By Erin McFarlane

The alerts are getting better. Many supply chain teams have made meaningful investments in predictive AI, deploying tools that flag potential disruptions days or even weeks before they escalate into crises. Port congestion, supplier delays, demand spikes. The signals are there.

And yet, the firefighting hasn’t stopped.

That paradox is worth sitting with. The challenge has shifted beyond prediction to the gap between early awareness and the ability to act quickly enough to change the outcome.

Where the Clock Starts Running Against You

In most organizations, the moment an insight surfaces is the moment a very slow internal process begins, if it is noticed and understood. An alert gets flagged. Someone pulls a report. A meeting gets scheduled. By the time the right people are aligned on what to do, the window for a proactive response has quietly closed….or, in some cases, the signal wasn’t acted on at all.

This reflects a breakdown in orchestration rather than a limitation of the underlying technology. The data did its job. The system between the data and the decision didn’t.

The time lost between insight and action tends to accumulate in predictable places: hand-offs between teams, approval chains that weren’t designed with speed in mind, and playbooks that exist in someone’s head rather than in a system. Each one is manageable on its own. Together, they can easily eat up days or weeks on a decision that needs to happen in hours.

Prediction Without Orchestration Is Just Expensive Anxiety

There’s a real risk that predictive tools, implemented in isolation, make things worse before they make them better. Teams see more of what could go wrong without having the infrastructure to respond differently. That creates pressure without release, and eventually, alert fatigue.

The operations leaders getting real value from predictive AI are the ones who treated the AI investment and the process investment as the same project. They asked: when this model flags a disruption, exactly who gets notified, what do they have authority to do, and what does “ready to act” actually look like in our organization?

Those aren’t glamorous questions. But they’re the ones that determine whether prediction translates into resilience or just into better-informed panic.

What Readiness Actually Looks Like

True readiness in a predictive supply chain depends on a set of pre-made decisions rather than visibility alone. It’s knowing in advance which suppliers you’ll call first, which lanes you can reroute, what inventory thresholds trigger which responses. It means your teams have response playbooks they’ve actually rehearsed, not just reviewed.

It also means being honest about where your organization’s real bottlenecks are. For some teams, it’s data quality upstream. For others, it’s cross-functional alignment: getting procurement, logistics, and finance to act on the same signal at the same time. Identifying that bottleneck and designing around it is the work that makes the prediction meaningful.

Over the next few years, supply chains that outperform will be defined less by model sophistication and more by their ability to close the loop, ensuring that predictions consistently trigger coordinated action rather than prolonged discussion.

We’re not far from that being standard practice. But we’re not there yet.

Erin McFarlane is VP of Operations at Fairmarkit. A former procurement executive, she has held leadership roles across financial services and technology, and works closely with supply chain teams navigating cost volatility, supplier complexity, and operational change. She is based in the greater Boston area.