The best AI applications in supply chain may not start with technology. They start with the task to be done.Messy, irregular, or open-ended tasks might not be there yet. But data-rich, repeatable, goal-driven workflows are already proving fertile ground for early success and long-term compounding gains.
Some parts of business run on vibes. Supply chain doesn’t.
It’s full of things that creep up on you. A wrong item packed in a box. A delay you only discover when it’s already happened. It's a world where little mistakes compound into big problems. And that’s exactly what makes it a fertile ground for AI.
While much of the attention around generative AI has been focused on writing emails and polishing presentations, we’ve been more interested in invoices and containers. Not because it’s more glamorous, but because the environment is better suited to what AI actually needs.
AI doesn’t thrive on hype. It needs structure, repetition, and feedback. And supply chains happen to offer all three.
We’ve been thinking about this a lot. Not every role or workflow is ready for AI. But some stand out and they tend to share three characteristics:
When a task is frequent, structured, and tied to a tangible outcome, it becomes a prime candidate for AI support.
Freight invoice audit is dull, repetitive, and hugely important. Humans don’t like doing it, and when they do, they usually spot the obvious stuff and miss the edge cases. AI, on the other hand, is relentless. It compares every line, every charge, every contract. It never gets tired. And when trained on your real-world billing practices, it gets frighteningly accurate.
More importantly, audit has knock-on effects. It reveals patterns. Done right, it helps pinpoint recurring breakdowns and systemic inefficiencies. It becomes the operational feedback loop that other decisions can plug into.
Less frequent than audit, but no less impactful. Contract negotiation is fundamentally a data problem: historic rates, performance metrics, SLAs, and risk thresholds. This is where AI shows its value in augmenting decision-making by surfacing patterns, highlighting outliers, and even simulating "what if" scenarios. Think of it as a powerful research assistant that never sleeps.
A few areas are already showing promise, with potential to scale further:
What this all points to is a mindset shift: The best AI applications in supply chain may not start with technology. They start with the task to be done.
Messy, irregular, or open-ended tasks might not be there yet. But data-rich, repeatable, goal-driven workflows are already proving fertile ground for early success and long-term compounding gains.
This is why we keep coming back to audit and negotiation. They’re clear, measurable, and already showing results. And more importantly, they’re interconnected. Audit is informed by contract terms. Negotiations are shaped by historical audit data. The loop gets tighter. The system gets smarter.
That’s the bar. Not whether it’s exciting. But whether it works, and whether it scales.
AI won’t fix everything. It won’t eliminate chaos. And it certainly won’t make the containers move faster (not yet, anyway). But used in the right places, by the right teams, it can remove friction, reveal insight, and quietly transform the way supply chains run.
And maybe the most telling sign that it’s working? You won’t notice it. The task just gets done better, faster, and with fewer surprises.
