Why Logistics Tech Is Failing the AI Test

Logistics technology is failing to deliver on AI promises because most companies lack the foundational data infrastructure needed for accurate AI execution, according to Gnosis Freight executives. Michael Rentz, Chief Revenue Officer, argues that AI amplifies errors in incomplete or conflicting container data, leading to costly mistakes and eroded trust in the technology.

The freight technology industry has a problem that has become more apparent as more organizations implement artificial intelligence. AI has become the dominant selling point across virtually every logistics software category, from transportation management to container visibility to customs compliance. Carriers, forwarders, and shippers are being pitched AI-powered dashboards, AI-driven ETAs, and AI-enabled workflow automation at a pace that has outrun the industry’s ability to evaluate what any of it actually means in practice. Michael Rentz, Chief Revenue Officer of Gnosis Freight https://www.gnosisfreight.com/container-tracking-software?utm source=freightwaves&utm medium=media&utm campaign=48764839-Q3.26%20%7C%20FreightWaves%20Media , has a clear diagnosis for why so many of those implementations disappoint. “AI does not create accuracy, it amplifies whatever you feed it,” Rentz said. “If the underlying container data is incomplete, delayed, or conflicting, the AI does not just fail quietly. It confidently makes the wrong call, automates the wrong action, and scales the mistake across your entire operation.” Austin McCombs founded Gnosis Freight in 2017 with a focus on building powerful container tracking software. Over time, the company arrived at a more foundational realization that has grown more consequential as AI has entered the logistics mainstream. “We didn’t fully recognize the scale of this challenge until the platform itself demanded it,” Rentz said. “We started out focused on building great software. What we learned is that the data infrastructure surrounding the container lifecycle has to be solved first. Without that foundation, no downstream outcome, whether it’s operational efficiency, automation, or AI execution, can be fully realized.” Gnosis Freight’s platform, built around its proprietary container tracking engine, was designed to establish a single, validated, real-time record of every container milestone from booking through empty return. According to Rentz that foundation is less a competitive differentiator than a prerequisite most of the industry has yet to build. “Most companies are skipping the foundation and going straight to the model,” he said, “and that’s why so many AI pilots in supply chain look great in a demo and fall apart in production.” The Data Readiness Gap The gap between AI promise and AI reality in freight operations is, at its core, a data problem, according to Rentz. The industry is only beginning to address it. “True data readiness is rare,” Rentz said. “What we see most often is organizations that have data, but it’s fragmented across carrier portals, spreadsheets, freight forwarder emails, and legacy TMS systems with no common structure or timestamp logic.” Data readiness, as Rentz defines it, means a single, validated, real-time record of every container milestone that every team and every system works from simultaneously. By that standard, most shippers are still early in what he describes as a data sovereignty journey. “A lot of shippers are just stitching together three sources and hoping they agree,” Rentz said. “The ones who’ve done the work to get there are the ones seeing real ROI from automation.” When the underlying data isn’t ready, the consequences rarely show up as a dramatic system failure. More often, the team slowly loses trust in the imperfect technology. “It looks like a demurrage https://getfreightdata.com/wiki/terms/demurrage?utm source=fw article&utm medium=tooltip&utm content=demurrage bill nobody saw coming,” Rentz said. “It looks like an ETA prediction that was off by four days and nobody caught it because the system said everything was fine. It looks like an automated workflow that triggered the wrong drayage pickup because a terminal update never made it into the system cleanly.” “The failure mode isn’t dramatic,” he continued. “It’s death by a thousand small errors that erode trust in the technology until the team stops using it and goes back to manual. That’s the graveyard most AI logistics pilots end up in, and bad data is almost always the cause.” What Operational-Grade Data Actually Requires Gnosis Freight’s answer to the data problem is the container tracking engine at the core of the platform https://www.gnosisfreight.com/container-tracking-software?utm source=freightwaves&utm medium=media&utm campaign=48764839-Q3.26%20%7C%20FreightWaves%20Media . Rather than routing data through third-party aggregators, Gnosis establishes direct, first-party relationships with ocean carriers, ports, terminals, Class I railroads, AIS satellite feeds, and U.S. Customs. That first-party access, Rentz said, is foundational to what separates Gnosis’s approach from much of the rest of the market. “Unlike a lot of providers that lean on third-party aggregators, we go directly to the source,” he said. “That relationship-first approach gives you a stronger data infrastructure, but it also opens the door to workflows the industry hasn’t been able to build before.” He points to Gnosis’s partnership with PayCargo to build the Container Payment Portal as an example of what becomes possible when ecosystem partnerships go deep enough. But sourcing the data directly is only part of the equation. Raw data, however well-sourced, isn’t the same thing as operational-grade infrastructure. “Raw ingestion is only half of it,” Rentz said. “The validation layer is where the real work happens. When sources conflict, Gnosis uses a smart hierarchy and contextual logic to resolve those conflicts rather than just displaying whatever came in last. A carrier API telling you one thing and a terminal feed telling you another doesn’t surface as noise to the user. It gets resolved before it ever hits the platform.” Gnosis Freight’s forward-deployed engineering model comes into play with that validation layer. Rentz considers that piece of the operation just as important as the technology itself. “A lot of people think we’re just extracting milestone data,” he said. “What we’re actually extracting is operational knowledge. The most valuable logistics data often lives in the heads of the people managing exceptions every day. By embedding with customers and ecosystem partners, our teams capture that tribal knowledge. There’s a lot of nuance in how a specific business interprets a milestone, handles an exception, or structures a workflow. No integration alone gets you that. It has to be built alongside the customer.” A combination of first-party data access and embedded operational expertise is what sets Gnosis Freight’s accuracy claims apart. “If anybody tells you they’re 98.7% accurate