Like nearly every aspect of modern industry and business, artificial intelligence (AI) is coming to the life sciences supply chain. Numerous general-purpose logistics and supply chain software providers have made forays into the effort, and many of them have been using more traditional machine learning tools to streamline tasks like scheduling. However, the requirements of managing a life sciences supply chain — specifically, the process of distributing and delivering commercial or clinical biopharma products — have numerous and demanding rules unique to the industry. Even more of these rules are required for cellular and genetic therapies — the hottest area of biopharma development today.
Many of these unique requirements were codified in the mid-2010s as Good Distribution Practices (GDPs), modeled more or less after Good Manufacturing Practices and related guidelines.1,2 GDPs call on manufacturers and distributors (and, by extension, any logistics firm used by the first two) to ensure protection of drug products in storage or transit. Requirements include the following:
- Equipment, vehicles and facilities are validated, monitored and documented for appropriate performance.
- Operational details — transportation modes and schedules, environmental monitoring (including the temperature of packages in transit) and the like — are documented, along with procedures for addressing noncompliant conditions (exceptions).
- Personnel are trained appropriately, and a responsible person is designated to review and sign off on operations and workplace standards.
- Systems are in place for ongoing quality management, including root-cause investigations of exceptions, corrective actions and predictive/proactive analysis.
The risks of GDP noncompliance are obvious — patient safety above all else — and can be as serious as products that were adulterated in manufacturing. In practice, pharma manufacturers have internal supply chain management teams as well as quality management teams and, in turn, set specific contract language with logistics providers, wholesalers and others that deliver products to final destinations, such as hospitals, pharmacies or even a patient’s home.
The logistics challenges of today’s pharmaceutical distribution are especially high for specialty products. At the same time, supply chain processes have become complex, involving handoffs among air carriers, ground transportation, secondary packaging providers and others. Compliant distribution also involves tracking the unique identifiers of each package and verifying their delivery at each handoff.
Logistics and distribution companies have relied for several years now on information technology (IT) systems to monitor operations (often in real time), collect data, address potential or actual errors (such as a missed handoff) and analyze where operations can be improved. The common metaphor for this function is the distribution “control tower.” Even the best of these control tower systems has limitations, including the following:
- Complete data aggregation is an ever-moving target as vendors change, operational conditions (such as weather) vary and alerts are overlooked.
- Understanding and reacting to an operational failing is time-consuming and inefficient; the more often it occurs, the more staff is needed to address the problem.
- Most exceptions issues are handled reactively, i.e., a problem occurs and a solution must be found in a timely manner. Proactive management is a challenge.
AI Points the Way
The newest forms of AI being developed today have two important characteristics: the use of large language models, which enable a higher degree of precision and accuracy; and agentic AI, which has a degree of autonomy to enable rapid decision-making. Until recently, AI applied to logistics problems simply represented an overlayer on the existing system, which was built to aggregate data from carrier portals, sensor platforms and enterprise resource planning systems into a single screen. That is useful. But aggregation and action are two different things. A consolidated view of a flight delay still requires a human to collect relevant information related to possible options; weigh those options based on risk, time and cost; and then decide what to do. And once a decision has been made, they still need to make the call, send the email and log the outcome.
A related issue is what might be called “alert fatigue.” If a logistics system flags every exception, operators can be overwhelmed. The ability to prioritize exceptions goes from “nice to have” to “must have.” Some existing systems enable operators to predetermine some priorities, but this can be a hit-or-miss, inflexible process.
Now, newly evolved “native-layer” AI can work directly with large datasets while being able to perceive issues and act on them. Zoomlogi, equipped with native-layer AI and a set of proprietary algorithms, is one of the companies pursuing life sciences logistics applications.
So, what does this evolving new technology bring to the table? At an initial level, the AI system should monitor operations continuously and prioritize exceptions as they occur. A runway delay is one level of exception; another is the need to reroute a delivery. At the next level, communications need to be routed to appropriate parties: the truck driver, the logistics provider, the client at the receiving end. The AI system automatically follows instructions in the standard operating procedures (SOPs) document to determine which parties need an email notification, which need a text alert and which need an AI-generated voice response. At yet another level, all these actions, along with the decision outcomes, need to be logged back to an auditable system that meets regulatory standards.
AI-native platforms take this further by continuously monitoring upstream risk signals, such as weather events, labor actions and carrier operational status, to identify at-risk shipments and trigger dynamic rerouting recommendations even before an exception occurs. Recommendations could be based on several different weighted factors, including time in transit, cost, number of handoffs, perceived future risk of delay, compliance with SOPs, capabilities of service providers and even historical performance.
Even before a shipment begins, AI-native technology can be used to translate SOPs directly into automated sequences that escalate or execute without human intervention. The digitization of defined and approved workflows enables the execution of SOPs with full auditability. For example, if a flight delay alert fires with less than four hours remaining in the product’s stability window, the system automatically calls the courier’s operations line using the AI voice agent; simultaneously notifies the receiving site; logs the alert and all subsequent communications in the chain of custody; and escalates to the on-call coordinator only if the courier cannot commit to a resolution within 30 minutes.
Zero-Touch Resolution
The traditional metric for measuring logistics performance is on-time delivery (OTD). Importantly, this is a lagging metric, measurable only in retrospect. The new native-AI capabilities create a new metric for analyzing supply chain operations: zero-touch resolution rate, which is the percentage of shipment exceptions that are identified, actioned, resolved and documented without requiring any human intervention. The higher the zero-touch resolution rate, the more your logistics team is freed to address significant exceptions and, generally, the faster exceptions are resolved satisfactorily. Measuring zero-touch resolutions reveals what is going on with OTD, whether or not on-time performance was achieved.
