The Conveyor Belt Is Jamming: Why AI’s Global Free-For-All Should Alarm Supply Chain Leaders

If you’ve ever worked in operations, you know the satisfying rhythm of a conveyor belt running just right -smooth, predictable, efficient. Until one thing slips. A box misaligns, a sensor misfires, and suddenly the whole system backs up.

That’s how AI is starting to feel.

What started as a fringe innovation is now stitched into everything – our inboxes, workflows, dashboards, and devices. Gmail drafts replies before we’ve thought them. Meta decides what content we care about. Copilot finishes our thoughts before we’ve had them. And in supply chain and logistics? AI is now forecasting demand, optimising routes, managing supplier risk, even screening job candidates.

It’s fast. It’s everywhere. But is it trustworthy?

This question became more urgent as AI developers themselves called for a moratorium – a moment to take stock, ask tough questions, and put a few safety measures in place before AI advanced even further into critical systems.

But little in the way of cohesive principles and laws has eventuated so far.

We’ve seen some movement internationally, just not the kind that gives confidence.

The EU has led the charge with its Artificial Intelligence Act (AI Act): a comprehensive legal framework that regulates AI development and use within the EU, with a focus on trust, safety, and ethical considerations. It categorises AI systems based on risk and imposes mandatory requirements for developers, including those of high-risk AI. Some provisions are already in force; full enforcement is expected by 2026.

The US, on the other hand, is doing the opposite. Tucked inside the “Big Beautiful Bill” is a clause that proposes a decade-long moratorium, not on AI development, but on most state-level attempts to regulate it. In other words, it’s a green light for AI innovation with the guardrails deliberately held back.

  

So globally, AI regulation is disparate, with more holes than Swiss cheese.

We are at the end of the runway, and the truth is: we’re no closer to having the answers we need. There’s still no enforceable regulation. No global mandated standards. No agreed-upon principles for ethical AI use and no emergency stop button if things go off track.

The conveyor belt hasn’t stopped. In fact, it’s speeding up. And we’re still hoping nothing breaks.

 

Why This Should Concern Supply Chain and Logistics 

This industry runs on precision. We live and breathe process optimisation, predictive modelling, automation, and control. And AI promises to supercharge all of that. But it also introduces a new layer of fragility-one that hides behind an interface and speaks in probabilities, not accountabilities.

Let’s call it what it is:

• Opaque logic. We can’t trace how many AI systems reach their conclusions. For a sector built on audit trails, security of ownership as goods and services move through the supply chain-that’s a flashing red light.
• Flawed inferences. AI doesn’t “know” context-it guesses. That’s risky in dynamic supply chain environments where nuance matters.
• Over-reliance. When something works well, we relax. But what happens when the system misfires, and no one’s watching?

Picture this: your AI tool over-predicts freight volume and you lock in too much warehousing. Or your AI-driven supplier screening tool deprioritises a trusted partner based on incomplete data. You don’t just lose efficiency – you lose trust, money, and relationships.

These aren’t hypotheticals. They’re operational risks. Financial liabilities. Reputational hazards. And they’re already happening.

This isn’t about being anti-tech. It’s about being pro-responsibility.

 

What Needs to Happen Before the Line Breaks

No one’s saying we put the AI genie back in the bottle. But blind adoption? That’s just bad business.

If any industry knows how to manage complex systems with tight tolerances, it’s this one. We should be leading the conversation on what responsible AI actually looks like in practice.

Here’s where to start:

• Sector-specific standards. One-size-fits-all frameworks won’t cut it for freight variability, port disruptions, or demand fluctuations.
• Transparency. If AI is making decisions, we deserve to know how – and where it can go wrong.
• Human oversight. AI can assist. It cannot be the final voice – especially where safety, compliance, or strategic relationships are concerned.
• Contingency protocols. What’s the playbook when AI fails? If you don’t have one, that’s your first task.

AI is not inherently good or bad; it’s powerful. But power without principle? That’s a system jam waiting to happen.

As leaders in logistics and supply chain, we don’t just manage flow, we manage risk. So let’s act like it.

Let’s lead the conversation to build the right frameworks, ask the hard questions, and make sure we’re not outsourcing our judgement to an algorithm or machine that doesn’t understand the stakes.

Because when the conveyor belt breaks, it’s not the AI that wears the cost – it’s us.

 

Sue Tomic

SCLAA Chair  |  Board Advisor – Institute of Transport & Logistics Studies, University of Sydney Business School

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