Your warehouse labor plan looked solid the night before. Waves were released, dock doors were assigned and the inbound schedule was 100% doable. The day was balanced on the screen until Murphy’s Law reared its ugly head. The plan was already off the rails by 9 a.m., with no promises of righting itself without a lot of manual intervention and scrambling.
This is just par for the course in busy fulfillment operations, where suppliers miss pickup windows, order volumes spike on a handful of fast-moving SKUs and key associates call out sick. Whatever the trigger was, the plan that looked airtight at 6 p.m. the day before starts to fray.
These aren’t new problems by any means. Warehouse managers have dealt with shifting volumes, late trucks and labor gaps for decades. What’s changed is the speed and scale. Order profiles turn faster, customer expectations change daily and SKU proliferation is real. There’s less room to absorb a misstep, and decisions have to be made faster than ever.
Traditional supply chain software generates structured operating plans. It ingests orders, inventory data and transportation schedules and translates that information into waves, dock assignments and labor allocations. Those core systems still form the backbone of daily execution in most warehouses. But once the shift begins and conditions start to change, even well-built plans require adjustment.
“These software systems give you a plan, and a plan is great,” says Howard Turner, director of supply chain execution systems at St. Onge Co. “But unfortunately, plans have to change throughout the day because you’re dealing with real-life, real-time conditions.”
That need for constant adjustment is driving the next phase of supply chain software. In response, vendors are embedding artificial intelligence capabilities into established WMS and TMS platforms to support shifting plans. So instead of stopping at wave creation and labor allocation, the software monitors activity throughout the day, detects imbalances and recommends changes.
Turner refers to this as “intraday planning,” or the ability to evaluate workload, labor availability and order priorities in real time and make targeted adjustments before minor disruptions turn into larger operational issues. Here are five practical ways AI is helping managers make better workday decisions in fulfillment environments that never sit still:
1. Use real-time visibility that keeps the machine humming.
Vision and perception AI uses cameras and sensors to monitor activity across picking, sortation and dock areas in real time. According to Dematic, these systems can flag misaligned totes, stalled conveyors, misplaced inventory or emerging congestion before they drag down throughput. Instead of discovering issues after performance dips, managers get early alerts and can step in quickly. In more automated facilities, this added visibility reduces downtime, improves safety and keeps shifts on track.
2. Get answers in seconds, and without digging through menus and screens
On the warehouse floor, generative AI is changing how managers interact with warehouse systems during a live shift. Instead of navigating layers of menus or relying on keyword searches, users can ask direct questions in natural language and receive immediate guidance. “When I’m describing this to clients, I use the ChatGPT-like description,” Turner says. “You can use natural language to ask a question, and the system understands context and responds without needing to hit certain keywords.” Having faster access to information helps managers make decisions quickly, and without losing the day’s momentum.
3. Adjust slotting decisions as the intraday playbook changes
AI-driven slotting tools analyze historical and live picking data to identify patterns that impact performance. Oracle says machine learning models can flag storage locations that consistently miss targets or recommend moving high-demand items closer to packing and shipping areas. Rather than waiting for a quarterly slotting review, managers can make targeted changes as order profiles evolve. The payoff? Shorter travel paths, smoother picking flow and decisions that are actually grounded in current demand, and not guesswork.
4. Use AI agents to monitor specific operational needs
Some software vendors are introducing AI agents that can monitor defined operational conditions throughout the shift. Instead of waiting for reports, these agents can be configured to specific needs, such as workload balancing or shipment monitoring. “We’re seeing a lot of vendors invest R&D dollars into deploying agents,” Turner says, describing an AI framework where you can “define a specific type of need” and monitor it during the workday.
5. Flag issues before they snowball
These models analyze historical and real-time warehouse data to identify patterns that impact performance. They can pinpoint storage locations that routinely miss targets, predict when replenishment should occur and detect congestion zones as they form. Infor says AI can also flag anomalies in inventory counts or movement that signal potential errors. This means earlier warnings and better timing, allowing small course corrections before minor issues disrupt a whole shift.
