We asked distribution leaders during a recent webinar how AI is showing up in their operations today.

The first poll showed that AI adoption is still mixed. In the second, we asked how attendees were using AI: to take or coordinate action, to summarize information, or not at all.

The clearest signal was what no one selected: “take action.”

Among those who responded, AI was being used to summarize information—not yet to drive or coordinate the next step.

That captures where frontline operations are today: AI is starting to help teams make sense of what is happening, but there is still a gap between understanding and action.

The supervisor as the human API

Here is why that matters more in a DC than almost anywhere else.

Walk into most distribution operations and the supervisor is doing a job no one wrote down. They are piecing together what happened in the WMS, what is documented in an SOP, what someone was trained on, what was observed on the floor, what showed up in performance data, and what needs to happen next.

None of those systems talk to each other. So the supervisor becomes the connective tissue. When the system doesn’t connect the work, the supervisor becomes the system — the human API between tools that were never designed to meet.

The cost is not abstract. It is hours. Searching, reconciling, following up, manually coordinating — instead of leading on the floor.

And floor leadership is where execution actually gets shaped. Every point of inventory accuracy, every service level, every safety outcome traces back to whether the right work happened the right way, consistently. If supervisors are stuck behind a screen assembling the picture, they are not out there reinforcing the standard.

Record, insight, action

For a long time, operations software was a system of record. It stored the SOP. It stored the training completion. It stored the observation. It stored the performance data. Useful, but passive.

The last several years brought systems of insight — dashboards, alerts, trend lines, recommendations. But what does a dashboard actually ask of a leader: interpret this, decide what matters, figure out who needs to act, coordinate the follow-up, and verify it happened. The work didn’t go away. It moved upstream and was renamed “analysis.”

The shift that matters now is toward systems of action — software that identifies the gap, determines the next step, coordinates the action, and tracks whether the work got done.

That is the real AI opportunity in frontline operations. Not visibility for visibility’s sake. Not another dashboard. Operators don’t need another dashboard to look at. They need a system that surfaces what needs attention, triggers the right action, and follows through until the work is closed.

What getting supervisors back to the floor looks like

When supervisors no longer have to roll up reports by hand to answer basic productivity and cost questions, that time goes back to the floor. They can watch the work as it happens, have meaningful conversations with their teams, and coach when it’s needed. Almost any of that is worth more than a supervisor behind a desk pulling data a tool could produce.

That doesn’t mean AI replaces people. In frontline operations, AI can point leaders toward what needs attention, but the coaching, the judgment calls, and the work itself still belong to people.

More data isn’t automatically better, either. Even a simple operation generates data on every transaction, and teams can end up spending more time digging into it than acting on it. The goal is to get the data to a point where a leader opens it, sees where to go, and goes to execute.

AI needs something real to work with

One last point, and it is the one most easily skipped in the rush to deploy something.

AI does not replace the operating fundamentals. It depends on them. If you don’t understand how the work is actually happening, and if your SOPs aren’t tied to execution, then there is very little meaningful signal for any system — intelligent or otherwise — to work with.

The intelligence layer is only worth something when the foundation underneath it is real: time on the floor, structured observations, and SOPs that describe what good work looks like.

Or, as Smart Access co-founder John White put it in the session, quoting one of his favorite operators:

“We don’t want our leaders to be data scientists.”

They need to be on the floor, with the right information already in hand.

Where to start

If your supervisors are the integration layer between your WMS, your LMS, your training records, and your spreadsheets, more dashboards will not help them. The question to ask of any tool, AI or otherwise, is narrower than “what can it tell me”:

Does it identify the gap, name the next step, route it to the right person, and confirm it got done?

That is the difference between visibility and execution.

Talk to our team about how Smart Access will set frontline leaders up for success.