities.

Intelligent demand forecasting supports a just-in-time future for clinical trial supply chains that reduces the potential for waste or supply shortages.

Here, the industry can train AI models on historical trial data from the company to get a baseline understanding of the activity. In practice, this data may include enrollment velocity by site, randomization patterns, patient visit schedules, depot stock levels, shipment timelines, and country activation delays that can affect investigational product demand. This allows supply teams to adjust manufacturing schedules, redistribute inventory between depots, or proactively trigger resupply shipments before shortages impact patients or study timelines.

This near-real-time approach reduces the risk of overproduction or shortages and enables proactive decision-making.

Collaboration Across Global Teams

Miscommunication is another common source of waste. Clinical trials may be taking place across multiple countries and time zones. Meanwhile, modern supply chains are complex global networks that are reliant on certain manufacturers for core equipment and medical supplies.

This represents a challenge that AI-powered communication can solve, avoiding errors due to language barriers or time differences.

Natural language processing (NLP) systems can summarize lengthy reports, extract key updates, and deliver concise briefings tailored to specific roles in a variety of languages. This ensures that every stakeholder within the supply chain has access to the right information in an accessible manner.

For example, a supply chain manager might receive alerts about inventory risks, while a clinical site coordinator gets updates on shipment timelines. This becomes particularly important in clinical trials where delays in shipment approvals, temperature excursion reviews, import/export documentation, or protocol amendment communication can disrupt site readiness and patient dosing schedules.

By delivering the right information to the right person at the right time, AI reduces information overload and improves operational clarity.

In turn, automated translations promise to boost engagement across global teams and enhance collaboration between research institutions and hospitals.

Given that up to 80% of clinical trials fail to meet their enrollment targets, enhancing collaboration and engagement can support a more robust supply chain across the industry. 

Regulatory Compliance

Finally, regulatory compliance is another challenge for global supply chains. Trial managers need to ensure that the regulations of each country are met as products and materials pass through the chain.

This process needs to be closely documented to create a proper audit trail. As such, regulatory compliance is a critical yet resource-intensive task associated with the supply chain. Further, manual processes and fragmented records create an added risk that could see vital paperwork stuck in a silo. For clinical trial supply teams, this may include temperature monitoring records, chain-of-custody documentation, shipment release approvals, or investigational product accountability records required for inspection and audits.

Here, AI can be used to create a central knowledge bank to store important records and communications throughout the trial. Its usefulness can be enhanced further with the addition of intelligent AI assistants with the ability to retrieve information, answer questions, and carry out administrative tasks.

Additionally, AI can flag potential compliance risks in communications, such as missing information or inconsistent data, allowing teams to address issues before they escalate.

The Hidden Cost Of Poor Communication

Product delays, delivery issues, and site closures are just some of the issues that can burden supply chain efficiency.

AI-powered communication tools can help clinical trial supply teams reduce operational silos, improve coordination across sponsors, depots, manufacturers, and sites, and respond more quickly to disruptions that impact trial timelines and patients.

About The Author:

Rajat Mishra is a senior technology executive and entrepreneur focused on applying AI and human expertise to communication in life sciences. With leadership experience spanning Microsoft and Cisco, where he led a team of more than 1,000 as one of the company’s youngest Senior VPs, Rajat has spent his career scaling technology and operations within complex global organizations. He is also the co-founder of the Mobion Foundation, a nonprofit public benefit corporation whose mission is to empower underprivileged children to realize their full potential.