In the modern supply chain landscape, the term 'control tower' has become ubiquitous. Most large organizations have either deployed one or are currently evaluating the technology, drawn by the promise of consolidated data from warehousing, transportation, and planning systems into a single operating view. However, a critical distinction remains largely misunderstood: visibility is not the same as control. While these platforms are excellent at highlighting that a shipment is late or an inventory level is off, they often fail to provide the logic required to rectify the problem automatically.

The maturity gap in many of these initiatives stems from a tendency to stop at the visibility stage. Organizations aggregate events and surface exceptions, but they rarely overhaul the underlying decision model. This means that even with better data, planners must still interpret alerts manually, navigate departmental silos, and engage in lengthy email chains to resolve issues. The technology exposes the exception, but the internal operating model often lacks the clear decision authority needed to act on it in real-time.

The introduction of AI into these systems raises the stakes significantly. AI can certainly improve performance by predicting delays, ranking risks, and suggesting interventions. Yet, without clear business rules, incomplete data sets, and poorly defined workflows, these AI recommendations often stall or produce noise rather than actual value. AI does not act as a magic wand that turns passive dashboards into active control systems; rather, it amplifies the necessity for explicit, codified decision logic.

A truly mature control system must do more than display information. It must detect events, assess the business impact, apply pre-defined decision rules, and route actions—whether to a human or an automated system—for immediate execution. This shift moves the focus from merely seeing what is happening to actively shaping the outcome. The goal for supply chain leaders is no longer to ask if they have a control tower, but to determine whether they have established the necessary governance and orchestration to execute decisions under constraint.

In the modern supply chain landscape, the term 'control tower' has become ubiquitous. Most large organizations have either deployed one or are currently evaluating the technology, drawn by the promise of consolidated data from warehousing, transportation, and planning systems into a single operating view. However, a critical distinction remains largely misunderstood: visibility is not the same as control. While these platforms are excellent at highlighting that a shipment is late or an inventory level is off, they often fail to provide the logic required to rectify the problem automatically.

The maturity gap in many of these initiatives stems from a tendency to stop at the visibility stage. Organizations aggregate events and surface exceptions, but they rarely overhaul the underlying decision model. This means that even with better data, planners must still interpret alerts manually, navigate departmental silos, and engage in lengthy email chains to resolve issues. The technology exposes the exception, but the internal operating model often lacks the clear decision authority needed to act on it in real-time.

The introduction of AI into these systems raises the stakes significantly. AI can certainly improve performance by predicting delays, ranking risks, and suggesting interventions. Yet, without clear business rules, incomplete data sets, and poorly defined workflows, these AI recommendations often stall or produce noise rather than actual value. AI does not act as a magic wand that turns passive dashboards into active control systems; rather, it amplifies the necessity for explicit, codified decision logic.

A truly mature control system must do more than display information. It must detect events, assess the business impact, apply pre-defined decision rules, and route actions—whether to a human or an automated system—for immediate execution. This shift moves the focus from merely seeing what is happening to actively shaping the outcome. The goal for supply chain leaders is no longer to ask if they have a control tower, but to determine whether they have established the necessary governance and orchestration to execute decisions under constraint.