A critical medicine, e.g. a dose of insulin, delivered too late can interrupt treatment, however accurate the logistics provider’s tracking information. My starting point for supply chain resilience is a question management must tackle: can the team still change the outcome? Visibility matters when it gives doctors, pharmacists and medical personnel time and evidence to act.
Generative AI can help teams interpret scattered information. But access to a language model does not create resilience. Start with processes, decision rights and people who respond when something goes wrong.
Make Visibility Useful
Shipment information sits across transport and warehouse management systems, temperature records and Customer correspondence. Knowing a consignment’s location does not establish whether the medicine remains usable or will arrive before hospital stock runs out.
A GenAI assistant could combine records, retrieve procedures and explain what needs attention. Material statements need sources and timestamps. Missing temperature records and conflicting updates must remain visible. If evidence is incomplete, the answer should say so.
The EU Guidelines on Good Distribution Practice provide the quality framework for healthcare logistics. AI must fit those responsibilities and controls. A new interface is no reason to weaken a safeguard.
Keep Prediction and Judgement Separate
Forecasting and explaining a forecast are different jobs. Arrival estimates, demand projections and inventory scenarios need models tested for their intended use. GenAI can interpret outputs and help users question assumptions. Fluency tells us nothing about forecast accuracy.
Take a hypothetical refrigerated shipment of insulin delayed at an airport. A transport model estimates arrival, monitoring records show temperature conditions, and inventory data indicates remaining stock cover. The assistant prepares a decision brief, highlighting gaps and feasible responses.
Logistics checks alternative capacity. Quality personnel assess any temperature excursion against approved procedures and product-specific evidence. Clinical substitution remains with the appropriate healthcare professionals.
Give Customers Something They Can Trust
The US National Institute of Standards and Technology (NIST) identifies confidently generated false content as a GenAI risk. Approved documents can help ground answers in evidence, but cannot guarantee accuracy. Test how the service handles outdated procedures, contradictory records and questions it should decline to answer.
Restrict access by Customer and user. Contracts must address data ownership, model training, retention, processing locations and subcontractors. Where personal data is processed, assess obligations under the EU General Data Protection Regulation (GDPR). For EU deployments, assess the intended use under the EU Artificial Intelligence Act (EU AI Act). Document which obligations apply, who is responsible and when compliance is required.
Record retrieved evidence, model and procedure versions, recommendations, approvals and actions. Apply validation and change control according to intended use and quality impact. Test operations when the assistant or its data feeds fail.
The first demonstration I would ask for is a difficult case with incomplete records. Show me what the assistant cannot establish, who receives the escalation and what happens next. That reveals more about readiness than a polished answer to a prepared question. Assure means checking whether the process remains dependable as data, procedures and models change.
Build a Service Around the Operational Problem
Third-party logistics providers (3PLs) coordinate carriers, warehouses and Customer interfaces. That creates opportunities for disruption briefings, document reconciliation and preparation of investigation records. The Customer should know what work is delivered, who owns it and how performance is assessed.
For a 3PL, commercial value comes from taking defined work off the Customer’s desk and doing it reliably. Agree response times, escalation routes and liability before launch. An attractive subscription price becomes less attractive if the Customer still has to reconstruct every answer.
Start with a baseline. Measure detection time, time to approved action, recovery time and investigation effort. Where measurable, track critical medicine unavailability at the receiving site. Include write-offs, delivery performance, missed exceptions, false alerts and reviewer corrections. Prioritise exceptions by treatment criticality, available stock and recovery options, rather than shipment value alone.
Count integration, assurance and review costs as well as hours saved. Separate AI effects from changes in inventory, staffing or carrier performance. Faster writing is useful, but does not prove that the supply chain recovered sooner.
Make Change Work for All Supply Chain Participants and Contributors
In transformation work, I always test implementation against four conditions: pressure for change, shared vision, a feasible plan and capability.
Pressure for change means naming the problem and its consequences. Rebuilding shipment histories by hand is a concrete starting point. A general ambition to adopt AI is not.
The shared vision must align logistics, quality, IT and Customers on the service, its limits and decision ownership. Without agreement, functions can pursue different versions of success.
A feasible plan starts with one workflow, named owners and acceptance criteria. Test historical cases, then run alongside the existing process. Expand when results justify it. Pilots alone are not an operating model.
Capability requires data stewardship, quality expertise, operational judgement and AI literacy. People need time to learn, inspect evidence and stop unsafe work. Adding responsibility without building capability leaves teams overwhelmed.
The leadership test returns to the opening question: can the team still change the outcome? GenAI earns its place when it gives teams time and evidence to act. Physical capacity, qualified partners, contingency plans and accountability remain essential. For the patient, the critical medicine must arrive in time and fit for use, so treatment can continue.
References
European Commission. Guidelines on Good Distribution Practice of medicinal products for human use. 2013/C 343/01. https://health.ec.europa.eu/medicinal-products/good-manufacturing-and-distribution-practices_en
NIST. Artificial Intelligence Risk Management Framework Generative Artificial Intelligence Profile. 2024. https://doi.org/10.6028/NIST.AI.600-1
European Union. General Data Protection Regulation. Regulation (EU) 2016/679. https://eur-lex.europa.eu/eli/reg/2016/679/oj
European Commission. EU Artificial Intelligence Act. Current framework and implementation. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
Viehmann, Stefan. “The Resilient Chain: GenAI Capabilities that 3PLs Can Monetize and Shippers Can Trust.” In Managing Modern Supply Chains: Digital Transformation, Innovation, and Resilience in a Global Economy, edited by Johannes Kern. Wiley. Publication planned for late 2026.