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

AI adoption in US logistics has accelerated dramatically over the past 18 months. Not because of hype, but because AI agents have finally become practical, accurate, and reliable enough to handle real operational work. Today, US freight operators are deploying AI to automate tasks that were historically done manually by brokers, carrier reps, CSRs, and back-office teams. The results are dramatic: lower cost per load , faster cycles, fewer errors, and teams freed to focus on

Document collection is one of the most frustrating bottlenecks in freight operations. PODs stuck in email threads. Lumper receipts that never arrive. Carriers who forget to upload. Billing cycles that drag on for days. Customer service teams fielding "where's my proof?" calls. If you run a brokerage, you know the reality: document collection is high-volume, time-sensitive, and painfully manual. AI is changing that. Why Document Collection Is Perfect for AI Document workflows

Logistics teams are under constant pressure: more shipments, tighter SLAs, rising customer expectations, and shrinking margins. Across brokerages, 3PLs, and shipper operations teams, leaders ask the same foundational question: "How do AI agents actually improve logistics team efficiency?" This guide breaks the answer down clearly using real workflows, operator-driven insights, and practical examples. No hype. No theory. Just how AI agents make teams faster, more accurate, and

Freight brokers, 3PLs, and shippers are all facing the same pressure heading into 2025: do more with less, reduce cost per load, improve customer SLAs, and scale without hiring. AI agents are now the fastest path to that outcome. This year, the freight tech market finally matured enough to deliver real automation - not demos, not hype. This guide breaks down the best AI agents for freight in 2025, the problems they solve, and where they fit in your tech stack. You'll also lea

This Isn't Really a Competition As AI tools flood into freight and logistics, you're probably seeing more platforms promising to "automate everything." But not all AI is built the same way, and that's actually a good thing. Two distinct categories are emerging: Tactical AI agents  that do one job extremely well (like LunaPath) Generalist AI platforms  that coordinate across many workflows (like CloneOps) Here's the thing: these aren't competing approaches. They're complementa

Why This Matters When logistics teams adopt automation and AI, they're trusting new technology with their most sensitive data. Load information. Customer details. Payment records. Operational metrics. Security and compliance aren't optional; they're foundational. That's why LunaPath is AICPA SOC certified, a globally recognized standard for data security, system reliability, and organizational trust. What Is AICPA SOC Certification? The American Institute of Certified Public

The Bottom Line (For Busy IT & Ops Leaders) No re-platforming required.  LunaPath connects to your existing TMS and visibility stack via APIs, webhooks, and secure mailboxes. Your system of record stays your system of record.  Our agents write back clean, validated data; no shadow databases. Security first.  SOC-aligned controls, role-based access, encryption, and audit trails for every action. Built-in guardrails.  Clear scope, human-in-the-loop escalation, rate-limited outr

(Webinar Recap) TL;DR Agents ≠ chatbots.  True agents decide, reason across multiple steps, and take actions across systems. Value compounds with orchestration.  The Agent Maturity Curve moves from single-task agents → multiple agents → agents that coordinate and share context. Readiness matters:  data quality, connected systems, and continuous learning determine outcomes. Trust is designed, not assumed:  disclosure, rate-limited outreach, language adaptability, audit trails,