Changing global tariffs are already pushing cost structures upward, but their full impact on inventory and working capital will only become visible with a lag. This delay creates a dangerous blind spot for operators — higher landed costs, longer lead times and shifting sourcing patterns will start to accumulate in warehouses over the next two to 12 months. For many, this will be compounded by the risk of a recession, sharpening the consequences of excess inventory, stockouts or misallocations.
The core issue isn’t simply cost; it’s accuracy and agility. In a world where tariff and shipping uncertainty can compress margins and flexibility overnight, the companies that can trust their inventory data and make fast, confident decisions will survive. Those who can’t may face a cascade of unforced errors: over-ordering to “play it safe,” running out of stock on critical SKUs, or scrambling in fire drills to find misallocated inventory or reconcile physical versus digital inventory. Each of these outcomes burns cash and morale.
This is exactly where inventory robotics employing artificial intelligence is reaching an inflection point in 2025. The newest generation of AI-native warehouse inventory drones are hitting a sweet spot: cheaper than manual labor while also being 10 times faster and more accurate. These systems, often in the form of lightweight autonomous drones with no human operator, or more expensive but higher-throughput telescoping autonomous mobile robot (AMR), draws on advances in AI “world models” to navigate cluttered, dynamic warehouse environments — something not feasible via technology even a few years ago.
These robots don’t require infrastructure changes like installing thousands of beacons or location stickers. They can be deployed in under a month, integrated with existing warehouse management system (WMS) platforms, and within two or three weeks begin identifying high-value discrepancies between the physical world and digital inventory. This new form of inventory automation paints a low-friction path to clarity. The difference is stark: Instead of relying on once-a-quarter or even annual full physical counts, or chaotic and extrapolated human cycle counts, operators can get a near-real-time picture of what’s actually on the racks or on the floor.
The benefits are both tactical and strategic. First, the immediate benefits for warehouses: reducing shortages and incorrect reorder points, eliminating the stress and cost of emergency orders, and minimizing the chance of losing customers due to unfulfilled promises. But just as important, the units directly cut working capital needs. Accurate inventory means you can confidently lower safety stock buffers, freeing up cash without increasing risk in these turbulent times in global trade.
Second is the broader strategic importance: As the macroeconomic environment tightens, and tariffs start to bite harder, it will be crucial to both lower operating costs and also protect against increased volatility. Having a clear and current view of inventory becomes the foundation for intelligent procurement, better vendor negotiations and smart reallocation of inventory across nodes.
Inventory is capital sitting on the floor. If it’s miscounted, mislocated, or miscategorized, it’s dead capital — and worse, it leads to bad decisions downstream. Many companies throw bodies at this problem: manual cycle counts, overtime shifts, auditors. But that’s not just expensive; it’s slow, prone to error (working a full shift in inventory counting is mind-numbing), and unsustainable, during peak season, labor shortages or budget cuts.
The emerging class of AI-native inventory robotics is finally breaking this cost/accuracy tradeoff. These robots don’t just “scan barcodes faster”; they build probabilistic models of case counts and occupancy, detect SKU-level anomalies, and learn to flag edge cases without needing rigid rulesets. They’re driven by warehouse-scale vision and localization models that have only become commercially viable in the last 12 months, thanks to advances in transformer-based architectures, self-supervised learning and low-cost edge computing and sensors.
We now have the tools to track inventory more accurately, while spending less money on tracking. These small, nimble robots flip inventory management from a cost center to a leverage point, while collecting valuable data on warehouse operations.
For leadership teams navigating an uncertain economy, this is the time to bring discipline and automation to core operational data. If shipping times get worse, tariffs go higher, demand softens or whipsaws, and the margin for error shrinks fast. The one thing you can control is how clean, current and accurate your internal data is, starting with inventory.
A WMS is only as good as the data you feed it. Robots that give you daily or weekly updates on inventory accuracy, with minimal human input, become a force multiplier for every other function — purchasing, finance, operations and customer success.
AI inventory automation, in the form of affordable, high-speed autonomous robotic systems, has moved from experimental to essential. In the face of geopolitical and macroeconomic headwinds, the companies that adopt it now will be better positioned to operate lean and resilient. Those that don’t will be navigating uncharted waters with erroneous maps, guaranteeing turbulence when precision is most needed.
Jackie Wu is co-founder and chief executive officer of Corvus Robotics.
