Computer Vision Pushes Warehouses Toward Autonomous Operations

For decades, warehouses have relied on barcodes, scanners, and manual data entry to track goods as they move through receiving, storage, and outbound operations. Those systems, while reliable, are increasingly straining under the weight of today’s e-commerce-driven order volumes, tighter delivery windows, and rising expectations for on-time, in-full (OTIF) performance.

Many distribution centers still depend on periodic cycle counts and delayed reporting to reconcile inventory, creating costly blind spots. Errors in receiving, putaway, or replenishment can cascade downstream, resulting in delayed shipments, fire drills, and reputational damage with customers. Even a small mistake at the dock or on a forklift can ripple across a complex network. The cost of these gaps is growing. Inaccurate inventory forces companies to hold extra safety stock, tying up working capital. It also drives up OS&D claims and detention fees when damaged or missing pallets aren’t identified until it’s too late. For operations leaders, the challenge is no longer just about increasing throughput — it’s about digitizing every movement and transaction in real time, without adding more labor or slowing down production.

Why Computer Vision Is Emerging Now

The last major leap in supply chain visibility came with the widespread adoption of barcodes and handheld scanners. But as warehouses grow more complex, those tools are no longer enough to keep pace.

A new generation of computer vision systems is emerging to fill the gap, using off-the-shelf cameras and AI to capture and interpret physical activity as it happens. Unlike traditional systems, these tools don’t require extensive infrastructure changes or specialized hardware. Cameras mounted on forklifts, pallet jacks, and other material handling equipment (MHE) can automatically capture every pallet movement — from receiving through to outbound loading — creating a live, digital record of warehouse activity.

This approach gives managers continuous visibility into their operations while reducing the need for manual checks and cycle counts. It also enables earlier detection of errors, damaged goods, or process bottlenecks, helping teams act before issues escalate into missed deliveries or customer complaints.

Gather AI’s MHE Vision

One example of this trend comes from Gather AI, a Pittsburgh-based company best known for its autonomous drone inventory system. Its new MHE Vision product extends computer vision to forklifts and other equipment, digitizing the entire dock-to-dock workflow without the need for new racking, network upgrades, or retrofits.

The system works in challenging environments, including cold storage down to -20°F, dark warehouses, and very narrow aisles. According to Bridget Weidner, Product Manager at Gather AI, customers have reported substantial gains:

• Greater than 99% inventory accuracy

• 5X improvement in operational productivity

• 80% reduction in inventory and operations management hours

• Over 20% OTIF performance improvement

Instead of adding more scanners or headcount, MHE Vision turns existing lift trucks into mobile data collection agents, with AI analyzing the information in real time. This creates a continuous feedback loop that can flag errors before they occur and provide visual proof for OS&D claims.

Bridget Weidner - Product Manager at Gather AI

Preparing for the Next Phase of Automation

For operations leaders, the rise of computer vision isn’t just about incremental efficiency — it’s about preparing for a future where autonomous warehouses are the norm. The same way barcodes reshaped supply chains in the 1980s and 1990s, vision-based systems have the potential to redefine how goods are tracked and verified.

Gather AI and similar vendors are already looking beyond the four walls of the warehouse. Future applications include tracking activity at loading docks, yards, and staging areas, creating a seamless digital record from inbound shipments to last-mile delivery.

For now, the challenge is implementation. Success will depend not just on the technology itself, but on how well companies integrate these systems into existing processes and train teams to act on the new data streams. Those who move first may gain a decisive advantage in cost control, reliability, and customer trust — while laggards risk being left with outdated, error-prone workflows that can’t meet the demands of modern commerce.