Why Warehouse Technology Companies Increasingly Need Logistics People

Warehouse automation may dominate industry headlines, but Prologis Research estimates that only around 30% of modern logistics space globally incorporated some form of automation in 2025. More advanced fixed systems remain much rarer, with automated storage and retrieval systems present in just 3% to 5% of warehouses. Turning the remaining opportunity into working systems will require a workforce the industry does not yet have at scale.

When Locus Robotics chief executive Rick Faulk tells the company’s origin story, he begins with a warehouse problem. Quiet Logistics, the 3PL from which Locus emerged, had automated its operation with Kiva robots. After Amazon acquired Kiva and withdrew the system from the external market, the team faced a choice: return to a manual process or build something themselves. They built inside the warehouse they were trying to improve. As Faulk put it, “great businesses are built out of major problems.”

That origin story runs counter to a common pattern in warehouse technology today. Many products begin with a technical capability and search for an operational use case later, only to discover that an impressive demo does not always survive contact with warehouse reality. Anyone familiar with the discourse around automation within the four walls will recognize the many idiosyncrasies that make warehouses difficult to generalize across: awkward stock, rushed replenishment, inaccurate master data, changing order profiles, temporary workers, customer-specific rules and a long tail of exceptions that appear at the worst possible moment.

As technology moves deeper into live warehouse execution, particularly in brownfield sites, hiring criteria need to evolve with it. In sales, for example, companies need to look beyond two familiar questions: Have they hit quota? Are they a strong consultative seller? Those qualities still matter, but so does an understanding of warehouse operations and the ability to translate that knowledge into decisions across product, engineering, sales and deployment. So perhaps some more relevant questions would be: Tell me about a deal where the customer’s stated requirement was different from the real operational problem. How did you uncover it? How did you quantify the business case, and which customer assumptions did you challenge? 

The reverse test applies when hiring an operator into a technology company. Years spent running a warehouse or managing a large team establish operational credibility, but say little about whether someone can translate that experience into a product environment, where problems must be abstracted beyond one site and solved through influence rather than direct operational control. Employers should ask: Tell me about a process failure you traced to a system, data or integration issue. How did you isolate the cause? When have you converted a recurring floor-level exception into a technical requirement, and how did you test whether the change worked? Strong candidates should be able to reason about systems, interrogate data and distinguish a local workaround from a repeatable product problem. Whether from operations or technology, making warehouse reality legible to technical teams and technical constraints intelligible to operators will be a pillar of any successful candidate.

  

The investment gap has become an execution gap

The 2026 MHI Annual Industry Report found that 58% of industry leaders considered talent acquisition and workforce challenges their leading supply-chain concern, while 73% expect to adopt robotics and automation within five years. MHI attributes the talent constraint to a combination of unfilled frontline roles, high turnover and a widening mismatch between existing skills and the skills needed in increasingly automated operations. Operators need people to run the warehouse today, while technology companies need people who understand how to automate it tomorrow.

 

A broader operations survey suggests that technology investment is still running ahead of execution. PwC’s 2026 Digital Trends in Operations Survey found that 89% of 767 US operations and supply-chain leaders said their technology investments had not fully delivered the expected results. Integration complexity was the most commonly cited reason, followed by data problems and user adoption. The survey covered eight industries and technology investments broadly, so it should not be read as a failure rate for warehouse automation. Its findings are still highly relevant to the conditions in which warehouse systems are deployed.

Warehouse automation has reached an awkward middle. Technical capability is maturing, but value still depends on selecting the right problem, finding the right solution, fitting that solution into an existing operation and converting technical performance into a credible operational return. 

 

A warehouse is a system of exceptions

Engineers naturally model the standard flow. Experienced operators notice where it breaks. They understand how an average units-per-hour figure can conceal a disastrous shift mix; how a small change in carton dimensions can disrupt induction; how replenishment and picking compete for the same labour and space; and how a process that works at normal volume can collapse during peak.

A McKinsey report from 2023 offers a costly illustration. One consumer-goods company invested more than $150 million in a consolidated automated facility, but inaccurate assumptions about inventory turns and the balance between online and store orders left much of its advanced picking automation underused. McKinsey’s prescription is ostensibly about forecasting, although producing those forecasts requires operational judgement. Someone must know which order profiles are representative, which handling units create disproportionate friction and which assumptions will disintegrate during peak.

