What is agentic AI for warehouse operations?

Agentic AI refers to autonomous digital workers that can reason, act, and learn within warehouse operations. Unlike traditional automation that follows rigid rules, AI agents understand context, investigate exceptions, and take action—like generating coaching plans, investigating claims with video evidence, or surfacing safety patterns across a network of sites.

How does AI-powered warehouse intelligence differ from WMS or LMS?

WMS and LMS are systems of record—they capture transactions and timestamps. AI-powered operational intelligence is a system of reality—it captures what actually happens using computer vision, understands why using AI, and acts autonomously using agents. It sits above existing infrastructure without requiring rip-and-replace, connecting WMS, LMS, TMS, and other systems into a unified intelligence layer.

What ROI can warehouse executives expect from agentic AI?

Early adopters of AI in logistics report 15% cost reduction, 35% lower inventory levels, and 65% improved service levels. OneTrack customers specifically have achieved 93% fewer safety incidents, 11% UPH improvement, and payback within 90 days. One customer saved $287K annually from break and lunch compliance alone.

Why should warehouse leaders act now on AI adoption?

The AI in logistics market is projected to grow from $26B to $708B by 2034. By end of 2026, 40% of enterprise applications will integrate AI agents, up from less than 5% in 2025. Companies deploying now are building data advantages, process advantages, and talent advantages that compound over time. With 490,000+ open logistics positions and 40%+ turnover, the industry can't hire its way out of the labor crisis.

Executive Briefing

The $28 trillion blind spot in physical operations

AI transformed digital businesses. Physical operations got left behind. This briefing is for leaders ready to move first.

Schedule a briefing Read the thesis

For VP Operations, VP Supply Chain, Directors of Warehouse Operations at multi-site enterprises

The Problem

Your enterprise stack captures transactions. Not reality.

Warehouses, factories, distribution centers—30% of global GDP. $28 trillion in physical operations running on software designed in the 1990s. WMS, ERP, LMS, TMS, YMS, HRIS. Six systems, six partial views. None with the truth.

System	What it knows	What it misses
WMS	Inventory locations, scan timestamps	What actually happened between scans
LMS	Outcomes and metrics	Root causes of performance gaps
TMS	Shipment schedules	Loading quality and dock behavior
Telematics	G-force impacts	Whether the hard brake was reckless or saved a life

150,000+ warehouses in the US. The average supervisor observes 5% of floor activity. The other 95% is invisible to every system in your stack.

Why Now

Three forces are converging

This isn't a gradual evolution. It's a window—and it's closing.

The labor crisis is structural

490,000+ open logistics positions. 40%+ annual turnover.

Not a cycle. A structural shift. Workers chose flexibility over physical labor. Immigration shrinking the pipeline. Aging workforce accelerating retirements. By 2033, 3.8M manufacturing jobs needed—nearly half will go unfilled. You can't hire your way out.

AI finally works for physical operations

$26B today → $708B by 2034 (44% CAGR)

Vision models hit 98%+ accuracy. Edge processing runs real-time at the sensor. Inference cost dropped from $0.10 to $0.001. The bridge between digital intelligence and physical operations exists.

AI agents are the new workforce

40% of enterprise apps will integrate AI agents by end of 2026

Not chatbots. Autonomous digital workers that reason, investigate, and act. 23% of organizations scaling them enterprise-wide. They handle the computer work so your people do the human work.

The New Model

From systems of record to systems of reality

The operating stack for physical operations. Rebuilt from scratch. Three layers.

Layer 01

Capture

Cameras are the most powerful sensors in the world. Every other sensor gives you one data point. A camera captures everything: context, sequence, cause and effect.

30-minute install per forklift. No IT project. Go live the same week.

Layer 02

Understand

AI finds the signal. Not the 98% that's fine—the 2% that needs attention. Sensor data combined with WMS, LMS, and clock data to answer questions that were previously impossible.

Exception-based operations. Supervisors get coaching plans, not dashboards.

Layer 03

Automate

AI agents that investigate, report, and act autonomously. The work of 100 engineers analyzing every case pick across a network of DCs—done with a single person.

