What Is AI in Warehouse Management, Explained for Non-Tech Owners

Take your stock list onto the floor and check 100 locations by hand. On the average figure CAPS Research reported for 2024, 17 of those 100 records will be wrong. Your forklifts carry a harder number, because OSHA counts about 85 deaths and 34,900 serious injuries from forklifts every year in the United States. You knew both problems existed long before anyone sold you the letters A and I. What changed in the last three years is that ordinary software can now read your own cameras and your own records well enough to catch both, without a single robot in the building.

This post is for the owner or managing director of a warehouse or distribution business who signs the cheques and has sat through at least one AI pitch that never explained what the thing does. You’ll get a one-paragraph definition you can repeat to your bank, four examples you can picture on your own floor, a table comparing the three ways to buy, and the conditions under which AI in a warehouse fails. The argument running through the whole post is simple to state. AI in a warehouse is four jobs done by software on information you already produce, and the only decision that sits with you is which job to buy first.

What is AI in a warehouse, in plain words?

AI in warehouse management is software that has been shown thousands of examples of a thing, such as photos of pallet labels or a year of pick records, and has learned the pattern well enough to act on new cases on its own. The older software in your building follows rules somebody typed. AI software follows patterns it learned, which is why it can cope with a torn label or an order spike that no rule anticipated.

Your warehouse management system (WMS), whether that is Manhattan Associates, Blue Yonder, SAP Extended Warehouse Management, Körber or a smaller product, is a ledger with rules. It knows what you have, where each item should be, and what to do when stock drops below a minimum. It does not know that the pallet at Dock 3 arrived two cases short, because nobody told it. AI is the layer that can look at the pallet and tell it.

Every warehouse AI product you will be shown does one of four jobs, and it helps to name them before a salesperson does. It looks, it counts, it plans, or it watches. We call this the Four Jobs test at Brainy Neurals, and I ask it of every vendor on a client’s behalf before anything else. Which of the four jobs does this do, and which number on the owner’s monthly report does it move? If either half of the answer is vague, the product is not ready for your building. Across our AI in logistics work, the four jobs cover every deployment we have scoped, from a single dock camera to a building full of carts.

Each of the four jobs is described below in the order you would meet them walking a pallet from the inbound door to the outbound truck, and each one runs without a robot.

Example one: how does AI help at the receiving dock?

At the receiving dock, AI does two jobs you currently pay a clerk to do under time pressure. A camera over the dock reads every label, counts the cases on the pallet and flags crushed corners or wet cartons before the pallet leaves the door. A second piece of software reads the packing slip and the bill of lading and posts the lines into your system, so the clerk reviews exceptions instead of typing.

Receiving errors are expensive because they travel. A miscount at the dock becomes a wrong stock record, then a short pick, then a customer credit, and nobody traces it back to the pallet where it started. Vimaan, which sells camera gates for docks, reports pallet processing moving from one pallet every three to five minutes to three pallets a minute on its system. That is a vendor figure, so test it against your own dock times before you repeat it.

Two technical details matter for an owner here. The camera has to measure in real metres rather than in pixels if you want it to tell you a pallet stands 1.9 metres tall and overhangs on one side, which is why we use the Stereolabs ZED 2i stereo camera, a device that reports depth in metres. The paperwork side is an ordinary document pipeline. Our document AI work for one cross-industry client reads 50,000+ documents a month across 47 formats and cut manual review by 80%, and a packing slip is one of the simpler documents in that range. Camera and reading software run on an NVIDIA Jetson Orin box at the dock, so footage never leaves your building, the same edge AI pattern we run for tire inspection at 99.2% detection and 200+ tires an hour.

A dock camera pays back on the claims you can now prove and the short picks that stop happening, and the receiving clerk becomes the person who resolves exceptions rather than the person who creates them.

Example two: how does AI count inventory without a stock take?

AI counts inventory by photographing every rack location on a schedule, reading the labels and quantities in the images, and comparing what it saw to what your records say. Drones from Gather AI or Verity, or ground robots from Corvus Robotics and Dexory, do the flying and driving. The software does the reading, and the output is a list of locations where the record and the shelf disagree.

The baseline you are working from is poor. CAPS Research put average inventory accuracy at 83% in 2024, which means 17 locations in every 100 hold a record that is wrong, and only 69% of companies track the figure at all. Gather AI says its drones scan up to 900 pallet locations an hour. NFI, a large US third-party logistics provider, reported cutting its counting workload from 4,400 to 800 hours a year, and Barrett Distribution reported count accuracy of 99.95% with six cycle counters moved to other work. Those are vendor-published case studies, so ask for a reference site in your own sector before you believe the ratio.

