How warehouse and distribution operations are changing frontline workers’ perceptions on using AI

The conversation around artificial intelligence (AI) in warehouse and distribution operations has a perception challenge. It is dominated by two competing camps: executives who are sold on the technology and warehouse associates, floor leads, and distribution center (DC) managers who are wary of it. What gets lost between those two positions is the thing that actually determines whether an AI deployment succeeds or fails: the experience of the warehouse team who now need to use it every day. The numbers tell a similar story. A 2026 PwC survey found that 89% of operations and supply chain leaders said technology investments had not fully delivered their expected results, with user adoption among the leading challenges. Why warehouse workers push back Understanding resistance starts with understanding what workers are actually hearing before a new AI system arrives on the warehouse floor. A 2026 Qualtrics report found that 39% of frontline workers were excited or hopeful about AI changing their work, compared with 53% of knowledge workers. By the time a new system is introduced, many frontline workers may already have formed strong opinions about what it means for them. The fear is compounded by how most deployment decisions are made. A 2026 study from PwC and the Manufacturing Institute found that 45% of leaders cited the exclusion of frontline leaders from AI design and rollout as a significant contributor to unsuccessful AI initiatives. When workers have little visibility into why a tool was chosen, what problem it is meant to solve, or how their feedback could shape how it gets used; the result is a deployment that begins with a credibility deficit before a single employee has logged in. Those concerns are not irrational responses. They reflect a reasonable reaction to being asked to adopt something that was never designed around how they actually work. The adoption issue is an organizational problem Framing AI resistance as a worker problem misdiagnoses the situation. When adoption stalls, it is almost always because the organization failed to create the conditions for success, not because employees are incapable or unwilling. The most common failure pattern looks like this: the technology is implemented correctly, training covers the features, the rollout is announced, and then nothing changes on the floor. Associates keep working their pick paths the way they always have, dispatchers keep building routes by feel, and the AI system becomes expensive infrastructure running in the background while real work happens somewhere else. The 2026 PwC and Manufacturing Institute study also found that 40% of leaders cited insufficient training as a common concern among frontline employees. What a better rollout looks like on the warehouse floor Organizations that succeed with frontline AI adoption share a common characteristic: They treat deployment as a workforce initiative supported by technology, not a technology initiative imposed on the workforce. That starts with involvement before launch. Workers who participate in pilots, feedback sessions, or early testing develop a sense of ownership over the outcome. They are more likely to identify real problems, suggest useful adjustments, and advocate for the tool among peers. The transition from skeptic to internal champion almost always begins with the feeling of being heard. It also requires that the AI system deliver immediate, practical value at the individual worker level, not just at the organizational reporting level. If the only visible beneficiaries of a new AI system are managers reviewing dashboards, frontline workers have little reason to trust it. When a system helps an associate optimize a pick path, locate a mislabeled pallet, or walk a new seasonal hire through an unfamiliar putaway process, and enables a shift supervisor to identify a labor shortfall before the outbound dock falls behind on volume, the value becomes personal and concrete to the people doing the physical work. For example, a global retailer introduced an AI assistant into its warehouse operations after implementing a warehouse management system (WMS) across four distribution centers. The AI assistant gave warehouse managers and logistics operators a natural-language way to retrieve operational information, create support tickets, access dashboards, and resolve issues. The implementation resulted in increased user autonomy, faster issue resolution, accelerated decision-making, and more streamlined workflows. This is where design philosophy matters enormously. Systems built in isolation from the dock, the line, or the cab tend to optimize for capabilities that look impressive in a vendor demo but do not map to how work gets done under real throughput pressure. Systems developed in close partnership with the workers who will use them solve the problems those workers face, which makes adoption feel less like change management and more like common sense. Communication is the third piece. Workers need honest answers to the questions they are asking: Will this change my hours or my role on the floor? Who decided this and why? What happens if I raise a concern? Transparency about the purpose and limits of an AI system does far more to build trust than any feature announcement. From threat to tool The organizations getting frontline AI adoption right are not necessarily the ones that spent the most on technology. They are the ones that invested in helping workers understand what the technology is for and gave those workers a real role in shaping how it gets used. When that happens, the narrative shifts. AI stops being the thing workers heard about on the news and starts being the thing that made a difficult part of their job easier. That shift does not happen automatically. It requires deliberate choices made long before any system goes live. The question most organizations face is not whether to adopt AI. That decision has largely been made. The more consequential question is whether the people on the warehouse floor who have to use it every shift will ever actually trust it. About The Author: Michelle Jones is the director of Presales and Solutions Consulting at Logistics Reply, the Reply Group company specializing in supply chain execution software. With more than 25 years of experience spanning supply chain, retail, and technology, she specializes in sales consulting and the design of scalable logistics solutions, drawing on a career that includes roles in industrial engineering, implementation, client success, and consulting. For more information, please visit www.logistics-reply.com.