When ‘Good Enough’ Data Isn’t: Why AI is Failing the Supply Chain

Picture this: A global electronics manufacturer invests millions to embed artificial intelligence across its supply chain. The tools are sophisticated — automated sourcing, intelligent risk alerts, and predictive inventory availability. The hype is strong. The software is up and running. Everyone’s expecting transformation.

But nothing happens. There’s no noticeable boost in performance. No game-changing insights. Just more dashboards, more alerts and more noise. It wasn’t the AI that failed. It was the data.

The Illusion of Structure

At first glance, supply chain data often looks clean and well-organized. It lives inside enterprise systems packed with dropdowns, timestamps and seemingly complete fields. Columns align, reports run, and everything appears structured and orderly.

But once you go beneath the surface, a very different picture emerges.

Product part numbers are formatted inconsistently across teams and regions. Supplier records are duplicated, sometimes in multiple systems, sometimes within the same platform. Product lifecycle statuses are missing or inaccurate. Lead times haven’t been updated in years, and reflect long-outdated assumptions, not the realities of today’s global logistics environment.

Each department — engineering, sourcing, logistics, and compliance — operates with its own version of the truth. There’s no unified data foundation. No shared source of accuracy. And without that, the data that AI relies on isn’t just fragmented. It’s misleading.

To put it simply, applying AI to bad supply chain data is like asking GPS to guide you based on a fifty-year-old map. You’ll be pointed in a direction, but it won’t be the right one. And you won’t know until you’re too far off course to recover quickly.

AI Doesn’t Forgive. It Follows.

Artificial intelligence systems are not designed to question their inputs. They don’t stop to verify context, or cross-check for missing information unless explicitly programmed to do so. They follow instructions, consume whatever data is provided, and generate results based on that information, flawed or not.

When the inputs are messy or inconsistent, the outcomes reflect that mess. Forecasts drift off-target. Risk models flag the wrong suppliers or miss critical disruptions. Inventory shortfalls go unnoticed until they create real bottlenecks. AI is often praised for its speed and scale, but without trustworthy inputs, it simply accelerates bad decisions.

In response, companies often sink even more time and money into tweaking the models. They retrain algorithms. Reconfigure dashboards. Re-evaluate parameters. But the core issue usually isn’t buried in the machine learning. It’s hiding in the bill of materials. It’s buried in a lifecycle tag that was never updated. It’s in a supplier name spelled three different ways.

In this environment, bad data doesn’t just slow progress. It actively blocks it.

What the Best Supply Chains Do First

The most effective supply chain organizations understand this. They know that jumping into AI without first addressing data quality is like building a skyscraper on sand.

These teams don’t focus on the flashiest tools. They start with the unglamorous but critical work of cleaning, structuring, and enriching their data. They build infrastructure that consolidates information from across the supply chain into a single, unified foundation. They validate records against third-party sources to catch inconsistencies. They fill in the gaps with contextual details, things like compliance flags, pricing data, sourcing alternatives and accurate lead times, which transform raw data into actual intelligence.

Just as important, they recognize that data cleanup isn’t a one-time task; it’s an ongoing discipline. They put systems in place to keep data fresh through automated checks, real-time alerts, and even crowdsourced corrections from team members across the organization. Their dashboards reflect what’s happening now, not what was true three or six months ago.

This isn’t the kind of work that gets headlines, but it’s what enables every other initiative to succeed.

AI Comes Second

The phrase “AI readiness” is everywhere right now. But in practice, real readiness doesn’t begin with models or code. It starts with trust. Can you trust what’s in your system? Can you trust the connections between your data sources? Can you trust that the information feeding your decisions is complete, consistent, and current?

If not, no algorithm, no matter how advanced, will save you.

Instead of asking, “What can AI do for our supply chain?” more leaders should be asking, “What’s preventing us from making great decisions today?” More often than not, the answer isn’t lack of software. It’s a lack of confidence in the data.

That’s why the companies getting the most value from AI aren’t necessarily the ones with the biggest budgets or the most advanced models. They’re the ones who’ve invested first, and most deeply, in building a solid foundation. They’ve made data accuracy, quality control, and alignment a central part of their strategy, not an afterthought.

Because ultimately, the future of the supply chain is intelligent. But intelligence doesn’t start with AI. It starts with accuracy. And it starts now.

Andy Kohm is founder & CEO of SCIP.