without telling you accurate compared to what, they’re not giving you the full picture,” he said. “Accuracy isn’t a static number. It’s a continuous journey. You should be improving completeness, latency, reliability, and operational context over time. That requires a strong data foundation, alignment across ecosystem partners, deep integrations, first-party access, embedded tribal knowledge, and continuous feedback loops on exceptions. That’s the infrastructure question. The percentage is the easy part to talk about and the hardest part to actually earn.” That infrastructure is also what enables the platform’s predictive capabilities, he said. “When you have that volume of clean, structured, real-time data flowing from that many sources, combined with the operational context our teams bring in, you can start generating predictive ETA milestones that aren’t based on what the carrier told you, but on what the data actually shows is happening across every touchpoint in that container’s journey,” Rentz said. “That’s not something you can buy off the shelf, and it’s not something you can fake with a single feed.” Connecting Infrastructure to the P&L According to Rentz, the value of operational-grade data shows up directly in landed cost, margin https://getfreightdata.com/wiki/terms/margin?utm source=fw article&utm medium=tooltip&utm content=margin protection, and bottom-line outcomes that shippers are already accountable for. “At the end of the day, AI isn’t the goal,” he said. “The goal is using operational-grade infrastructure to drive measurable business value. For us, that means connecting things like demurrage and detention https://getfreightdata.com/wiki/terms/detention?utm source=fw article&utm medium=tooltip&utm content=detention mitigation, drayage scheduling and delivery order automation, automated invoice auditing, and our arrival notice AI agent directly to landed cost optimization. That’s the narrative that actually matters to a shipper’s P&L.” Gnosis Freight’s customers have recorded incredible ROI. One top 50 U.S. importer reported more than $12 million in demurrage and detention savings in under 12 months using the Gnosis platform, with customers reporting an average of 81% reduction in demurrage charges and 64% reduction in detention charges in their first year. Rentz traces those results back to the same infrastructure question. “Once the data foundation is there, the opportunities compound fast,” he said. “The use cases we’re seeing range from EIR email capture that automatically reads inbound terminal emails, extracts gate-in and gate-out details, and files them against the right container without anyone touching it, to real-time demurrage and detention recalculation that updates your free-time risk exposure every time a milestone changes so surprise charges stop happening.” The list extends to automated delivery order creation, drayage https://getfreightdata.com/wiki/terms/drayage?utm source=fw article&utm medium=tooltip&utm content=drayage scheduling triggers, arrival notice processing, and freight invoice auditing against actual operational events. All of this is tied back to margin protection and not treated as automation for its own sake. “None of that is possible without the foundation that makes the underlying data trustworthy,” Rentz said. “AI isn’t the hard part. The hard part is building the infrastructure that makes AI trustworthy enough to act on. Once you have that, and once everything lives in one place, the possibilities for protecting margin compound quickly.” When Rentz describes Gnosis Freight as an AI Global Freight Operating System, he’s referring to the fact that it’s one platform with one validated data source that allows all logistics partners to work from the same record. “Our technology provides the accuracy and transparency today’s supply chains demand, ensuring every container and SKU is tracked and acted on from port to door,” he said. “By leveraging Gnosis’ container tracking engine and AI-powered workflows, leading shippers are taking the next step toward a fully connected supply chain that empowers both their teams and their customers with actionable, real-time intelligence.” What Shippers Should Actually Be Asking At this point, the question is not whether or not your technology vendor offers an AI solution. The question is whether the data infrastructure behind it is capable of making that AI reliable enough to act on. That means asking vendors where, specifically, their data actually comes from: which carriers, which terminals, which rail partners, and through what mechanism. It means asking how conflicting data from different sources gets resolved, and what happens when two feeds disagree. It means asking what “accuracy” is being measured against, and whether completeness and latency are held to the same standard. It also means asking what happens after implementation, like whether the vendor embeds with your operation to learn how your business interprets a milestone or handles an exception, or whether they hand over a data feed and wish you luck. “Every business operates differently,” Rentz said. “Not every organization structures exceptions or measures outcomes the same way. If a vendor isn’t building those nuances into the data layer continuously through feedback loops and exception management, the accuracy you’re sold at the time of purchase is the best it’s ever going to be. That’s simply not good enough for most shippers.” The vendors that cannot answer those questions with specificity are selling the AI layer without the infrastructure to support it, and Rentz has seen enough of those implementations to know how they end. Much of the broader industry recognizes that data matters, but recognizing it and building for it are different things. “A lot of emerging AI logistics companies understand that data is important,” he said. “Far fewer are addressing the operational complexity underneath it. Logistics is nuanced and exception-driven, and that requires real operational expertise, not just a clean integration. That’s the gap between good data and what we’d call operational-grade data, and it’s still pretty wide across the market.” Gnosis Freight received a strategic growth investment from Vista Equity Partners in September 2024 and was named to the FreightWaves FreightTech 100 for 2026. The Gnosis Freight platform is ranked No. 1 for Momentum Leader in both Supply Chain Visibility Software and Transportation Management System https://getfreightdata.com/wiki/terms/transportation-management-system?utm source=fw article&utm medium=tooltip&utm content=transportation-management-system categories on G2. AI outcomes are only as strong as the operational-grade infrastructure supporting them. The organizations that invest in that infrastructure now, Rentz believes, will be the ones positioned to realize AI’s full value later. Learn more about Gnosis Freight at gnosisfreight.com . Supply Chain AI Symposium Past the hype. 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