What do warehouse leaders actually need from AI? Better labor decisions, lower cost, stronger visibility, and cleaner data that improves execution.
Article written by
Alex Rhea
There is no shortage of AI talk in supply chain right now. Inside the warehouse, though, leaders are not asking for more hype, more dashboards, or another layer of technology that looks good in a demo and creates more work on the floor.
They are trying to improve the decisions that drive labor cost and execution every day.
That usually comes down to three things:
making better labor moves during the shift
reducing and understanding indirect time
improving labor planning before work begins
That is why the latest market research is useful. Inbound Logistics' 2026 logistics IT survey shows that cost reduction remains the top customer challenge, while visibility and data management continue to rise in importance. AI enablement is climbing fast too, but the more important takeaway is that companies are not pursuing AI as a standalone goal. They are looking for better ways to solve the same execution problems they have been fighting for years.
For warehouse leaders, that distinction matters. The opportunity is not to “do AI.” The opportunity is to make better decisions, faster, with more confidence.
The warehouse pressure is still operational, not theoretical
The outside story may be about AI acceleration, but the warehouse story is still about execution under pressure.
Order profiles keep getting more complex. Service expectations keep rising. Labor is still expensive, harder to retain, and harder to deploy well across a shift. At the same time, leaders are expected to control cost, protect service levels, and explain performance with more precision than ever.
That picture shows up across recent research.
Inbound Logistics reports growing demand for warehouse technology that supports evolving workflows and data-driven decision-making. Zebra's 2025 warehousing study adds more detail from the floor: many associates say they spend too much time on tasks that could be automated, many report a shortage of qualified staff, and warehouse leaders still struggle to consistently hit fill-rate and order-preparation expectations. WERC's 2025 DC Measures survey points in the same direction, with ongoing focus on inventory management, productivity reporting, labor management, and engineered labor standards.
That is not a search for novelty. It is a search for control.
More specifically, it is a search for better operating decisions.
The real value is in better labor decisions
For most warehouse leaders, the biggest AI opportunity is not abstract optimization. It is better labor decisions in the moments that matter.
Can we move people sooner when one department starts to slip?
Can we spot when indirect time is building before it becomes a cost problem?
Can we plan labor more accurately before the shift, instead of spending the day recovering from a weak starting point?
Those are the decisions that shape cost, throughput, and service.
That is also why this conversation belongs in the warehouse, not just in IT or strategy meetings. If AI cannot help supervisors, managers, and planners make better labor decisions, it is not solving the problem that matters most.
Cost reduction is a labor-move problem, an indirect-time problem, and a planning problem
When warehouse leaders talk about reducing cost, they are usually not talking about one big lever. They are talking about the small misses that stack up over the course of a day.
A team stays too long in one area while another falls behind. Supervisors react late because performance issues are buried in yesterday's reports. Indirect time grows because people are waiting, walking, covering for gaps, or moving without a clear plan. Overtime rises because the building never recovers from early misses. Productivity gaps get explained away because no one can quickly separate a labor issue from a process issue.
This is where AI can help, but only when it is tied to real operating decisions.
Useful warehouse AI should help leaders understand what is happening, why it is happening, and what move to make next. It should make it easier to rebalance labor mid-shift, see where indirect time is creeping up, and identify whether the real problem started with the labor plan itself.
That is also why engineered labor standards matter here. WERC's survey continues to highlight investment interest in integrated labor management with engineered labor standards for a reason: leaders need a better baseline for what good looks like. Without that, labor planning is softer, labor moves are less precise, and indirect time is harder to explain. With it, teams can see where performance is off plan and respond faster.
Visibility only matters if it improves decisions
Visibility has been a supply chain priority for years, but the definition is changing.
Warehouse leaders do not need visibility as a passive reporting function. They need visibility that helps them act before the shift is lost.
That means knowing:
which departments are falling behind right now
where labor is over-allocated or under-allocated
how much time is being lost to indirect work
which tasks are driving missed goals
whether the building is executing against the labor plan or drifting away from it
UPS' 2026 supply chain outlook makes the broader point clearly: most executives say visibility is vital, yet fewer than a third believe they have really achieved it. The problem is not a lack of data. It is the inability to connect data across systems and turn it into timely decisions.