What this job finds is uncomfortable for everyone involved. A warehouse that has never been counted completely learns, in its first week of drone flights, how wrong its records are. That discovery is the first return on the money, and it is why we put the Count job before the Plan job whenever the records have not been audited in the last year. If you have been pitched a digital twin of your building, our explainer on digital twins for factories and warehouses covers why the twin is only as good as the count feeding it.

Counting with cameras turns a quarterly ordeal into a nightly list of exceptions, and the list is what makes every later AI project in the building possible.

Example three: how does AI decide what your pickers do next?

AI plans picking by learning from your order history which items ship together, at what time of day and in what season, and then it does two things with that knowledge. It moves fast-moving and often-paired items to locations that cut walking, and it sequences each picker’s work so the route through the aisles is short. Nothing physical changes except where stock sits and the order on the handheld.

Picking is where the money leaves the building. René de Koster and colleagues at Erasmus University estimated order picking at up to 55% of total warehouse operating expense, and travel is the largest slice of a picker’s time, around half of it by Supply Chain Digest’s estimate. Amazon’s DeepFleet model, trained on its own warehouse data, cut travel time by about 10% for a fleet of more than a million robots. Your pickers walk
rather than roll, but the arithmetic is the same, and a 10% cut in walking on a 40-person pick team is four people’s worth of hours a shift [ILLUSTRATIVE].

This is also the job where robots enter, because once the software has worked out the pick sequence, a goodsto-person system from AutoStore or a fleet of Locus Robotics carts can carry the walking, and Walmart is rolling Symbotic case-handling robots into its distribution centres for the same reason. You do not need any of that to start. The software layer pays back on its own in buildings that are years away from robots, and our deeper piece on pick-path AI optimisation shows how the sequencing is built. For an owner’s view of whether to buy the module or build it, see build, buy, partner or wait.

Example four: how does AI keep forklifts and people apart?

AI watches forklifts through cameras, either mounted on the truck or fixed above intersections and dock edges, and it recognises people, trucks and loads in the picture in real time. When a person steps into a forklift’s path or a truck takes a blind corner too fast, the system warns the driver, logs the near miss with a video clip, and on some trucks slows the vehicle. Mirrors and painted lines never produced a record you could review on Monday morning, and cameras produce one for every event.

The numbers behind this job are the ones that keep owners awake. OSHA’s figures, as compiled by Damotech, put forklifts at about 85 deaths and 34,900 serious injuries a year in the United States, with tip-overs causing 42% of the deaths, and OSHA estimates that 70% of forklift accidents could be prevented with better training and policy. OSHA 29 CFR 1910.178 already requires operator training and daily truck inspections, and the
camera record is what proves you did them. In Zebra’s 2025 Warehousing Vision Study of more than 1,700 warehouse staff and leaders, 70% of frontline associates said they worry about injuries and 79% of leaders expect AI to improve hazard detection.

Elokon, Voxel and a crowd of camera vendors sell this job, and the 85% to 94% reductions in near misses on their sites are figures from their own installs. Treat them as the upper bound and insist on a pilot at the intersection your supervisors complain about. Our video analytics work runs the detection on an NVIDIA Jetson Orin at the camera with a YOLOv11 detector, so the alert reaches the driver in well under a second and the footage stays on site.

The same cameras answer questions after the fact, which is the part supervisors come back to after the first month. Our Intelligent NVR product lets a supervisor type plain questions across every camera in the building, such as these four.

• “Show me every forklift that entered Aisle 14 above 8 km/h this week.”
• “Find the pallet from purchase order 4471 after it left Dock 3 yesterday.”
• “Summarise all loading dock activity between 10 PM and 6 AM.”
• “Find every time someone walked under a raised load this month.”

Forklift cameras give you what the OSHA standard assumes you already have and almost no mid-size building does, a record of what happened in the lanes between incidents.

How do you buy AI for a warehouse without buying a robot?

You have three ways to put AI into a warehouse, and the right one follows from which of the four jobs you want done and how clean your records are. Your warehouse software vendor sells AI modules for the Plan and Count jobs as a subscription. Robotics and drone vendors sell the Count and Plan jobs bundled with hardware. An AI partner builds any of the four on the cameras and systems you already have, and that is the route Brainy Neurals takes.

