As SKU counts climb and customer expectations tighten, warehouse and DC operators feel the strain of manual cycle counts, paper checks and scanning processes that don’t keep pace with daily activity. They need better data, and they need it faster than traditional tools can deliver.
That pressure has pushed data capture to the front of the automation conversation. Operators sit at different points on the adoption curve, but face the same challenge, namely the need for accurate, fast, real-time information about what’s on the shelves, what was shipped out and what went wrong.
Machine vision tools are stepping in to help fill that gap. Inventory drones and autonomous mobile robots (AMRs) gather images and location data throughout the day and turn that information into usable insights. The steady feed of images and location data helps teams catch inventory gaps and fix problems before they ripple through the operation.
“Everything that happens in the warehouse or DC flows downstream of inventory accuracy,” says Jackie Wu, CEO at Corvus Robotics. “You have to know what’s where for every order if you want to efficiently complete tasks like receiving, put-away, replenishment and so forth.”
Companies still sticking to their manual approaches risk major efficiency drains, Wu adds, and set themselves up to spend entirely too much time searching for products, dealing with mis-picks and forcing employees to run all over the facility to find what they’re looking for.
Manual processes also make quarter- and year-end cycle counts needlessly arduous and labor-intensive, Wu adds. “If the one person who knows where everything is calls in sick, where does that leave you?” he points out. “This is one of countless reasons why cycle counting is basically impossible without some type of data capture technology.”
For many operations, that data capture technology is rooted in bar code scanning—a method that’s been around for 75+ years, but still dominates in the fulfillment space. This has left the door open for makers of machine vision-enabled drones and AMRs that collect inventory data as the operation moves, versus manual snapshots that offer a narrow snapshot in time.
Replacing error prone processes
Bryan Boatner spent two decades in machine vision at Cognex, where most early work focused on manufacturing instead of warehouses. Even then he picked up on how frequently inspections broke down because people had to write down pallet numbers, judge quality by eye or follow steps that changed from one person to the next. Subjectivity, turnover and fatigue made those checks hard to repeat with the consistency that industrial processes needed.
“The issue with manual processes is simple: they’re error prone and hard to keep consistent over a full shift,” says Boatner, now chief revenue officer at Ranpak, maker of the Rabot vision-AI platform. He notes that even a well-trained operator might reach 85% to 90% accuracy on a manual task, but says variation across people and time adds up fast.
Those same weaknesses show up in warehouses and DCs, many of which still rely on people to inspect items, check labels and verify orders.
Companies may not instinctively invest in automation to handle these tasks, but Boatner says when they do, the return shows up quickly. The work sits so far upstream, for example, that consistent machine vision (versus just doing manual checks) improves accuracy and makes every downstream task “that much more reliable.”
Bar codes only go so far
Wu has seen how far warehouses can go with basic bar code scanning. He’s also seen how quickly they hit the ceiling with those solutions. He draws a straight line from today’s manual inventory checks back to the earliest record keeping. “Some of those ancient Phoenician tablets were basically inventory counts,” he says. “Paper and pencil still look a lot like that.”
Bar code scanning moved the work forward about seven decades ago, but Wu says the core challenge remains: Employees still have to climb lifts, read labels and keep counts current in environments that have grown too complex for handwritten and handheld methods. Enter machine vision, a set of camera-based tools and AI models used to capture and interpret inventory data without relying on people to scan, record or verify it.
New AI vision models can recognize parts, identify damaged pallets or flag irregularities without training on every SKU. Wu says these models learn from broad sets of warehouse images and interpret conditions that earlier tools couldn’t handle. Hardware is shifting in the same direction. For example, Corvus’ autonomous drones move through aisles with onboard vision sensors and capture images at height without lifts or fixed guidance.
“Employees don’t have to climb 40 feet in the air anymore,” Wu explains. “The drones carry the machine vision sensor up there and use AI to understand the environment.” The drones then sync those images and location points with the warehouse management system (WMS) so teams spend less time searching and more time reviewing exceptions.
