FAQ: How Is AI Being Deployed in Pharma Supply Chains?

At LogiPharma USA 2026, supply chain executives described where AI is already handling work in pharma distribution, from resolving shipment exceptions to flagging demand shifts. Alongside early results came a recurring caveat that connected data has to come first and that accountability stays with people. Below are answers to the key questions about how AI is being deployed in pharma supply chains and where it is headed next.

Where is AI being deployed in pharma supply chains today?

AI's clearest gains so far are in routine work. Adopters are still testing where the technology fits, and the most value sits in the basics of supply chain, such as chasing missing shipment data and fielding after-hours calls. The tools themselves fall into three broad types. Legacy machine learning has been in use for about 20 years, generative AI lets users query data and agentic AI can take actions and recommend solutions.

How are AI agents handling shipment exceptions?

Exception management breaks into three steps. A system detects the disruption, an agent takes the first action and every step is documented as it happens. In cell and gene therapy, for example, companies expect their specialty courier to place a real-time IoT tracker on each shipment. In some cases, the courier attaches the tracker but never links its unique serial number to the shipment record, so the tracking data isn’t tied to that shipment. Logistics managers have spent a lot of time on the phone chasing couriers to make that link. An AI agent can follow an escalation protocol and place the call instead. In the words of Henry Ames, vice president of corporate strategy at Zoomlogi, the agent will “sit on hold as long as they need to to get the job done.”

How does AI get an exception to an audit-ready record?

Exception records are usually assembled after the fact from multiple portals and calls to couriers and sites, a manual process that is prone to error. Digitizing the standard operating procedure lets an agent gather that information first and log call transcripts, emails and messages while the event is still unfolding. A quality manager then reviews the compiled record and decides whether product goes into quarantine or can be released based on stability data.

How is AI being used to read demand shifts?

Consumer supply chains see point-of-sale data that shows in real time whether sales are running ahead of or behind forecast. Pharmaceutical wholesalers largely lack that signal, because they do not receive demand data directly from their customers. To close the gap, Cencora has rolled out an AI tool to a handful of buyers that compares the forecast against incoming ordering patterns. When the two diverge, the tool flags that the forecast needs correcting and explains why.

What results is AI demand sensing producing?

Across the limited set of SKUs and buyers covered so far, Cencora’s forecasting has improved by 10%, according to Matt Sample, the company’s senior vice president of manufacturer, quality and replenishment operations. That translates into either more product on the shelf or less excess inventory from overbuying. The company is also building a way for buyers to ask the system what drove a demand spike, whether a single customer in one region or a seasonal run on flu-related products. Buyers previously pieced that picture together from spreadsheets and enterprise systems.

Can AI flag shortage risk before it hits?

There were 227 active national drug shortages in the second quarter of 2026, the third consecutive quarterly increase.¹ One emerging approach to anticipating shortages pairs demand signals with knowledge graphs. These connect manufacturer information with FDA actions and with geopolitical and financial factors to surface early risk patterns. When a demand spike and a risk warning appear together, “that’s a conversation that needs to happen,” Sample says.

What needs to be in place before AI is deployed?

Clean, connected data. One quality leader at LogiPharma put AI last in the sequence, after a standard process, trusted master data and a common platform. She tied that readiness to ICH Q9(R1), which links quality risk management to product availability.²

Visibility data needs the same groundwork. AI layered on fragmented tracking will not produce the outcomes companies want. The work starts with understanding what a signal shows, what its impact is and what should happen next.

Do GDP guidelines address AI?

Not yet. Good Distribution Practice (GDP) guidelines make no mention of AI. They were modeled on manufacturing rules that assume static, validated equipment, while shipments shift with weather, customs and carrier performance.

Regulators have started on the manufacturing side. The EU’s draft GMP Annex 22, the first dedicated AI guidance for manufacturing, would apply only to static models with deterministic outputs. It states that generative AI and large language models should not be used in critical GMP applications.³ Mark Talens, chief commercial officer at PAXAFE, says companies in GxP environments want AI but remain wary of it. He expects 2027 to be the year they can adopt it with confidence while staying compliant.

What comes next for AI in pharma supply chains?

One likely next step is route and lane risk assessment, which still rests largely on historical data and audits that may be years old. Predictive models built on actual supply chain performance could increasingly drive carrier, lane and packout choices instead. Early demand-sensing tools are also set to expand beyond their first users. Adoption across the industry remains early. One forecast predicts that 60% of enterprises using supply chain management software will have adopted agentic AI features by 2030, up from 5% in 2025.⁴

References

1. American Society of Health-System Pharmacists; University of Utah Drug Information Service. National Drug Shortages: January 2001–June 2026. 2026. Accessed October 16, 2026. https://www.ashp.org/-/media/assets/drug-shortages/docs/Drug-Shortages-Report.pdf
2. International Council for Harmonisation. Q9(R1) Quality Risk Management. January 18, 2023. Accessed October 16, 2026. https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2022_1219.pdf
3. European Commission. EudraLex Volume 4, Good Manufacturing Practice Guidelines: Annex 22 – Artificial Intelligence (consultation draft). July 2025. Accessed October 19, 2026. https://health.ec.europa.eu/document/download/5f38a92d-bb8e-4264-8898-ea076e926db6_en?filename=mp_vol4_chap4_annex22_consultation_guideline_en.pdf
4. Gartner. Gartner Forecasts Supply Chain Management Software with Agentic AI Will Grow to $53 Billion in Spend by 2030. April 7, 2026. Accessed October 16, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-04-07-gartner-forecasts-supply-chain-management-software-with-agentic-ai-will-grow-to-53-billion-in-spend-by-2030