The promise of artificial intelligence in logistics is colliding with a harsh reality: without clean, unified data, AI amplifies mistakes rather than solving them. According to a FreightWaves report, the freight technology industry has flooded carriers, forwarders, and shippers with AI-powered dashboards, AI-driven ETAs, and AI-enabled workflow automation, but the pace of adoption has outrun the industry’s ability to evaluate what those tools actually deliver in practice.
Michael Rentz, Chief Revenue Officer of Gnosis Freight, laid out the core problem bluntly. “AI does not create accuracy, it amplifies whatever you feed it,” Rentz told FreightWaves. “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.”
This diagnosis comes from Gnosis Freight, a company founded in 2017 by Austin McCombs that originally focused on building powerful container tracking software. Over time, the firm realized that the data infrastructure underpinning the container lifecycle must be solved before any downstream outcome—operational efficiency, automation, or AI execution—can be realized.
The Data Readiness Gap
Rentz described a pervasive “data readiness” problem. “True data readiness is rare,” he 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 calls a “data sovereignty journey.” He added: “A lot of shippers are just stitching together three sources and hoping they agree. The ones who've done the work to get there are the ones seeing real ROI from automation.”
Consequences of Poor Data Infrastructure
When underlying data isn’t ready, the consequences rarely show up as a dramatic system failure. Instead, trust erodes gradually. Rentz offered three concrete examples:
- A demurrage bill nobody saw coming – because terminal updates never reached the system cleanly.
- An ETA prediction that was off by four days – yet the system reported everything was fine.
- An automated workflow that triggered the wrong drayage pickup – because a critical update was missing.
“The failure mode isn’t dramatic,” Rentz said. “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.”
Skipping the Foundation
Gnosis Freight’s platform is built around a proprietary container tracking engine designed to establish a single, validated, real-time record from booking through empty return. Rentz emphasized that this 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,” Rentz said, “and that’s why so many AI pilots in supply chain look great in a demo and fall apart in production.”
| Problem | Consequence | Example from FreightWaves |
|---|---|---|
| Fragmented container data | Confident wrong decisions at scale | AI triggers wrong drayage pickup due to missing terminal update |
| Incomplete or delayed data | Erosion of trust in the technology | ETA prediction off by four days, system says fine |
| Data from multiple sources stitched together | Missed cost events | Demurrage bill that went undetected |
What This Means for Supply Chain Teams
For chief supply chain officers and procurement directors, the implication is clear: before investing in AI-powered logistics tools, ensure your data infrastructure can support it. Rentz’s message is that no amount of sophisticated modeling can compensate for dirty, fragmented, or delayed container data. The industry’s current trajectory—pushing AI pilots without fixing the underlying data—is producing a graveyard of failed implementations. The path to real ROI starts with achieving data readiness: a single source of truth for every container milestone.
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