Machine vision widens the lens
As machine vision becomes a core source of warehouse intelligence, the technology can capture more detail than traditional bar code reads.
Sensors, high-resolution cameras and LiDAR gather shape, volume and rack conditions along with the surrounding context. Those inputs feed digital models that refresh in real time and show how inventory and space are being used.
“You get a complete view that updates as the warehouse moves,” says Oana Jinga, co-founder and chief commercial and product officer at Dexory.
AI systems process that information and present it in dashboards that operators can act on without specialist support. Continuous updates help teams see how storage patterns shift, spot misplaced items and understand how layout decisions affect daily flow.
The technology is also moving toward more adaptive use. Machine vision and AI are beginning to test scenarios, predict disruptions and suggest adjustments earlier in the process.
“The next major shift is toward the adaptive warehouse, where machine vision, AI and autonomous agents work together to sense, decide and act in real time,” says Jinga, who adds that many operations are already moving away from static monitoring and over to more proactive optimization.
“As adoption accelerates, warehouses will evolve into fully responsive ecosystems,” she continues, “where processes update themselves dynamically, frontline teams focus on higher-value decision-making, and supply chains become more resilient from end-to-end.”
Machine vision on lift trucks
As he surveys the current warehouse environment, Joe Mirabile, vice president of operations at Gather AI, sees machine vision speeding up how operators verify inventory and location accuracy. One of the biggest gains comes from attaching cameras and sensors to forklifts so they can scan locations as they move.
“You’re seeing every move in real time, and that data is up to date,” Mirabile says. That’s a sharp contrast to counts that can take weeks and often produce stale results.
Accuracy has improved as cameras and sensors have become cheaper and more robust. Gather AI’s models read labels in poor lighting and use optical character recognition (OCR) to capture license plate numbers (LPNs) when bar codes are damaged or illegible. That helps correct WMS records and surface mistakes that build up over time.
Deployment is faster, too. Sites can be up and running quickly, and direct WMS integration turns the system into a real-time validator. Mirabile says expectations have shifted as costs have dropped. “The ROI is less than six months in a lot of situations,” he says.
The next phase extends machine vision beyond forklifts and drones. For example, Mirabile expects wearables to help capture the movements that influence inventory accuracy throughout a shift.
He also sees room for growth outside the warehouse, where the same vision tools could track inventory in yards and cargo areas that often operate with limited visibility.
Expert shopping tips
Warehouse and DC operators shopping for new data capture tools in 2026 face a crowded field, but Wu says a few simple filters can keep them from making costly mistakes. He urges buyers to start with vendors that have real deployments behind them and understand how machine vision fits into day-to-day work. Long term vendor support is also important, he notes, especially as AI models advance.
“You don’t want to buy something that’s really expensive and find out a year later that it’s obsolete,” he says.
Wu also urges buyers to separate proven tools from experiments. Automation should reduce labor, improve accuracy and show savings from day one.
“It can’t be science projects at this point,” he says. “The solution you pick has to deliver cost savings and ROI.” Finally, work with technology providers that deliver regular updates and are known for staying ahead of changes (versus falling behind them).
When evaluating solutions, companies can start with one basic question: Does the system deliver complete real-time data or just snapshots? Warehouses generate fast-changing information, says Jinga, which means accuracy, full-aisle coverage and update frequency all matter. She also stresses the importance of strong system integration.
Put simply, tools that don’t connect cleanly with a WMS or enterprise resource planning (ERP) system can create data silos and throttle ROI.
Finally, companies should also look closely at exactly what the technology produces once the scans are complete. Digital twins can help fill in the gap in this area by creating a clear, usable view of inventory instead of raw data that teams must interpret manually. Look for platforms that build those models reliably, update them as conditions change and then scale as needed.
“The bottom line is that if the solution doesn’t translate into something actionable,” says Jinga, “it won’t help you run your operation.”
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