Start in the left column if your records are clean and your WMS vendor already offers the module, because a subscription you already half-own beats a build. Move right when the job you need is Look or Watch, since no WMS module reads a camera, and insist on owning the trained models whichever column you land in.

Four steps get you from this post to a signed pilot without a robot in the quote.

1. Pick the number that hurts. Inventory accuracy, dock time per pallet, picks per labour hour or recordable incidents. Take one number from your last three monthly reports.
2. Match the number to one of the four jobs. Accuracy points to Count, dock time to Look, picks per hour to Plan, and incidents to Watch.
3. Check the data behind the job before anyone quotes you. For Look and Watch, walk the area with a phone camera at 6 AM and 6 PM and look at the light. For Count and Plan, export 200 locations or 200 orders from your WMS and time how long it takes.
4. Buy a paid pilot on one dock, one aisle or one intersection with the pass mark written down. Two to four weeks on your own floor beats any demo video, and our AI proof of concept and pilot team runs these as fixed-price engagements

What results can an owner expect, and from whom?

The results below come from published sources and from our own project work, and each one is labelled so you know which is which. Vendor case studies describe the vendor’s showcase sites. Industry studies describe the average site across a sector. Your building sits somewhere between the two, and the pilot in step four tells you where.

• 83% was the average inventory accuracy across businesses in 2024, with 69% of companies tracking it (CAPS Research via NetSuite, industry average).
• 75% fewer counting hours, from 4,400 to 800 a year, at NFI after drone counting (Gather AI case study, vendor-reported).
• 10% less travel time across a fleet of more than a million robots after Amazon trained DeepFleet on its own warehouse data (TechCrunch, July 2025, company-reported).
• 85 deaths and 34,900 serious injuries a year from forklifts in the United States, the baseline the Watch job works against (OSHA figures via Damotech, regulator data).
• 80% less manual review on 50,000+ documents a month across 47 formats (Brainy Neurals project data,first-party)

The money is moving whether or not your building does. In the 2026 MHI and Deloitte Annual Industry Report, drawn from 500+ supply chain leaders surveyed in December 2025, 71% said AI is disrupting supply chains, 48% rated the impact significant or greater, up 25 points on 2025, and 52% plan to spend over $1 million on supply chain innovation. Mordor Intelligence sizes the warehouse automation market at USD 34.17 billion in 2026, heading to USD 65.74 billion by 2031. Zebra’s study found 63% of warehouse leaders planning AI software within five years, and Modern Materials Handling puts typical warehouse turnover near 37% a year, the labour pressure behind the spend. DHL has published a full trend report on AI-driven computer vision for logistics, and the mid-size operators competing with DHL for the same customers are the readers this post is for.

When does AI in a warehouse fail?

AI in a warehouse fails in five recognisable ways, and four of them have nothing to do with the AI itself. Check each one before you sign, because every one of them costs less to fix before the pilot than after it.

• The records were wrong before the software arrived. Planning software fed by an 83% accurate stock file plans with 17% bad information. Count first, and let the count correct the file.
• The cameras are in the wrong place or the light is wrong. A dock camera that works at noon fails at 6AM in January with the door open and the sun behind the trailer. Walk the site at both ends of the shift before anyone mounts hardware.
• Nobody in the building owns the exceptions. Each of the four jobs produces a list of things for a person to check. If that list has no owner, it grows for a month, gets ignored, and the system is declared a failure by the people it was meant to help.
• You bought a robot for a data problem. A fleet of carts moving stock to the wrong locations faster is an expensive way to discover your slotting was wrong. Fix Count and Plan in software before you buy anything that rolls.
• Peak season looked nothing like the training data. A model that learned from March to September meets November with no idea what is coming. Ask every vendor how the system behaves on patterns it has never seen, and what a person does when it says so.

Our post on why warehouse AI pilots stall before production goes through the governance side of these failures, and how to choose an AI development company covers how to score the vendors who tell you none of this applies to them.

What should you do about warehouse AI this quarter?

Pick one number from your last three monthly reports that you would pay to move. Match it to one of the four jobs. If it is inventory accuracy, count first, and let the count tell you how wrong the records are before any planning software touches them. If it is incidents, put a camera on the intersection your supervisors complain about, then read a month of near-miss logs before you decide anything bigger. The first project should touch nothing that moves. Robots can follow once the data earns them, and the four examples above sit in the order the data usually earns them. If you would like a second opinion on which job to buy first, our AI in logistics team will walk your floor plan with you and tell you plainly where the money is.