Warehouse Intelligence platforms can strengthen that understanding by mapping inventory, movement, capacity and exceptions. Their growth reflects a basic dependency: automation requires a reliable picture of the operation beneath it. 

Operational knowledge also helps a technology company ask better questions before a feature enters the roadmap or a solution reaches a proposal. What is the real constraint? How often does the exception occur? Which upstream process creates it? What happens to service, labour and safety when the system is wrong? Can a supervisor recover without calling support?

That questioning prevents two expensive errors: building around a customer’s current workaround as though it were a universal need, and selling a technically viable solution into an operation where the business case cannot survive.

 

The role already exists, even when the title does not

Current vacancies at competing warehouse-robotics companies show the same underlying requirement appearing through different titles and hiring routes.

Sereact starts with logistics experience. Its Solutions Manager must have at least three years in intralogistics, logistics consulting or fulfilment operations, as well as an understanding of warehouse technologies and systems. The role translates operational requirements into robotic solutions and travels regularly to customer sites.

Ambi Robotics asks for a similar combination in its Senior Solutions Engineer. Candidates need at least two years of direct warehouse-robotics experience and must be able to investigate customer processes through site visits, analyse item and volume data, calculate ROI and specify the integrations required to make the robot work. The advert even asks for someone capable of identifying the small details that can make or break a project.

Nomagic demonstrates another route into the bridge role. Its Senior Engineering Project Manager is recruited primarily for deep robotics and industrial-automation expertise, with intralogistics experience listed as an advantage. The work spans design, commissioning and customer acceptance, live performance analysis, cross-system troubleshooting and time at customer sites.

Three vacancies cannot define an entire labour market, but they reveal several routes into the same bridge function: fulfilment operations, intralogistics consulting, established automation providers and robotics engineering. Each company ultimately needs people who can carry a solution from a warehouse problem to a working deployment.

Where will the next generation of bridge talent come from?

Recruiting exclusively from direct competitors simply recycles an already limited pool. Much of the relevant talent currently sits elsewhere in the warehouse ecosystem, under titles that do not immediately resemble product or technology roles: WMS superusers, industrial engineers, continuous-improvement managers, solutions designers, implementation leads, commissioning engineers, inventory-control specialists and operational-excellence managers.

Each brings a different form of operational credibility. A WMS superuser understands how systems are used, ignored and worked around on the floor. An industrial engineer can model throughput, labour and process interdependencies. An implementation lead has seen where apparently sound designs fail during go-live. A warehouse supervisor may lack formal technology experience but understand exception recovery, workforce adoption and the decisions that keep a shift moving.

A 2021 peer-reviewed study of four distribution-centre operators identified 66 critical incidents during cobot implementation. Missing information, limited experience and poor communication contributed to resistance, while the team leader played a decisive role in whether implementation succeeded. Five years later, I think that finding still holds. It also suggests that supervisors and operational change leaders could be a valuable source of bridge talent for future deployments. 

For employers, the real hiring decision is which part of the profile must exist on day one and which part can be taught. A brief demanding proven product craft, warehouse leadership, robotics knowledge, commercial experience and technical fluency creates a candidate who barely exists. The strongest candidates understand the operation, can interrogate the data and can make warehouse reality legible to an engineering team. Companies can then build the missing exposure through site rotations, implementation shadowing, exception reviews and direct access to operators.

Every successful deployment adds to the pool of people who understand what implementation actually demands. That pool remains small. Until that experience base matures, warehouse technology companies will increasingly need to recruit from adjacent careers, selecting for an ability to understand a physical operation, communicate across functions and turn operational complexity into decisions that technology teams can act on.

 

Proximity to the operation is a fundamental advantage

Warehouse technology companies will continue to need excellent roboticists, software engineers and data scientists. Their work becomes more valuable when the problem has been defined by someone who understands the operation.

Locus began with that proximity by accident: a 3PL lost access to a critical technology and built a replacement inside its own operation. Companies can manufacture some of that proximity by involving operators in product decisions and exposing technical teams to live workflows. Knowledge acquired during deployment should also feed back into the product, rather than remaining with the post-sale team.

As vendors converge on technical capability, the more defensible advantage will be operational judgement, accumulated over years of seeing what warehouses do to ostensibly sound systems. That experience produces systems that operators can still trust when the shift begins to unravel.