To offset tariff pressures, consider new labor models

Since January, the expectation of steep tariffs has formed the epicenter of U.S. retail strategy, with companies scrambling to scale back costs and reconfigure their supply chains before heavy fees were levied.

Now, many of those anticipated tariffs have gone into effect, stress-testing months of frantic planning and budget cuts. Retailers are shouldering the brunt of rising import costs, and the burden is bleeding into consumer prices, adding more risk to a market already dampened by persistent inflation and a rocky labor outlook.

In this high-cost, high-volatility environment, every hour of operation counts. Retailers must precisely balance strategy, technology and talent to make the most of each shift and maximize employee value.

Bracing for Impact

Most retailers won’t be able to sidestep tariffs entirely, but they can control costs by finding savings and efficiencies beyond the supply chain. This requires precise, strategic cuts and reallocations, as retailers need to lower costs without compromising their operational stability. Doing so may require an equilibrium of cuts and investments.

To promote long-term savings, retailers need to streamline processes and plug efficiency drains, especially where labor is concerned, given it typically accounts for around 70% of their business costs, if not more. But their employees are also their most powerful driver of business success.

With consumer sentiment low and sales expected to drop, the value of every customer that walks through the door just got higher. Having the right employees on staff at the right time is non-negotiable.

There is some good news in all this gloom: We’re in the middle of an AI revolution, and the innovations in AI for labor management are expanding retailers’ capabilities to determine the “right” employees for the job.

Numbers Game: The Crucial Role of Demand Data

An accurate demand forecast is the foundation of labor cost control. Optimizing shifts in line with projected demand will prevent overstaffing, which not only drives up labor costs, but potentially hinders employee productivity through inefficient workflows and low morale.

Conversely, accurate demand forecasting also prevents understaffing, which may look deceptively good on a balance sheet, but leads to poor service, employee burnout, and long-term financial losses.

To achieve the most accurate forecasts possible, retailers need a substantial volume of high-quality data. The ongoing digital transformation of retail has made it much easier for retailers to source historical data for demand forecasting across all customer touchpoints.

However, retailers will also need to ensure the integrity of external data, such as weather forecasts, traffic updates, local events, and economic trends. Custom-trained AI models can ensure that data is not only plentiful, but relevant. It’s also important to ensure that demand models are refreshing regularly, as situations can change throughout the day.

Automating demand forecasting will also take the onus off managers to puzzle out demand, reducing both the risk of human error and the administrative burden on managers. This way, they can focus on ensuring a smooth and productive shift, instead of getting tied up with time-consuming guesswork.

With an accurate forecast at their disposal, retailers have a clear roadmap for labor planning that maximizes savings while keeping operations running smoothly.

Evolving Labor Models in Frontline Retail

Projected demand is far from the only factor considered in the greater matrix of labor optimization. More than matching headcount to business needs and compliance, labor scheduling must be used to extract the most value out of every shift.

Advancements in AI are permitting the creation of new labor models that can help to maximize staffing efficiency. These include:

• Productivity-based: Using predetermined performance metrics, productivity-based scheduling matches highly productive workers to critical shifts, such as anticipated periods of high demand. These models can also ensure high-performing employees are staffed evenly throughout the day, driving productivity from open to close.
• Skill-based: AI models allow retailers to prioritize employees with certain skills at certain times. For example, if an employee is experienced at customer service, the model would prioritize them for shifts anticipating a high customer volume.
• Preference-based: It should go without saying that engaged employees are productive employees. Giving employees the ability to express their schedule preferences will reduce callouts and absenteeism, ensuring shifts are optimally staffed, in addition to promoting long-term satisfaction. This becomes a cost-control method by reducing the risk of turnover while improving engagement and productivity.

The most efficient possible labor plan will take into account all of the above, along with demand considerations and compliance requirements, to create schedules that are not only optimized for cost, but optimized for employee value.

However, accounting for all of these potential labor models is borderline impossible for a human manager to do alone, especially when they may already be spending hours each week on scheduling. A recent Legion survey found that over 50% of frontline managers would like to see the creation and management of schedules automated. But just 11% are actually using AI tools for it.

Tapping AI to power scheduling will not only allow businesses to take advantage of diverse labor models, but will save their managers hours of administrative labor each week, and they can redirect that time into revenue-driving work like customer service and training.

A New Reality, From C-Suite to Storefront

Everything is changing in retail. To manage rising costs, retailers are searching for new suppliers, shifting store headcounts, revising sales forecasts, altering expansion and acquisition plans, and redefining their growth trajectories for the next few years.

Employees at every level of the business, from top-level executives to the newest store associates, should be prepared for shifts in day-to-day operations. Given this, change management must be top-of-mind for retail leaders. Any shift in processes requires clear communication focused on the value it provides both the business and employees.

In the case of new labor models, changes in technology and procedures will come with an adjustment period. However, the benefits will outweigh initial hurdles, especially as AI makes onboarding and navigation easier. With a more granular, employee-centric approach to labor optimization, retailers will be better prepared to face retail’s new reality.