In Intralogistics, Plug-and-Play Has Never Really Plugged and Played
Every AI in warehousing pitch sounds the same. Plug it in. Watch your problems disappear.
Salespeople have pitched plug and play for decades across warehouse automation: material handling equipment, warehouse management systems (WMS), automated guided vehicles (AGVs), ERP systems and conveyor and sortation systems.
Well, maybe. But probably not.
Because after five decades of walking warehouse floors, I can honestly say I’ve never seen plug-and-play work exactly as promised. Every system needs two things salespeople downplay: people trained to run it, and someone who can make it talk to whatever’s already on the floor.
The same goes for AI in warehousing.
Artificial intelligence doesn’t fix broken warehouse processes. While AI-driven systems, automation and orchestration are genuinely changing warehouse operations, they only change what’s already there. Intralogistics – how goods, materials and information move inside your four walls – is what AI runs on top of.
If your warehouses have a broken layout, poor flow and bad data, intralogistics is still broken after AI shows up. You just get bad decisions at machine speed instead of human speed.
You just added another layer, more expense and got none of the promised cost savings.
What AI in Warehousing Actually Does Well
That doesn’t make artificial intelligence useless. In fact, AI can help improve operations throughout distribution centers, often in ways nobody on the floor notices directly.
Predictive maintenance: AI-powered sensors can detect a conveyor motor drawing more current than normal, or a sorter running hotter than its baseline. Maintenance can schedule the fix rather than waiting for an equipment failure.
Slotting: Sensor and picking data can show when orders have skyrocketed for SKUs stored at the back of the building, along with recommending where to move them for faster picking and packing.
Labor allocation: Labor management systems track work rates as a shift runs. AI can then recommend moving people from an overstaffed zone to an understaffed area before a backlog happens.
Order fulfillment: AI can prioritize which orders go first, group picks that share a route and reshuffle work as deadlines shift. Order fulfillment doesn’t back up at the pack station.
All four of these AI-powered “fixes” can signal process issues that require root-cause analysis. That’s especially true when AI repeatedly fixes the same problem.
Higher currents or temperatures could point to poor lubrication, misalignment or equipment overload. Companies might not be reviewing slotting arrangements often enough. Staffing plans may not reflect continually changing workloads.
Recurring packing backlogs can indicate that orders are released without considering downstream capacity. Or picking may continually produce work faster than packing can handle it.
Every one of those root causes existed before AI showed up. What changed is how fast you discover the problems. And that speed comes from AI, which watches the intralogistics system behind all four examples above.
This Is Your Intralogistics. AI Just Watches It.
Your warehouse management system acts as the central brain of your intralogistics.
Your WMS can track inventory, direct putaway and order picking and keep a real-time picture of stock levels and order status. Underneath that sits a warehouse control system or warehouse execution system, which orchestrates the equipment: conveyors, sorters and autonomous mobile robots (AMRs).
AI’s real strength shows up here, in speed.
Instead of waiting for a quarterly review, AI agents watch that data continuously. Artificial intelligence eyes every pick, every pallet, every sensor reading.
They catch waste as it happens: parts of the warehouse with too much labor and too little work. They note half-empty trucks dispatched and work released faster than the floor can absorb.
Agents can quantify the cost and flag the fix.
What does the fix look like? On a one-off basis, reassign the idle pickers to the backlog before the next shift starts. Hold a truck so it picks up a second order and leaves full instead of half-empty. Throttle a wave release so personnel on the floor can work through a queue they can actually clear and not get overwhelmed.
That’s real value. But that might not fix the root cause. AI detects but doesn’t redesign. Catching a bad layout isn’t the same as fixing one.
Automation and AI Do Not Automatically Fix Intralogistics
I’ve walked into plenty of distribution centers, run by managers who were convinced a fleet of robots or automation would save them. Most of the time, salvation didn’t arrive.
Automation and AI are tools. Intralogistics is the discipline those tools are supposed to serve.
Sometimes more automation is the right call. A high-volume operation running millions of picks a year may genuinely need to add more conveyors, sorters and robots.
But plenty of operations have a discipline problem. They don’t need to buy anything – yet. They need smarter slotting, better shelving, a schedule that doesn’t fight the building’s own layout.
Add an AI “fix” or buy automation before you’ve done that work, and you still have goods moving the wrong way, just faster. AI, robotics and orchestration software don’t ask whether your process makes sense. They execute whatever process you hand them, good or bad, at a speed no crew on the floor could ever match.
In other words, a robot doesn’t know your fast movers are slotted in the back corner. It just hauls them from the back corner, on schedule, forever.
And AI agents can’t decide your product mix changed, your building no longer fits your volume or your SKU count outgrew your storage strategy years ago. That takes a person who understands the whole operation, not just the data feed.
Hand a good process to AI, and it runs faster and cleaner than any crew could manage alone. Hand it a bad one, and it will happily automate the mess at scale, quantifying the waste in real time while nobody fixes the root cause.
Even the most automated distribution center still runs on people. Every project I’ve worked on proves it.
Robots don’t manage exceptions. They don’t redesign a process. People still do both.
Fix the Process First, Then Let AI in Warehousing Do Its Job
Artificial intelligence, automation and orchestration are doing real work in today’s warehouses. None of it does your work for you.
Fix the layout, fix the flow and train the people who run intralogistics before you spend a dollar on AI in warehousing. That gives AI the room to do what it’s actually good at: catching the motor before it fails, putting your fastest movers back within reach, moving labor to where the work is and keeping orders flowing through picking and packing.
Where is technology helping your operations – and where is it just exposing broken processes? I’d love to hear where you land. Drop me a line.
Related Reading
- Intralogistics Is Where Your Supply Chain Performs – or Not
- Odysseus and the 7 Warehouse Design Myths That Cost You Money
- Rip Van Supply Chain Wakes Up to New Execution Models
Jim Tompkins, Chairman and founder of Tompkins Ventures and Tompkins Solutions, is an international authority on designing and implementing end-to-end supply chains. Over five decades, he has designed countless industrial facilities and supply chain solutions, enhancing the growth of numerous companies. Jim earned his B.S., M.S. and Ph.D. in Industrial Engineering from Purdue University.