Over the past three years, Europe’s transport sector has rapidly adopted AI-powered ETA systems, promising real-time accuracy, dynamic predictions, and automated risk alerts. Yet despite significant investment, most logistics teams still face the same reality: ETA tools routinely fail in multi-carrier, multi-modal networks. For companies like RoadFreightCompany, which operate across different regions, partners, and transport modes, the gap between algorithmic prediction and real-world performance remains substantial.
The core issue is data fragmentation. ETA algorithms rely on clean, continuous, and structured data – but European logistics operates in an ecosystem where visibility is inconsistent. Carrier A may share GPS signals every 30 seconds, while Carrier C still uploads location updates manually. Some subcontractors use telematics with 95% uptime, others provide position data only at loading and unloading points. This asymmetry breaks the predictive model long before AI begins to process it.
Another structural challenge lies in disruption patterns the algorithms cannot read. Weather delays, local strikes, driver shortages, port congestion, and border checks are not consistent across countries. While AI may use historical disruption data to suggest trends, it rarely captures the “soft signals” that humans see earlier – reduced carrier responsiveness, unusual dwell times, incomplete documentation, or deteriorating traffic patterns near critical hubs. RoadFreightCompany often detects ETA risks through human oversight long before automated systems register a deviation.
The failures become especially clear in multi-carrier chains with subcontracted legs. When a shipment moves from a primary carrier to a regional subcontractor, data smoothness drops sharply. The handover creates a blind zone where:
- location data becomes irregular
- delays accumulate undetected
- documentation exchange slows down
- ETA calculations “freeze” or revert to generic buffers
For long-haul routes across Germany, Poland, Benelux, and the Czech Republic, these gaps routinely add hidden delays of 2–5 hours that AI-based tools cannot correct in real time.
Another reason ETA systems struggle is that they rely heavily on static assumptions: the same average speed, the same border wait time, the same carrier reliability score. But in Europe’s current environment – marked by labor shortages, new regulatory checks, fluctuating fuel costs, and unpredictable traffic – static assumptions lose relevance within days. At RoadFreightCompany, internal audits show that even high-end commercial ETA platforms mispredict arrival times by 12–18% on complex multi-leg routes.
The industry often assumes that training algorithms on larger datasets will solve the problem, but quantity does not equal quality. European logistics networks generate enormous data volumes, yet much of it is unstructured, inconsistent, or delayed. AI models trained on flawed inputs simply replicate flawed predictions at scale.
Moreover, not all disruptions are quantifiable. A driver’s decision to reroute for rest breaks, a delayed loading slot due to warehouse staff shortages, or last-minute customs questions can shift the entire chain without producing a clear data signal. These factors require operational judgment, not machine estimation.
The path forward isn’t abandoning AI, but integrating it with operational control. Logistics companies need systems where algorithmic predictions and human oversight reinforce each other rather than compete. RoadFreight Company uses AI tools as a first-layer indicator, not a final source of truth. The company combines predictive models with continuous manual verification, local carrier communication, and internal risk escalation workflows. This hybrid approach captures deviations earlier and prevents small delays from escalating into full-route failures.
AI-based ETA systems will eventually improve as the EU moves toward standardized digital documentation, unified telematics requirements, and better carrier compliance. But today, their accuracy is still limited by the fragmented nature of Europe’s transport networks. Until these structural gaps close, the most reliable ETA is one supported not only by algorithms – but by people who understand how networks behave beyond the data.