How AI Fits Into the Pharmaceutical Supply Chain

The pharmaceutical industry has always operated under – and indeed, against – conditions of uncertainty. From the earliest stages of research through to the delivery of finished medicines to patients, the sector must navigate scientific risk, regulatory scrutiny and increasingly fragile global supply chains. In recent years those pressures have intensified as drug shortages have reached record levels, geopolitical instability continues to disrupt transport networks and regulators raise greater demands around transparency and traceability. At the same time, advances in artificial intelligence have begun to reshape how organisations within the sector operate.

AI adoption within the pharmaceutical industry is often discussed in the context of drug discovery and clinical development, yet just as important are the applications that are emerging further downstream. Manufacturing, supply planning and logistics are becoming the focal points where analytic capability can translate into measurable operational impact.

The scale of this changing landscape is clear, with the market for artificial intelligence in pharmaceuticals projected to grow from around $4 billion today to $25.7 billion by 2030.¹ This rapid growth reflects AI’s increasing role in helping teams navigate uncertainty more effectively, ultimately strengthening the resilience of pharmaceutical supply chains.

What Are the Current Supply Chain Issues?

Drug shortages reflect growing pressure across global pharmaceutical supply chains. They point to deeper weaknesses in how supply chains are structured and managed. In the United States, at least 216 active drug shortages persisted into 2025 after reaching record levels in 2024.² Many have no clearly reported cause, which highlights a core issue in itself. There is limited visibility across the supply chain, making it difficult to identify where disruptions begin or how they move through the system. Similar pressures are also noted across Europe with pharmacists reporting they spend nearly 11 hours a week managing the consequences of a disrupted supply chain.³

With AI now being used to proactively identify potential supply chain disruptions, the focus can shift from reacting to shortages to preventing them. By improving data visibility across sourcing and distribution channels, AI can be used to flag emerging risks earlier and support co-ordinated decision-making across previously fragmented supply chains.

As Irene Sanchez Saura at Bluecrux, a leading supply chain consultancy observes, “AI is clearly going to reshape supply chains in life sciences, but it is becoming obvious that the impact will not come from the models alone. In many organizations, the harder work sits underneath: aligning data, connecting decisions and making sure the technology actually fits how people run the supply chain. Those challenges are rarely just technological. They are often surprisingly familiar: data quality, standards, people, processes and other foundational issues. When those pieces start to come together, AI stops being an experiment and begins to change how teams anticipate and respond to complexity.” This is an important distinction in how AI is being understood within pharmaceutical supply chains, moving the conversation away from autonomous optimisation and towards more practical human-in-the-loop decision making supported by AI.

Within pharmaceutical supply chains, a related data challenge is the ability to interpret it quickly enough to respond to change. AI is emerging less as a replacement for planners and more as a tool for sharper and faster decision-making.

Forecasting models can draw on a broader mix of demand signals, including disease prevalence and consumption patterns, to improve inventory positioning and ensure products are stored closer to points of need. At the same time, scenario modeling allows teams to assess trade-offs between service levels, cost and risk, such as whether to hold additional safety stock, adjust distribution routes, or reallocate inventory across regions, with greater speed and confidence. The real value lies not in full automation, but in enabling earlier intervention and more informed allocation decisions.

By combining historical data, real-time signal and external context, AI helps logistics teams anticipate changes in demand, optimise stock placement and time shipments more effectively, ultimately improving responsiveness and continuity across the supply chain.

Supporting this shift towards more adaptive, data-driven systems, Søren Skjødt, Partner at Implement Consulting Group stated “Today, AI is already creating real value in pharma supply chains by accelerating analysis, improving forecast quality and making trade-offs more transparent, especially in planning and execution. The biggest shift we’re seeing is not automation of decisions, but augmentation of decision makers. Looking ahead, the next step is agentic AI: systems that continuously sense, synthesize and prepare decisions across demand, supply, inventory and logistics, while keeping humans firmly in control. This will allow pharma organizations to move from calendar driven planning to truly adaptive, resilient operations that can respond to volatility in near real time.”

This shift in thinking is increasingly visible across the wider supply chain community, where the conversation has moved decisively from experimentation to implementation. Industry forums such as LogiPharma have become important spaces for discussing how organisations build trust in data and embed AI into core planning and logistics workflows. Increasingly, the focus is less on the technology itself and more on how it can be scaled in a way that strengthens resilience and delivers operational credibility.

Embedding AI Into Supply Chain Decision-Making

AI is increasingly being used to support decision making in environments where large volumes of information must be processed, interpreted and acted upon under time pressure. The challenge lies not only in identifying what is relevant and how it should be interpreted in context, but also in determining what requires attention first. AI therefore is acting as a form of decision triage, filtering and prioritising signals to help ensure that human attention is directed to where it is needed most urgently.

This development is seen elsewhere within the drug supply process. For example, in drug discovery, where only around one in ten compounds entering human clinical trials ultimately reaches the market, AI is being used to accelerate early-stage evaluation by screening large datasets of candidates and identifying the most promising options, with prediction accuracies of up to 95%.^4,5 The underlying challenge is the same; making structured decisions from large volumes of uncertain and often incomplete information.

Within the pharmaceutical supply chain, teams are required to piece together historical and operational context in order to understand the drivers behind disruptions, constraints or performance variation. The data is out there, but AI helps to bring it all together, identify patterns which would be difficult to detect manually and present it in a coherent manner that can enable action to be taken. Consistency and accuracy in data interpretation is equally as important as how quickly the data is actioned.

