A new survey by Ontegos Cloud shows the freight forwarding sector is betting big on artificial intelligence, yet most operators lack the clean, structured data that AI systems require to deliver results. The finding highlights a widening gap between industry expectations and operational readiness.
Optimism Outpaces Preparedness
According to research by Ontegos Cloud, nearly half (48%) of freight forwarding professionals believe AI will transform the industry within the next three years. But the enthusiasm is tempered by uncertainty: 39% expect AI to play a role but admit they don’t know how, while 14% doubt it will bring meaningful change.
The mixed sentiment highlights a deeper problem, companies may be excited about AI’s promise but remain unsure about the path to practical implementation. Similar dynamics have been seen in other industries where data readiness proved the real bottleneck to scaling AI, from financial services to healthcare. Without structured and reliable inputs, even advanced machine learning tools have failed to generate consistent value.
Data Gaps and Manual Workload
The survey revealed a pressing obstacle: poor data quality. Only 23% of respondents said at least three-quarters of their company’s data is clean and reliable. By contrast, 38% trust just half to three-quarters of their data, while nearly one in three admit less than half is dependable. For an industry where precision drives margins, that level of inconsistency makes automation a steep climb.
More than 70% of professionals spend at least a quarter of their working day on low-value, repetitive tasks such as document chasing and email follow-ups. Within this group, 43% spend more than 40% of their time on such activities. These inefficiencies not only erode productivity but also prevent companies from investing the resources needed to build the digital foundations required for AI adoption.
Oliver Gritz, founder and managing director of Ontegos Cloud, noted that companies often expect AI to fix messy, unstructured data automatically. “At the core of every successful AI project is a structured data model and a reliable system architecture,” he said in an official statement. “Without that discipline, the promises of AI will remain out of reach.”
The Overlooked Competitive Edge
While hype around AI remains strong, the real question may not be who deploys the most sophisticated algorithm but who first establishes a trustworthy data environment. In other sectors, firms that invested early in data governance, such as automakers integrating supplier quality systems, were able to scale digital tools faster and more profitably.
Freight forwarders now face a similar pivot. Those that move quickly to eliminate manual bottlenecks and clean up their data will be positioned to unlock AI’s potential in routing, pricing, and capacity forecasting. For others, the gap between expectations and reality may only widen, leaving them on the back foot as digital competitors advance.
