Waiting for Perfect Data Is the Biggest Mistake in Pharma Supply Chains

Pharmaceutical Executive spoke with Mark Talens, CCO of PAXAFE at LogiPharma 2026 in Boston. During the conversation., the executive discussed a variety of issues and updates on modern supply chains and the most important issues impacting them in today’s world.

Talens discussed how many companies haven’t properly organized their data in order to work best with modern technology. He also discussed broader organizational gaps in supply chains, citing many companies’ lack of cross-functional generalists as a significant issue.

Talens also detailed another major issue: the balancing of business-minded SOPs with real-world operations. While many companies think ahead to build in redundancies, they aren’t as prepared as they should be for certain real-world situations.

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Pharmaceutical Executive: Where do most pharma corporations still have meaningful gaps in their supply chains?
Mark Talens: From experience, the challenge comes down first and foremost to organizational design. You may have deep domain expertise in logistics and in planning — but do you have the right next-generation expertise to connect and correlate across those domains? Do you have the generalists your organization needs? And critically, is your infrastructure actually ready to absorb these new tools?

What I mean by that is this: you can attach an AI tool to a twenty-year-old system. You see it happening everywhere — organizations experimenting with ChatGPT, Claude, Copilot. But if you have not organized your data foundation well and contextualized it first, you are going to struggle to find the benefits. And then it becomes what I call the trough of disillusionment. You say, "Well, it isn't working." And logically, it isn't working — because you need to properly prepare the environment, educate the organization, and set the stage before you can expect results.

There are real gaps here. Every company right now wants to move fast. They have a fear of missing out. But what I always recommend to our prospects and customers is this: don't move solely toward the shiny object. The shiny object will be different again in two years. We had blockchain, we had command sensors, we had control towers — and now we have AI, workflows, and agentic systems. Be ready first.

The approach we advocate is what we call organizing your data foundry first, then getting your harness in place — meaning defining the rule sets you want to work with. Only then do you start working with the advanced tools. And that does not mean it has to be a year-long project. Many toolsets, including ours at PaxSafe, can ingest data and work with it agnostically. Which brings me to another important principle: stay with agnostic toolsets. Do not attach yourself to a single brand or platform — it will limit you. Once your data is organized and your rule sets are defined, then you move to execution.

PE: How should pharma companies think about the trade off between cost and supply chain resilience?
Talens: Pharma is pharma — compliance and quality are non-negotiable, and nothing I say should suggest otherwise. That said, there is what I call the gap between the book of the business and the real world.

The pharmaceutical industry is traditionally very conservative. If you look at how lanes and logistics are qualified and how backups are structured, I know from experience that in many cases they are 80 to 90 percent overqualified. The reason is straightforward: companies want to be sure. The result is a relatively static set of SOPs and procedures that is never fully overlaid against the dynamic reality of actual operations. That gap represents significant cost — but more than cost, it represents an opportunity to become more flexible and more resilient in the supply chain.

If you are only qualified on a single lane for a given product and something disrupts that lane, you may be in trouble. But with modern toolsets, setting up an alternative lane no longer takes four months — it takes days to weeks. That is a meaningful shift.

We are also seeing movement in certain product categories toward more direct distribution models. That does not mean these are consumer products — the underlying compounds remain highly complex biomolecules, immunotherapies, and vein-to-vein cell therapies. But the logistics model is evolving.

Think about what this means at scale for an average pharma company. Sixty product families, two to three hundred SKUs, thousands of lanes, multiple manufacturing sites — that is an extraordinarily complex network. With agentic AI, you can now overlay real-time optimization across that entire network: lane optimization, backup routing, logistics alternatives, noise cancellation. All of it simultaneously, and none of it at the expense of product quality.

There is a significant opportunity here for productivity gains and faster delivery to the patient — and I think that is where it all starts to matter.

PE: What are the most important lessons learned from building CGT supply chains?
Talens: If you look at the most complex pharmaceutical-type supply chains, what you're seeing is that conditional chain of custody is no longer the exclusive domain of pharma. The adjacencies are expanding — probiotics, perishable food and beverage products, high-value supply chains that require provenance tracking and serialized product verification. The question of where a product comes from and how its integrity was maintained throughout the supply chain is becoming universal.

The world is starting to recognize that full end-to-end control and chain of custody is no longer a pharma-specific requirement — it is becoming a competitive differentiator across industries. We are seeing that in our own prospect conversations, which are no longer exclusively with pharmaceutical companies. We are hearing from consumer goods companies whose products have regulatory adjacencies — food products, cleaning products — that still carry rule sets and require conditional checks throughout the supply chain.

The difference is that it all has to work in real time. It has to be dynamic. It has to be capable of ingesting horizontal data from across the supply chain and contextualizing that data into actionable insight. That is where the real differentiation will be made.

Pharma is the obvious bridgehead for this evolution — it has the most demanding requirements and the longest track record of managing complex, regulated supply chains. But the model pharma has developed is now migrating outward into adjacent industries. It is no longer pharma's exclusive domain; it is pharma's gift to the broader supply chain world.

Pharmaceutical Exeutive: What does best-in-class supply chain data infrastructure look like today?
Mark Talens: I actually sit on two trains of thought on this one, so let me start with the more controversial position.

Stop obsessing over the quality of your data. Let me explain what I mean by that.

Your data will never be 100% clean. I have spent millions of dollars at my former companies on master data management — aggregating data, building standards, creating a single source of truth. Those projects took six, nine, twelve months. What if you simply loaded the data as-is, created context around it, and ran an agent over it? In many of the problems we need to solve, data that is 60 or 70 percent accurate is often good enough. The question you should ask yourself is this: would 100% accuracy in my data materially change the decision I am making?

I want to be clear — this is not true everywhere, and I know some people will immediately reach for the edge cases where it is not true. My point is: start with the 80% of use cases where it will work. Accept that it will not work everywhere. Put controls in place to catch the cases where it does not, and then begin targeted optimization from there. You are flipping the conventional approach on its head.

Now for the second point, because I do agree that data understanding and data completeness matter. If you start throwing everything into large datasets and running agents and LLMs over poorly structured data, you begin to lose track of what you are actually solving. I am not saying data quality is irrelevant. But I am increasingly frustrated when I go to conferences and hear companies say they need to spend two more years getting their data right before they can start. If that is your attitude, you will never be ready.

Start with a use case where you can solve a real problem with your data as it exists today. And here is a useful perspective check: if you are a $20 billion publicly traded company telling me your data is absolutely terrible, it cannot be that bad — because you are running a profitable business. You are doing something with it, even if it is inefficient. Do not get paralyzed by perfectionism and miss the opportunity to learn how to connect and contextualize your data through real use cases.

Don't treat data quality as a problem that must be solved sequentially before anything else can begin. Treat it as a parallel problem — one that often improves as a byproduct of deploying use cases and learning cycles. We have demonstrated that repeatedly. It takes effort, but the data works itself out through the process, not before it.