How a Global Retailer Cut Scheduling Time from Three Days to 4.5 Hours

About this retailer

For a global retailer with more than 1,000 stores across 30+ countries and tens of thousands of frontline employees, building a weekly schedule shouldn’t take three days. That's the challenge one of the world's largest sporting goods retailers set out to solve

Scheduling ran on UKG’s Workforce Central (WFC), the official global scheduling tool, though some territories relied on spreadsheets to bridge gaps the platform couldn't flex to accommodate. What worked in one market often didn’t translate to another because the underlying data varied widely.

This is what changed when the retailer moved from managing workarounds to building a unified system.

The legacy stack

Time and attendance worked. Scheduling didn’t.

Time and attendance on UKG worked well, but scheduling lacked a consistent global scalable approach.

Managers in some locations were spending up to three days a week building schedules manually — time that could have gone toward coaching their teams. Without a unified demand signal, schedules were often based on estimates rather than accurate forecasts. Central teams had limited visibility into how labor was deployed across regions, and there was no consistent way to share what was working from one market to the next.

Why they chose Legion

The RFP was prompted by UKG's decision to sunset WFC, and it gave the retailer a chance to find a partner who could collaborate as closely as they needed to co-create a tool built around its stores.

Two things stood out in their evaluation.

Price-to-value.

Legion brought demand forecasting, automated scheduling, labor optimization, strategic insights, mobile management, and more together in a single platform, creating a clear business case for standardization at a global scale.

The relationship.

Rather than delivering a fixed product on a fixed roadmap, Legion worked closely with the retailer to co-develop and engineer features that fit its specific need.

For a Fortune 500 retailer operating at global scale, where vendor relationships are typically transactional, that partnership approach was a real differentiator.

Global rollout

Deployed across every region

The rollout went smoothly across the global network. According to the retailer's executive sponsor, it stood out as a genuine enterprise-wide success — deployed consistently across every region.

The impact was immediate and measurable:

• Scheduling time dropped from up to three days a week to about 4.5 hours
• Weighted forecast accuracy is above 80% globally
• More than 75% of frontline employees actively adopted the mobile app to view schedules and manage availability

Mobile adoption is particularly important. The workforce now manages schedules and availability directly through the app, reducing the admin work for managers to get their time back.

Turning data into better decisions, every week

The partnership didn’t stop at go-live.

The retailer continues to improve store-level accuracy across drivers like transactions, traffic, and product flow, while continuously refining labor optimization to produce more precise schedules.

Machine learning has long been part of the Legion WFM platform. What’s evolving now is how forecasting, scheduling, inventory, and operational data work together in more autonomous workflows using Agentic AI.

For many global retailers, store managers are still the last line of defense when something changes: a late shipment, an early promotion, a shift in foot traffic. By the time they know there’s a problem, it’s already become a scheduling problem.

The retailer’s next phase is built around changing that dynamic.

The most advanced workflow in development connects inventory allocation and delivery updates directly into workforce planning. If shipments or inventory materially shift the workload requirements, the system flags it, adjusts the forecast, and surfaces a recommended schedule change for manager approval. No manual detection required.

That’s one example of a broader direction: using agentic workflows to handle operational noise, such as scheduling smaller-format stores, monitoring data quality, analyzing labor model performance, so managers can focus on exceptions, not administration.

“Now we shift from launching to maximizing: driving forecast accuracy, schedule efficiency, and operational excellence at a global scale for our retail fleet. Right people. Right place. Right time.”

– Global Product Leader

The lesson for global operators

Most global retailers still treat workforce management as a back-office cost center. That framing is expensive, and it's one of the reasons so many AI initiatives struggle to move beyond isolated pilots.

AI in retail operations is only as effective as the data, forecasts, and execution systems underneath it. What changes the equation is when those systems stop operating in silos—when inventory receiving, scheduling, and labor decisions are connected and automated, rather than reconciled manually.

What this retailer built first was operational consistency: common forecasting logic, centralized visibility, mobile adoption, and standardized scheduling processes across regions. That created the conditions for more advanced workflows that can automate recommendations, proactively identify issues, and continuously improve labor decisions over time.

The retailers who modernize their workforce management layer now won't just run more efficient stores. They'll be in a fundamentally stronger position to operationalize AI, connecting labor, inventory, store activity, and frontline execution in ways that compound over time, rather than treating AI as another disconnected technology project with limited reach and impact. To learn more about Legion AI, visit us at legion.co.