Artificial intelligence is increasingly being deployed across logistics operations, from carrier communication to exception management and document collection. But while much of the conversation focuses on automation capabilities, a critical question remains: What data do AI agents actually need to operate effectively in logistics environments? The success of AI automation in freight operations depends heavily on data quality, structure, and accessibility. Without the right

Artificial intelligence is rapidly transforming logistics operations, but the next major shift is not just AI; it’s agentic AI. Instead of simply analyzing data or recommending actions, agentic AI systems can observe events, reason about outcomes, and take action autonomously within defined guardrails. In logistics environments where thousands of operational decisions occur every day, this shift has significant implications. Agentic systems can help teams reduce manual work,

Ocean freight doesn't usually fail loudly. It fails quietly through dwell time. Containers sit at the port longer than planned. Export bookings roll. Customs holds go unnoticed. Appointment windows lapse. By the time someone catches it, demurrage fees are already stacking up. For most shippers and 3PLs, demurrage and detention are reactive line items on an invoice. With the right automation in place, they can become proactively managed risk events. What Is Demurrage Monitorin

Freight brokerage margins are tight, and getting tighter. Labor remains the single largest controllable expense for most brokers, and as shipment volume fluctuates and service expectations rise, scaling headcount has historically been the only way to keep up. That model no longer works. AI is changing how brokerages manage labor by automating high-volume operational work without sacrificing service levels. The goal isn't to replace teams - it's to increase output per employe

“Workflow automation tools” is a broad term, and that’s exactly why it shows up so often in search. Operations leaders are looking for ways to reduce manual work, move faster, and scale without adding headcount. In logistics, though, not all workflow automation tools deliver real value. Some automate surface-level tasks. Others break under operational complexity. Here, we explain what workflow automation tools really mean in logistics, which types actually work at scale, and

Carrier communication is one of the most time-consuming parts of running a logistics operation. Every shipment creates dozens of interactions: check calls, ETA updates, delay notifications, appointment changes, and document requests. For years, this has been manual work - repetitive, fragmented, and hard to scale. AI is changing that. But it's not replacing people entirely. Here's how AI automates carrier communications in logistics, which tasks it handles best, and where hum

As AI adoption accelerates in logistics, one phrase keeps appearing in searches, sales conversations, and architecture discussions: AI agent stack . But what does it actually mean, and how is it different from traditional automation, point solutions, or standalone AI tools? This guide explains what an AI agent stack is, how it works in logistics operations, and how teams should think about building one that delivers real operational impact. What Is an AI Agent Stack? An AI ag

Artificial intelligence is no longer experimental in US logistics, but it's not universal either. Some logistics tasks are highly suited for AI automation. Others aren't. The difference comes down to frequency, structure, data availability, and risk. This guide answers a common and increasingly searched question: What logistics tasks can AI automate effectively in the United States today? Why AI Automation Works Better for Some Logistics Tasks AI performs best in environments

Guest post by CRO, Kris Glotzbach AI adoption in logistics is becoming more structured, but it's not linear. The companies generating real value from AI are not racing toward full autonomy. Instead, they are applying different levels of automation based on risk, trust, and accumulated learning. That nuance matters, especially in an industry defined by variability, exceptions, and high-impact decisions. The question is no longer whether  AI works in logistics. The real questio

The freight brokerage tech stack has changed more in the last 24 months than it did in the previous decade. In 2026, the question is no longer "Should brokers use AI?" It's Which AI tools actually drive operational outcomes without adding complexity. This guide updates our 2025 list to reflect how AI is actually being deployed inside brokerage operations today, with a focus on automation, agentic workflows, and measurable ROI. How AI Adoption Has Evolved Since 2025 In 2025 ,

Why Customer Satisfaction Is a Financial Metric, Not a Soft KPI In logistics, customer satisfaction (CSAT) is often treated as a lagging indicator - something you measure after a shipment moves or an issue gets resolved. But that view misses the bigger picture. CSAT is actually a leading financial lever that directly influences retention, contract renewals, expansion revenue, and long-term margin stability. As freight markets tighten and service expectations rise, logistics

Customer satisfaction in logistics has always been fragile - not because teams don't care, but because freight operations are inherently complex. Shipments move across multiple parties, delays are common, information is fragmented, and updates are often reactive. Most CSAT issues stem from the same root cause: customers feel uninformed, surprised, or ignored when something goes wrong. AI is changing that, not by replacing people, but by fixing the operational gaps that create

As AI adoption accelerates across logistics, the conversation is shifting from individual automation wins to a much bigger question: How do AI agents work together to drive real operational autonomy? That question sits at the center of project44's recent white paper, AI Agent Orchestration: The Path to Supply Chain Autonomy , which explores how multi-agent systems, event-driven architectures, and orchestration layers are redefining how supply chains operate at scale. LunaPath

By Kris Glotzbach, Chief Revenue Officer, LunaPath As I prepared for the recent webinar with project44 , I spent time reflecting on the questions I hear most often from supply chain leaders: What actually makes AI agents different from the last wave of automation? Why does adoption stall in so many organizations? And why does this moment feel different? The short answer is this: we’ve reached an inflection point where visibility alone is no longer enough. The next phase of va

The Track & Trace Problem Operations Leaders Know Too Well Track and trace should  be straightforward. In practice, it's one of the most time-consuming workflows in freight operations. Even with visibility platforms and ELD data, operations teams spend hours each day on: Calling drivers for ETAs Following up on missed milestones Clarifying delay reasons Manually updating systems Responding to "where's my load?" customer inquiries The real issue isn't missing data. It's the la

In freight operations, cost per load is the clearest reflection of operational health. It captures labor efficiency, process maturity, technology leverage, scalability, and margin resilience. Revenue can fluctuate with the market. Cost per load determines whether you survive it. As freight margins compress, operations leaders face mounting pressure to reduce cost per load without sacrificing service, burning out teams, or re-platforming systems. This is where AI automation is

In Project44’s “Agents in Action” webinar , Mark Strassman (Chief Product Officer, Project44) moderated a practical, production-focused panel with Jesse Buckingham (Founder, Vooma) , Pablo Palafox (Co-founder & CEO, HappyRobot) , and Kristopher Glotzbach (CRO, LunaPath) . The conversation was refreshingly direct: AI agents are no longer a “do they work?” question. Now it’s “how do we deploy them effectively?” Below is a summary of the most actionable lessons for shippers, 3PL

Why Multi-Agent Architectures Are Becoming the Standard As freight automation matures, leading logistics organizations are converging on the same realization: No single AI system can handle the full complexity of freight operations. Instead, the most effective teams are building multi-agent freight automation stacks - systems where specialized AI agents work together, coordinated by orchestration logic and overseen by humans. This approach mirrors how logistics teams already

The Question Every Freight Leader Is Asking As AI adoption accelerates across logistics, one debate keeps coming up: Should we deploy one powerful "super-agent" that handles everything, or multiple specialized AI agents designed for specific tasks? The super-agent sounds appealing - one system, one interface, one brain. But in freight operations, focus consistently beats breadth. This post explains why specialized AI agents outperform generalist "super-agents" in freight auto

Picture this: It's 3 PM on a Thursday, and your team has already made 47 check calls today. They've sent 128 emails asking for ETAs. They're waiting on PODs from 23 different carriers. And someone just walked into a meeting saying, "We lost visibility on the Phoenix delivery." This is the daily reality for freight brokers and 3PLs across the country. Carrier communication isn't just time-consuming; it's the operational bottleneck that determines whether you deliver exceptiona