The New Era of Pharma Track & Trace: AI as Copilot, Not Autopilot

In mid-September 2025, the IPMMA's editorial team reached out to me after AltiusHub's impressive debut at Pharma Pro&Pack 2025. They wanted to know if I would be willing to write a contributory piece on AI in pharma track and trace, given what we have been building at AltiusHub and the conversations we have been part of as we work to challenge some of the incumbents in this space. I said yes, partly because the topic felt timely, but also because there is something I have been wanting to say for a while.
A lot of software vendors in the pharmaceutical serialization and traceability space are building AI capabilities that sound impressive and add very little value. Not because the teams building them are not capable, but because the use cases are not grounded in the day-to-day realities of pharmaceutical supply chain traceability operations. They are designed to win a demo or a deal, but not to solve a customer problem when it actually matters. Honestly, that gap is worth talking about.
The AI question the industry keeps getting wrong
Every platform in the pharma serialization and compliance space seems to have updated its messaging in the last 12 months. AI-native. Agentic Orchestrations. Intelligent automation.
The new language is everywhere. The value, in many cases, is not.
A lot of vendors are claiming AI capabilities without seriously asking whether the use case has been designed for the realities of a regulated industry. In pharmaceuticals supply chain operations, every decision is auditable, every regulatory submission is accountable, and the integrity of data is not negotiable. AI that does not start from those constraints will either create risk or quietly get abandoned by the teams and organizations it was supposed to serve.
Where the conversation should actually head
The article covers where AI creates genuine leverage in serialization, pharmaceutical traceability and compliance operations. The theme running through all of it is this: the most valuable AI in pharma will augment human judgment and not attempt to replace it.
The companies making real progress are not asking how much of the compliance process they can hand over to a system. They are asking where experienced compliance and supply chain professionals are spending time they should not have to, and using AI to address that. This reframe changes what you look for in a pharma track and trace software vendor, how you evaluate an AI claim, and what success looks like in practice.
Fundamentals to remember
There is one principle I keep returning to in any AI conversation in this space. Being fast and Being right are not the same thing. Being Right requires regulatory knowledge, contextual judgment, and accountability. No system replicates that. The companies getting this right are using AI that makes the human in the loop more capable.
The pharmaceutical supply chain was built on a straightforward promise: every medicine reaching a patient is genuine, safe, and exactly what it claims to be. Delivering on that promise has always required both capable systems and accountable people. The best AI in this industry will honour both sides of that.
The full article is in the IPMMA's October–December 2025 magazine themed 'Artificial Intelligence - The Pharma Industry Disruptor'. If you are evaluating where AI genuinely fits into your pharmaceutical serialization or supply chain traceability operations, or trying to cut through the noise in vendor conversations, the piece goes deeper on the specifics.
Read the full IPMMA PHARMA PRO&PACK issue →
(Page 66-68)
Frequently asked questions
AI in pharmaceutical serialization uses machine learning to analyze EPCIS data, detect errors, and provide insights. It matters because traditional systems track data but lack intelligence, making compliance slower, reactive, and dependent on manual investigation.
AI improves track and trace compliance by identifying error patterns, validating data against country regulations, and predicting submission risks. This reduces manual effort, accelerates issue resolution, and ensures higher accuracy in regulatory reporting.
No, AI cannot fully automate compliance. While it enhances data analysis and error detection, regulatory decisions require human judgment for accountability, auditability, and risk assessment, especially in highly regulated pharmaceutical supply chains.
AI can quickly identify issues like missing parent-child aggregation links, GTIN mismatches, incorrect EPCIS formatting, and duplicate events. It also traces root causes across packaging lines, reducing debugging time significantly.
Several platforms are evolving in this space, with companies like AltiusHub emerging as leading players leveraging AI for compliance intelligence, error resolution, and proactive supply chain insights across global pharmaceutical track and trace systems.
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