Robust.AI and the Case for Automating Support Work First

Warehouse automation has absorbed tens of billions of dollars of capital over the past decade, yet adoption remains uneven and returns inconsistent. The core issue is not technical feasibility but economic sequencing. Most automation efforts target picking, packing, and sorting because those are the most visible components of fulfillment. They are also the most complex, the most intertwined with human judgment, and the most sensitive to variability in SKU mix, order profiles, and labor behavior.

Industry data reflects the consequences. According to McKinsey, more than 70% of warehouse automation projects fail to meet their original ROI targets, largely due to integration complexity, extended commissioning timelines, and unplanned process redesign. These failures increase customer skepticism, lengthen sales cycles, and constrain expansion, particularly for venture-backed companies that depend on repeatable deployments to scale.

Robust.AI approaches the problem from a different economic layer. Instead of targeting core execution, it focuses on warehouse support functions, specifically internal material movement. This includes transporting totes between zones, feeding workstations, clearing completed work, and repositioning pallets. These activities are operationally essential but do not increase order accuracy or customer experience. They consume paid labor hours without creating incremental value.

Operational benchmarks consistently show that internal travel accounts for 25% to 40% of direct warehouse labor time. With U.S. warehouse wages averaging $18 to $22 per hour according to Bureau of Labor Statistics data, this represents a large and persistent cost base. In a 500-person facility, a 10% reduction in non-value-added movement can reclaim tens of thousands of labor hours annually. At current wage levels, that translates into several hundred thousand dollars of addressable value without increasing throughput targets or changing service promises.

Robust.AI builds mobile robots to absorb this transport layer. The robots move goods between defined locations so human workers remain stationed at value-generating tasks. Importantly, deployments do not require changes to WMS logic, pick strategy, or facility layout. The robots operate alongside people, forklifts, and carts in live environments rather than relying on segregated lanes or controlled traffic patterns.

From a venture perspective, this design choice materially lowers deployment friction. Facilities can pilot the system in a limited area without committing to full-site automation. Time to value is short because success is measured in labor hours reduced rather than abstract productivity gains. Expansion occurs incrementally as utilization is proven, enabling a land-and-expand motion that aligns with how warehouses actually procure technology. The competitive landscape makes this positioning clearer.

Traditional AMR providers such as Locus Robotics and 6 River Systems focus primarily on pick assistance. Their systems increase pick rates by guiding workers through optimized paths, but they still rely heavily on human travel and require structured workflows. These solutions deliver value in e-commerce fulfillment environments with stable layouts and high item-level picking density, but they are less effective in mixed-use or pallet-heavy facilities where transport dominates labor time.

More infrastructure-heavy automation vendors such as AutoStore and Dematic pursue high-density storage and goods-to-person systems. These approaches can deliver exceptional throughput and accuracy, but they require significant upfront capital, multi-year planning horizons, and major facility redesign. According to public disclosures and operator benchmarks, AutoStore installations often cost tens of millions of dollars and require years to reach full utilization. These systems work best when volume density is stable and long-term demand is predictable.

At the other end of the spectrum, smaller AMR vendors often struggle with reliability in brownfield sites. Many systems perform well in controlled demonstrations but degrade in real-world environments where aisles are blocked, humans behave unpredictably, and layouts change frequently. The result is downtime, manual intervention, and hidden operating costs that erode ROI.

Robust.AI positions itself between these extremes. It does not attempt to replace pick logic or redesign fulfillment. It does not require dedicated infrastructure. Instead, it targets a labor category that exists in nearly every warehouse and behaves similarly across verticals. The technical emphasis on perception and behavior modeling is aimed at maintaining uptime in mixed-traffic environments rather than maximizing theoretical speed.

This tradeoff is economically rational. A slower system that operates consistently across shifts often delivers more value than a faster system that stops when conditions deviate from plan. For buyers, uptime and predictability matter more than peak performance. For investors, this translates into lower support burden, higher customer retention, and more repeatable deployments.

The macro environment reinforces this thesis. Labor availability remains constrained, turnover in warehousing remains elevated, and higher interest rates have increased scrutiny on capital-intensive projects. Buyers are increasingly reluctant to commit to automation initiatives with multi-year payback periods and high execution risk. Solutions that behave like operating expense, preserve optionality, and integrate into existing workflows face less resistance.

The remaining question for Robust.AI is scale. Support functions represent a large but bounded market. Venture-scale outcomes depend on adjacency, whether through deeper integration with labor management systems, safety and compliance workflows, or expansion into higher-value material handling tasks once trust and deployment density are established.

From an investment and competitive analysis perspective, Robust.AI’s appeal lies in its sequencing. It starts with a problem that is economically obvious, operationally measurable, and broadly applicable. It avoids the failure modes that have limited many robotics companies, while preserving optionality for expansion. In a category where ambition often outpaces execution, that discipline matters.

Robust.AI is not attempting to redefine warehouse automation in a single leap. It is removing waste from a layer of work that every warehouse carries, and using that foothold to build credibility, data, and operational presence. That is a quieter path to scale, but a more defensible one.