By Ayush Jhawar, Founder & CEO of Genefied
AI is everywhere in supply chain management these days — forecasting demand, optimising inventory, planning routes, flagging risky suppliers, managing exceptions. But here’s the catch nobody likes to talk about: AI can’t see what it was never shown. It can only work with the data it’s given, and for a lot of companies, that data has holes in it. IBM’s research puts a number on the problem — 59% of supply chain leaders say disconnected data is the single biggest barrier to running things autonomously, and 62% admit that inconsistent or biased data is actively blocking AI-driven decisions. No algorithm, however clever, can paper over that. If you don’t know where your products actually are, who’s handling them, whether a transaction is real, or how inventory is entering your network, the best AI can do is guess intelligently.
Traceability Makes AI Smarter
End-to-end traceability tackles this at the root. It gives every product a digital identity and a trail that can be audited — a QR code, a batch ID, a digital record that ties together manufacturing, dispatch, the moment a distributor receives it, warehouse handling, retail sales and, eventually, the consumer who buys it. QR-based tracking, for instance, lets brands follow their products, confirm they’re genuine, and build visibility deeper into the channel, all while plugging into the DMS, ERP and invoicing systems companies already use. But the real value here isn’t just being able to “see” the supply chain. It’s that every verified touchpoint generates clean, structured data — and that’s exactly the fuel AI needs to actually be useful.
Picture a manufacturer shipping 100,000 units out across five regions. An AI model might nail the regional demand forecast. Great. But no forecast on earth is going to tell you why 8,000 of those units have turned up outside their intended market, or why 2,500 units suddenly triggered strange activity at the retailer level, or whether that sales spike last week was real demand or someone gaming the numbers. That’s not a forecasting question — it’s an evidence question, and only traceability can answer it. Once you’ve connected the dots between product movement, location, partner behaviour and transaction history, AI stops guessing and starts spotting anomalies, flagging patterns, and telling you where to look first. That’s the difference between AI as a forecasting tool and AI as an actual operational intelligence layer.
The money angle is hard to ignore. Most supply chain headaches aren’t just “the truck was late” — they’re diversion, counterfeiting, incentive money leaking out of the system, and inventory nobody can quite account for. Building platforms around exactly these problems, pairing traceability with brand protection and channel loyalty features is a key to overcome these issues. It reflects a bigger shift happening in the industry: the job doesn’t end when goods leave the factory gate. Everything downstream — depots, distributors, dealers, retailers, even influencers, right down to the end consumer — is a source of useful signal. Capture that consistently, and suddenly brands can connect what’s physically happening to their products with what’s happening commercially.
Provenance Is What Builds Trust
There’s another layer to this: traceability gives AI something it desperately needs — provenance. An AI system crunching sales figures needs to know the difference between a genuine consumer purchase and an invoice that got generated somewhere else in the chain for entirely different reasons. An inventory model needs to know whether the stock it’s counting is actually sitting on a shelf, already promised to someone, quietly diverted elsewhere, or parked at a location nobody’s verified. Skip that context, and you get an algorithm confidently optimising for a version of reality that doesn’t exist. IBM’s research backs this up too — genuinely intelligent operations depend on data that’s timely, accurate, and connected across both the company and its partners, not just polished internally.
AI and Traceability Aren’t Rivals — They’re a Pair
So the real question isn’t AI versus traceability. It never was. It’s how you get them working together. Traceability builds the data foundation; AI supplies the thinking on top of it. One system confirms a product was made, shipped, received and sold. The other combs through millions of those events looking for patterns no human team could spot on their own. Put those two together, and the supply chain stops being something you report on after the fact and starts being something you actually monitor and act on in real time.
The Bottom Line
The supply chains that come out ahead won’t be the ones running the most AI models. They’ll be the ones with the most trustworthy data to actually feed those models. AI is genuinely good at predicting demand, suggesting next moves, and flagging risk — but it can’t reason about events it never got to see in the first place.
End-to-end traceability closes that gap, tying products, partners, transactions and locations together into a trail you can actually verify. It was never AI versus traceability. It’s traceability feeding the data, and AI turning that data into decisions worth acting on. For any company chasing a supply chain that’s resilient, transparent and genuinely intelligent, that combination beats AI on its own, every time.