Jasper Wouters, Vice President Life Sciences at OMP highlighted this shift, stating;
“In pharmaceutical supply chain planning, AI is moving beyond isolated pilots and becoming embedded in core processes such as demand forecasting, supply planning, and production scheduling. Its real value lies in augmenting planners with predictive insights, optimized scenarios, and explainable recommendations that help manage long lead times, yield variability, and regulatory constraints. The future of AI in pharma planning is decision-centric and continuously learning, enabling more autonomous, resilient, and data-driven supply chains.”

The impact of embedding AI is not a change in who makes decisions, but in how those decisions are shaped, by improving the quality and consistency of the information available at the point of action. AI is becoming part of the operating model, supporting more aligned and informed decision making across the supply chain, and reinforcing performance and compliance in a more systematic way.

What Are the Logistical Benefits of AI?

Downstream logistics introduces another layer of complexity, particularly where temperature control is involved. The pharmaceutical cold chain is designed to keep medicines within specified temperature ranges during storage and transport. Any deviation outside those limits risks product deterioration and, in some cases, can risk patient harm.

The financial impact of cold chain failures is substantial. The IQVIA Institute estimates that the biopharma industry loses approximately $35 billion annually due to failures in temperature controlled logistics, including lost product and the costs associated with root cause analysis.⁶ Therefore, using AI to reduce the risk of temperature deviation not only helps with compliance measures but also reduces costs and improves the resilience of the supply chain.

Increasing regulatory requirements for end-to-end traceability are accelerating AI adoption in pharmaceutical logistics. With regulations in both the US and EU driving greater visibility at package level, the volume of supply chain data available continues to increase. Combined with sensor and transport data, this creates opportunities for AI to identify anomalies, detect emerging risks and support faster decision-making when issues arise.

As conditions continue to change, organisations are looking for more flexible ways to respond. As Justin Couch, Managing Consulting Manager at Accenture, explains “AI in supply chains is no longer about pilots or futuristic autonomy. In biopharma, where global complexity and patient impact raise the stakes, we now have the technological and governance maturity to scale augmented, human-in-the-loop decision intelligence, making resilience operational, not aspirational.”

In logistics, as in manufacturing, accountability cannot be fully delegated to algorithms. However, what AI offers is a means to surface risk earlier, prioritise intervention and reduce the workload on teams tasked with managing increasingly complex networks.

How Can AI Be Used to Scale?

What is becoming increasingly clear from recent modeling of AI-enabled biopharma operations is that the most effective applications are those embedded into existing workflows, enhancing how teams operate rather than attempting to replace them. In one supply chain copilot model, where fragmented planning, production and inventory data were brought together to support what-if analysis and planner decision-making, reported outcomes included a 2 to 3 percent reduction in supply chain costs, a 15 percent improvement in forecast accuracy and a 20 to 30 percent reduction in planning workload.⁷

Better planning capabilities through AI strengthens supply continuity, reduces the risk of shortages and supports more informed allocation decisions at a time when resilience remains a strategic priority across pharmaceutical supply chains. It also frees capacity for teams to focus their time elsewhere.

As AI adoption accelerates, the conversation across pharmaceutical supply chains is increasingly shifting from experimentation and its use in theory, to practical implementation. The focus is less on whether these technologies have a role to play and more on how and where they should be implemented to get the most benefit.

Increasingly, the emphasis is on turning large volumes of fragmented data into faster, more informed decisions that strengthen resilience and improve continuity of supply. This is rapidly becoming one of the defining themes across the sector. Rather than viewing AI as a standalone innovation initiative, supply chain leaders are beginning to position it as part of a broader transformation agenda centered on visibility, predictability and responsiveness. With the ultimate aim of ensuring patients have timely, reliable access to the medicines and treatments they need.

As supply chain leaders engage with these challenges, forums that accelerate these conversations become increasingly important. At the recent LogiPharma 2026 conference in Vienna, the emerging capability of A.I. was discussed and assessed by some of the biggest names in the business – but given its rapidly accelerating potential, insight-sharing cannot be limited to a single moment in time, but must continue throughout the year.

References

1. Mordor Intelligence. Artificial Intelligence (AI) in Pharmaceutical Market Analysis: Industry Growth, Size & Forecast Report 2031. Mordor Intelligence. Accessed May 14, 2026. https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-pharmaceutical-market
2. American Society of Health-System Pharmacists. Drug shortages statistics. ASHP. Updated 2025. Accessed May 14, 2026. https://www.ashp.org/drug-shortages/shortage-resources/drug-shortages-statistics
3. Pharmaceutical Group of the European Union. PGEU medicine shortages report 2024. PGEU. Published January 2025. Accessed May 14, 2026. https://www.pgeu.eu/wp-content/uploads/2025/01/PGEU-Medicine-Shortages-Report-2024.pdf
4. The Economist. AI is transforming the pharma industry for the better. The Economist. January 8, 2026. Accessed May 14, 2026. https://www.economist.com/leaders/2026/01/08/ai-is-transforming-the-pharma-industry-for-the-better
5. Saini JPS, Thakur A, Yadav D. AI-driven innovations in pharmaceuticals: optimizing drug discovery and industry operations. RSC Pharm. 2025;2:437-454. doi:10.1039/D4PM00323C
6. Pelican BioThermal. 2019 biopharma cold chain logistics survey. Pelican BioThermal. Published 2019. Accessed May 14, 2026. https://info.pelicanbiothermal.com/2019tcpsurveyreportpeli-lp-0
7. McKinsey & Company. Gen AI: A game changer for biopharma operations. McKinsey & Company. Published January 28, 2025. Accessed May 14, 2026. https://www.mckinsey.com/industries/life-sciences/our-insights/gen-ai-a-game-changer-for-biopharma-operations