Will AI In the Pharma Supply Chain be Regulated?
On its face, Good Distribution Practice (GDP) and artificial intelligence (AI) are at cross-purposes: The former seeks to verify steps taken in managing a shipment via human intervention, whereas the latter seeks to automate many of these steps. At present, there is no mention of AI in pharma distribution or logistics standards, including GDP and ISO 21973, the various Parenteral Drug Association (PDA) Technical Reports, US Pharmacopeia and others. There are some standards and guidelines issued by the FDA and the European Medicines Agency, but they are more focused on how drug research and manufacturing should be conducted.
A key concept in Good Manufacturing Practice and related guidance is that systems are essentially static and deterministic. For example, a piece of manufacturing equipment goes through a four-step process of design qualification, installation qualification, operational qualification and performance qualification, and thereafter is monitored to ensure that its operation is stable (i.e., void of drift). A typical pharma shipment, on the other hand, is essentially dynamic and nondeterministic: Weather, traffic, customs behavior and carrier performance are all variable, even from shipment to shipment.
Thus, GDP incorporates risk analysis of operations, enabling decisions to be made based on these and other variables. There is language in GDP — and some more static regulatory situations — that call for evaluating exceptions or out-of-compliance situations based on the potential risk to a delivery. A good example of this is the defined stability profile of a temperature-controlled drug. It can be out of compliance for a predetermined, limited amount of time before its efficacy is affected.
In fact, the earlier days of how the pharma cold chain was managed could be a good example of how the industry can go forward. Prior to industry GDP or PDA standards, the industry depended on the “tribal knowledge” of practitioners in the field.3 Those experts gathered in volunteer groups and hashed out recommendations that eventually became industry standards.
Once a system is capable of performing zero-touch resolution, other quality metrics also become possible, including the following:
- Time to resolution by exception type: For exceptions needing human intervention, how long from alert to close? This exposes where bottlenecks lie: carrier communication, quality assurance documentation or recipient coordination. AI-assisted resolution should compress this even where judgment is required: AI handles communication and presents a risk-based assessment of options, while the human decides.
- Coordinator hours per exception: How many hours does your team spend per exception, fully loaded — response, carrier and recipient communications, analysis, documentation, follow-up? Most teams can’t say, because the work is scattered across email, phone and spreadsheets. If you can’t measure cost per exception, you can’t tell whether a platform is improving it.
- Chain-of-custody completeness rate: For regulated shipments, what percentage have a complete, auditable record of every event — carrier updates, temperature readings, site and recipient communications, documented outcomes? An incomplete chain of custody is both a compliance risk and a service risk, and signals that your tools aren’t capturing the full record automatically.
In actuality, these three metrics can be calculated from current (non-AI-related) operations today, provided that an existing system collects sufficient data. Understanding them is a revelation itself and sets a minimum standard for how a native-AI system can improve overall performance. Current clients of Zoomlogi have achieved zero-touch resolution rates upward of 70% (meaning 30% of exceptions required human intervention) and a comparable reduction in time spent managing exceptions. Additionally, after-the-fact quality reviews have been reduced from days to hours.
When matching native-AI potential against GDP standards, there are obvious performance enhancements: Auditable documentation is generated as a matter of routine; corrective actions for exceptions can be compiled and analyzed; and proactive risk management is realized. Each company employing native-AI technology will need to evaluate the role and responsibilities of the responsible person; this GDP requirement doesn’t go away, but the role will be redefined by experience.
What’s Next?
Most of the preceding discussion has to do with ongoing management of existing supply chains, from manufacturer to distributor or from distributor to pharmacy or hospital. For specialty pharmacies, the chain from pharmacy to patient is also present — and affords the opportunity for patients to interact with the pharmacy that supplies them. However, there is a broader perspective to consider for the near future. Consider the following:
- Redesigning a supply chain for better performance, based on root-cause analysis of operational exceptions. The same thought process can go into designing an optimal supply chain in the first place.
- Creating — not just following — SOPs. SOPs can be voluminous documents that are carefully worded to form the basis of contracts between suppliers and logistics providers. Already, it is possible to use no-code IT tools to operationalize SOPs and form the operating standards of a supply chain. In the near future, it will be possible to build those SOPs using the language-generating power of AI.
- Taking the downstream processes of delivering pharmaceuticals to clients and applying them to the upstream processes of identifying, tracking and managing inbound supplier networks for manufacturers. In the current, postpandemic environment of changing trading patterns, analyzing supplier networks is already a critical task.
Pharma supply chain managers are under increasing stress driven by the growth of specialty pharmaceuticals that require a new paradigm for distribution, the growth of overall pharmaceutical consumption and the increasingly globalized nature of the industry. Resources like native-AI logistics platforms are becoming a necessary asset. In the same way an IT manager would not operate a corporate network without appropriate cybersecurity software, a supply chain manager will ultimately find that a native-AI operating system is becoming a critical component of the overall network management of a supply chain.
Henry Ames is vice president of corporate strategy at Zoomlogi.
References
- European Commission. Guidelines of 5 November 2013 on Good Distribution Practice of medicinal products for human use. Official Journal of the European Union. November 23, 2013. Accessed September 8, 2026. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52013XC1123(01)&from=EN
- US Code of Federal Regulations. Title 21, chapter I, subchapter C, part 211: Current Good Manufacturing Practice for Finished Pharmaceuticals. Electronic Code of Federal Regulations. Accessed September 8, 2026. https://www.ecfr.gov/current/title-21/chapter-I/subchapter-C/part-211
- A conversation with Rafik Bishara. Pharmaceutical Commerce. September 3, 2018. Accessed September 8, 2026. https://www.pharmaceuticalcommerce.com/view/a-conversation-with-rafik-bishara
