Let’s be honest: most “AI-powered” warehouse systems aren’t intelligent, they’re just automated. They follow rules, push alerts, and surface dashboards. They’ve digitized the manual process, but they haven’t changed the way decisions are made.
Meanwhile, modern commerce has changed everything.
Fulfillment centers now face dynamic demand, shrinking labor pools, and customers who expect precision and speed with every order. What once worked for predictable supply chains delivering to a route of stores each week now breaks under volatility.
The result? Operations teams are buried in reports while the real opportunities like labor inefficiencies, shipping overages, and inventory misplacement go unnoticed until they hit the P&L.
It’s time to move beyond systems of record and even beyond systems of intelligence.
Supply chains are entering a new era: agentic systems: Platforms that understand performance, explain the “why,” and guide teams toward actions that create measurable improvement.
The Real Problem Isn’t the Warehouse, it’s the Data Model
Warehouses today don’t suffer from a lack of information. They suffer from disconnected, outdated information that is sometimes siloed and generally not well defined with the context required to make it useful to AI models.
Take the approach to benchmarking and providing targets for your operations. Traditional benchmarking systems rely on static, survey-based metrics, numbers that describe where you’ve been, not where you’re headed.
Using the WERC Benchmark Report as an example, it is published annually and based on 100–200 respondents, it’s a snapshot in time. Useful for historical reflection, but far too slow for operators managing thousands of shipments daily.
By contrast, Supply Chain Intelligence (SCI) benchmarks are derived from real-time data across billions of transactions. Deposco’s network processed over 2.9 million shipments and $523 million in GMV during a single peak season, creating a live view of performance across industries, regions, and fulfillment models.
Where static benchmarks lag, SCI provides live context about your operations, helping operators see how they perform relative to their peers today, not last year.
From Systems of Record to Systems of Action to Agentic AI WMS
Business software has evolved in three major eras:
- Systems of Record digitized transactions.
- Systems of Intelligence analyzed them.
- Systems of Action began to automate them.
But the next evolution is here: Agentic WMS Solutions, systems that learn from performance, identify root causes, and recommend specific actions to capture the opportunity.
Instead of reacting to problems, these systems build feedback loops between what happened, why it happened, and what to do next.
That is the foundation of Deposco’s Supply Chain Intelligence (SCI) model.
The Three Stages of Supply Chain Intelligence
Stage 1: Benchmarking Intelligence and Understanding Where You Stand
SCI starts by establishing live operational benchmarks across key fulfillment dimensions: labor, inventory, and shipping.
Through anonymized network data, the system continuously compares your KPIs like pick rates, order accuracy, shipment cost per unit, and others against your peers in similar industries, sizes, and regions.
This doesn’t just provide a scorecard. It provides context, showing whether your “good” performance is truly good, or whether you’re leaving margin on the table. Context that is a key input to make AI more specific to your current business challenges. How can an Agent know what to impact without specific industry context? The answer is that it can’t and will provide only general recommendations that don’t move the needle.
Example Use Case:
One SCI customer discovered a 14% productivity gap versus its industry cohort on a specific pick process ahead of peak season. This allowed them to adjust staffing and recover weeks of lost output before volume surged. Without benchmarking, they could have wasted cycles on hiring initiatives without as much impact.
Stage 2: Using Causal AI and Advanced Analytics to Understand Why
Benchmarking highlights where performance deviates from peers. Causal AI explains why.
Using statistical and machine learning models, and our proprietary Causal AI, SCI isolates the underlying processes driving those variances. It can differentiate between human performance issues, process bottlenecks, and systemic design flaws, pinpointing the root cause with quantifiable precision.
Instead of “your labor cost is high,” SCI reveals why: a 12% increase in indirect time caused by suboptimal picking and packing staffing, for example, or a specific receiving pattern driving excessive cycle times.
Example Use Case:
A Brand using SCI identified that what looked like a shipping cost issue was actually from an initiative to increase inventory turns. The buying pattern created through their ERP to reduce turns was driving up priority shipments and inter-facility transfers. Fixing the inventory availability issue solved the shipping cost trend, not rate shopping or carrier diversity as first thought.
This causal layer transforms dashboards into diagnosis, moving operators from reactive management to predictive control.
Stage 3: Knowing What to Do Next
Once SCI understands why performance diverges, it generates clear, actionable recommendations to close the gap.
This is where the system becomes agentic, not just analyzing, but advising. SCI uses Generative AI to summarize insights, forecast impact, and present options ranked by opportunity of doing as well as others on the platform, providing a real path to improvement.
Recommendations might include:
- Reallocating staff between zones based on current order velocity.
- Adjusting inventory placement logic to reduce fulfillment times by 8%.
- Changing carrier volume to capture $180,000 in quarterly shipping savings.
Over time, these decisions compound. The system learns from each action, refining its recommendations and building the foundation for autonomous fulfillment optimization.
From Automation to Autonomy
Automation has always been about efficiency. But autonomy is about intelligence.
Automation executes rules. Autonomy defines them, tests them, and evolves them based on outcomes.
That’s what SCI enables: agentic autonomy. By connecting benchmarking, causal analysis, and generative recommendations into a closed learning loop, it allows the operation itself to become smarter with every transaction.
You can’t automate what you don’t understand. Benchmarking and causal analysis provide that understanding, and Generative AI turns it into confident, measurable action.
The Measurable Impact of SCI Maturity
Every stage of SCI delivers tangible value:
Deposco customers leveraging SCI’s layered approach can expect measurable ROI in under a year, even before full automation maturity, clear proof that understanding drives profitability faster than automation alone.
The Agentic Journey Starts with Clarity
The difference between legacy systems and agentic AI warehouse management solutions isn’t the use of AI—it’s the use of intelligence. Legacy WMS platforms collect data. SCI connects, interprets, and acts on it.
The journey to an autonomous supply chain starts with a single question: how do you perform against the best?