1,000+ agents deployed. Scheduled reports, automatic investigations, real-time coaching alerts.

Yesterday

Transaction-based operations

Event happens → Human records it → System stores record → Report generated → Manager reviews → Action taken (maybe)

Tomorrow

Reality-based operations

Event happens → Sensor captures it → AI understands it → Agent acts on it → Human handles exceptions

This doesn't replace your WMS, ERP, or LMS. It sits above them—connecting SAP, Blue Yonder, Manhattan, or legacy AS400 systems into a unified intelligence layer. No rip-and-replace. One integration, every system connected.

Customer Evidence

Results from Fortune 500 operations

CJ Logistics

40+

Sites deployed

Rolled out as a labor management system across their North American network. 40 sites, two engineers. Every milestone coordinated by AI agents. Engineers only respond to exceptions.

Church & Dwight

$287K

Annual savings

Paid for the entire system at one site with break and lunch compliance alone. Went from 120 lost labor hours per week to about five or six.

Kellanova

93%

Fewer safety incidents

Full supply chain tracking from factory to customer. Track a box of Cheez-Its at every touch point with video. Every distribution manager presents safety analytics with the same data, in minutes.

It's not always the technology's fault. It's not always the people. It's the intersection of people, process, and technology. How do you use that together to get the most out of everything?

Kevin Coleman, CEO — CJ Logistics America

93%

Safety incident reduction

11%

UPH improvement

Day payback period

70%

Fewer OS&D claims

Our Thesis

What we believe—and what we don't

Human augmentation before robotic automation

Robotic automation: $5-50M, 18-24 months. Human augmentation: 30-90 days, immediate ROI. Before you automate tasks away from humans, make humans as effective as possible.

Intelligence creates the moat, not the agent

Anyone can spin up an AI agent in an afternoon. Differentiation comes from data infrastructure, operational calculations refined across hundreds of deployments, and network benchmarks that make every customer better. You can vibe-code an agent. You cannot vibe-code hundreds of warehouse sensor networks.

The rising tide effect

Every instrumented operation adds to our understanding of what good looks like. New customers benefit immediately from network-wide intelligence. We don't optimize against your history. We optimize against best-in-class across the industry.

Transformation Roadmap

Four phases. Start seeing results in weeks.

From blind to autonomous. Phase 1 delivers impact in weeks.

Now

Visibility

Deploy sensors. Establish ground truth. Decisions based on reality, not guesswork. Weeks, not months.

6-12 Months

Intelligence

Pattern recognition: which behaviors precede accidents, which breakdowns cause delays, where hidden waste lives.

12-24 Months

Automation

Coaching plans generated automatically. Claims investigated without human intervention. Exceptions surfaced, prioritized, and routed.

24+ Months

Autonomy

Facilities that analyze, optimize, and act continuously. Human oversight, not human initiation.

Industry Voice

From the field: AI at scale in logistics

CJ Logistics America — $1.2B Revenue, 3,500+ Employees

“Supply chain has a ton of data. It's limited on information and it's harder to get to the intelligence piece. AI is that bridge—getting to intelligence at a much quicker pace and a much greater scale.”

Kevin Coleman, CEO — CJ Logistics America

The Window Is Closing

Timelines that were 18 months out six months ago are shipping now. If you're still evaluating, your competitors are already deploying.

By 2030, 50% of supply chain decisions will be made by intelligent agents. 80% of commercial applications will embed AI. The first fully AI-driven manufacturing sites launch this year. Every month you wait, early movers build data advantages that compound. This isn't a market where fast followers win.

Access to intelligence at the cost of energy. That changes everything.

Schedule a Briefing

This is a strategic conversation, not a product demo

How early movers are deploying AI across their operations—and what the roadmap looks like for yours.

Industry landscape and competitive dynamics
How AI agents are deployed across safety, productivity, and quality
Customer case studies from Fortune 500 networks
A custom transformation roadmap for your operation

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30-minute strategic conversation with our leadership team