The Importance of Ground Truth Sensor Data to Agentic Automation in Logistics

Before any logistics operation invests in artificial intelligence, the most important question to ask is simply: why? What are we actually trying to achieve? Why are automation and AI the right tools to achieve it? And, critically, how do they create real, measurable value?

The Value of Speed

In supply chain and logistics, speed is a competitive advantage. The ability to make faster decisions, reroute shipments in real time, and anticipate disruptions before they cascade; these capabilities translate directly into customer satisfaction, cost reduction, and margin improvement. Where automation and AI can be used to increase supply chain velocity, value is unlocked.

But there is a catch. Faster decisions do not automatically produce faster outcomes. If your AI system makes a routing decision in milliseconds but the transportation team takes hours to act on it, you have not increased velocity, you have simply moved the bottleneck downstream. Agentic AI has the potential to change this by being able to enable faster action as well as faster decisions.

Choose Your Starting Point

Once you are clear on why automation and AI add value, the next question is where. Not every logistics process is an equally good starting point. A good approach is to evaluate potential use cases along two dimensions: the value that they unlock, and the complexity required to automate them.

Look first for decisions that are high-frequency and well-defined, where the logic can be codified and the outcomes can be measured. Exception reporting and resolution is a good place to look, as most transportation exceptions are repetitive and the resolution is often a chain of calls or emails that can largely be automated.

Human oversight remains important, particularly for edge cases and unusual shipments but what constitutes an “edge case” will shrink over time as the agents become more capable, freeing up teams for more valuable, customer-facing activities.

The Data Paradox

Organizations are hearing two opposing pieces of advice on AI. The first is to start by getting their data in order; the second is to “go for it” and figure out data challenges (using automation and AI) along the way.

The solution is to go back to the choice of starting point and make data availability an additional qualifier in choosing early use cases.  Every company has enough data to get going if the right starting point is chosen.

Ground Truth Sensor Data

One of the most powerful methods to accelerate a logistics automation and AI roadmap is to look for new sources of data, particularly ground truth sensor data.

Ground truth data is data that directly represents assets, activities, or outcomes within a supply chain.  Examples include feeds from digital cameras and from Internet of Things (IoT) sensors.

Ground truth sensor data is fundamentally different from transactional data generated by systems of record such as a TMS. Sensors can generate data related to location, time, temperature, condition, and other variables in a way that is high-frequency, consistent, and granular.

Is this data perfect? No. Sensors drift, connectivity lapses, and edge cases produce anomalies. But because the data arrives in continuous streams with predictable signatures, outliers and malfunctions can be identified and filtered automatically. This results in data that can be trusted, and trusted data is the foundation of automation.

The use cases that can be built on sensor data expand as more context is added from internal systems such as TMS and ERP as well as external data such as risk or disruptive events.

From Data to Action

The logistics operations that will win using automation and AI are not necessarily those with the most sophisticated algorithms. They are the ones that have connected reliable data to executable actions across their networks. IoT data, properly integrated, provides the sensory layer that makes agentic AI possible; it provides the capability to know what is happening, and to respond to it automatically, at scale.

Start with the right questions. Prioritize by value and feasibility. Get your data strategy moving even before it is perfect. And look to ground truth sensor data from the physical world to accelerate the journey. The competitive advantage in modern logistics is not just moving goods faster, it is building a system that learns, adapts, and acts faster than any manual process ever could.

David Shillingford is president of Pegasus Analytics through which he advises investors and high growth supply chain technology companies.  He can be found at https://www.linkedin.com/in/shillingford/