Inside the warehouse, that gap shows up every day. Teams may have WMS data, labor data, operational reports, and local workarounds, but still lack a usable operating picture. A supervisor might know the building is behind, but not where the best labor move is. A manager might know labor cost is high, but not whether the main driver is poor labor planning, rising indirect time, or a department that was left unsupported for too long.
That is not a reporting problem. It is a decision problem.
Data management is what makes better decisions possible
This is where many AI conversations go sideways.
Leaders hear that AI can optimize, predict, recommend, and automate. All of that may be true. But if warehouse data is fragmented, inconsistent, delayed, or hard to interpret, AI will only amplify the confusion.
The Inbound Logistics survey makes this point directly. AI adoption is rising, but so is the importance of data management and security. In a warehouse context, that is exactly right. The quality of the recommendation depends on the quality of the operating context behind it.
If labor data does not line up with task data, if standards are weak, if goals are unclear, or if the building relies on manual interpretation to understand performance, then AI becomes another layer of output that someone still has to decode.
The better approach is to start with the decisions warehouse teams need to make, then apply AI where it can compress analysis time, reduce manual effort, and improve action.
Better data discipline comes first. Better decisions come second. Better AI outcomes follow.
What practical warehouse AI should actually do
For most operations teams, the most valuable AI use cases are not flashy. They are practical.
They help teams analyze data faster. They help leaders make better labor moves during the shift. They help teams understand where indirect time is coming from and what to do about it. They help labor planners build stronger plans before the shift starts. They help managers create labor standards faster and go deeper on misses, bottlenecks, and performance variance.
That is the kind of work warehouse leaders will actually value because it connects directly to cost, service, and accountability.
It also aligns with how many operations teams want technology to behave. Zebra's research shows strong support for technology that augments workers rather than simply replacing them. In the warehouse, that usually means helping people make faster, better decisions with less wasted effort.
The goal is not to remove human judgment from the floor. The goal is to give supervisors, managers, and planners better leverage.
Where Takt fits
Takt is built around that practical view of warehouse execution.
Instead of treating AI as the product, Takt applies AI to the operational decisions warehouse teams are already trying to improve.
Customers are using Takt's AI capabilities to:
analyze warehouse performance data faster
make better labor moves during the shift
understand and reduce indirect time
improve labor planning before the shift starts
build labor standards more efficiently
go deeper on operational data to understand variance, bottlenecks, and missed goals
That matters because warehouse teams do not need more disconnected insight. They need a system that helps them connect labor, execution, and performance in a way they can use while work is still in motion.
When AI is applied in that context, it becomes much more valuable. It can compress the time between signal and action, help leaders understand what changed, surface better next moves, and make operational analysis less manual and less dependent on a few experts pulling reports after the fact.
That is a much stronger position than simply adding an AI label to an existing system.
Five questions warehouse leaders should ask before investing in AI
If you are evaluating AI in the warehouse, the best starting point is not the feature list. It is the operating decision.
Ask:
Will this help us make better labor moves during the shift?
Will it help us understand and reduce indirect time?
Will it improve labor planning before the shift starts?
Does it work with the reality of our warehouse data, not an ideal version of it?
Can we connect its output to measurable operational outcomes?
If the answer to those questions is unclear, the issue usually is not the model. It is the use case.
The real opportunity
The warehouse does not need AI for its own sake. It needs better execution.
That means better labor moves, less uncontrolled indirect time, stronger labor planning, better in-shift visibility, and faster decisions that improve throughput and cost performance at the same time. AI can absolutely help with that, but only when it is grounded in the operating realities of the building and tied to decisions teams actually need to make.
That is the real opportunity for warehouse leaders in 2026: not to chase the newest buzzword, but to build a better decision-making layer for the shift.
If that is the goal, AI stops being the story by itself. It becomes part of how better warehouse teams work.
Article written by
Alex Rhea