Automation Exhaust: The Next Generation of Business Information

Written by: Jim McCullen, Chief Technology Officer

During a recent session with our AI partner Kognitos , founder Binny Gill introduced the term "automation exhaust" as a powerful new way to think about data that emerges during AI-driven automation processes.

What is Automation Exhaust?

At its core, automation exhaust is the secondary data generated as a byproduct of automated or AI-driven workflows. It’s not always the primary output you’re seeking, but it holds untapped business value.

For example, at Century Supply Chain Solutions we use AI to convert PDF shipping documents into structured data. The immediate need may be to extract a few specific fields for audit or validation in our VIZIV® platform. But once digitized, the full set of data, the exhaust, can unlock broader business insights we hadn’t planned for.

From Hidden Thinking to Transparent Intelligence

Now, consider a process previously done manually in Excel. A person may have run multiple scenarios before finalizing a result, but their logic and interim work remained invisible, trapped in their head or overwritten in the file.

With agentic AI, the system can run through dozens of scenarios, evaluate each, and document every path it takes. That entire decision tree, the exhaust, is available as structured data. It helps the business understand not just the final outcome, but why and how that outcome was chosen.

Logistics Example: Freight Optimization

Let’s look at another logistics case. Imagine an AI agent that routes freight based on the lowest cost. The result is a shipment delivered via the most cost-effective mode.

But the exhaust? That includes all the options the AI considered and discarded; different carriers, modes, lead times, costs. Now, we can not only quantify the savings we’re delivering to our customers but also simulate future strategies. For example, we could model the cost impact of shifting 10 percent of freight to lower-carbon routes.

Real-Time Root Cause Analysis: The Next Leap

I was recently speaking with a large importer about a persistent challenge in supply chain management explaining what went wrong when service failures occur.

Today, when freight is delayed, rerouted, or otherwise disrupted, customers receive that information through analytics in our VIZIV® platform, which uses reason codes defined by our operations teams. It’s structured, reliable, and effective but it still relies on humans selecting the reason codes.

As we continue integrating agentic AI into our operations and platforms, we’re entering a future where those answers can be available in real time, without requiring human documentation.

The key to making this shift is to ensure that all the contextual data our teams currently rely on to assign reason codes is available in our platform. Once that foundation is in place, AI can:

• Continuously monitor freight movement and operational context

• Perform situational analysis as events unfold

• Automatically generate and record explanations for deviations or exceptions

When questions arise; Why was this shipment delayed? Why was it routed differently? AI agents will be able to answer instantly, with full transparency and context.

We’re not fully there yet, but we’re getting very close. And the automation exhaust generated by these systems will be the raw material for tomorrow’s intelligent, responsive supply chains.

Business Integration Exhaust

The same applies to data integration. Every company moves data between internal systems and partners. Chain.io, an innovator in this space, is pioneering the capture of business integration exhaust.

Take a purchase order interface. Its goal is to move data from System A to System B. But the activity within that interface how often a PO changes, what fields change most, how close to ship date those changes occur is rich with operational insight. Capturing that exhaust can surface patterns and opportunities for improvement that were once invisible.

Why Now?

In the past, storing this kind of data was challenging, not anymore. Tools like Snowflake, Bigeye, Elastic make it easy to capture, organize, and analyze automation exhaust at scale.

Most AI-driven automation systems are only as smart as the context you give them. The more exhaust you collect, the better they’ll get over time and the more insights you’ll unlock.

What can you start doing today to collect your automation or integration exhaust? You might not have a plan for how to use it yet but you’ll be glad you started when you do.