Poor Data Quality Threatens Early AI Adoption Among Freight Forwarders: Study

A new survey by Ontegos Cloud revealed a striking disconnect between industry optimism for artificial intelligence (AI) and the data readiness of companies to put it into practice.

The research shows that while almost half of logistics professionals expect AI to reshape freight forwarding within the next three years, most companies still lack the reliable data foundations required to adopt these solutions successfully. At the same time, manual work continues to dominate day-to-day operations, underscoring the urgent need for automation.

“The optimism around AI is real. Almost half of freight professionals believe it will transform the industry within just three years. But what is missing is clarity on how this will actually happen. Too many assume today’s AI can turn messy, unstructured data into a masterpiece of accuracy, when in fact the opposite is true,” says Oliver Gritz, founder and managing director of Ontegos Cloud. “At the core of every successful AI project is a structured company data model and a reliable system architecture. Without that discipline, the promises of AI will remain out of reach. This is why data quality matters so much. If the information feeding these systems cannot be trusted, AI will fail to deliver consistent, real-world results.”

Key takeaways:

·        48% of respondents are confident that AI will transform freight forwarding in the next three years. But enthusiasm is tempered by uncertainty.

·        More than one-third (39%) say they believe AI will play a role but are unclear how it will impact operations. A further 14% remain unconvinced that AI will deliver meaningful change at all.

·        This mixed outlook suggests that while the hype around AI is strong, the pathway to practical adoption is far from clear for many operators.

·        Just 23% of respondents say that three-quarters or more of their company’s data is clean and reliable. A larger group, 38%, admit that only 50-75% of their data can be trusted, while nearly one-third (31%) say less than half of their data is dependable.

·        More than 70% of professionals report spending at least a quarter of their working day on manual processes such as chasing documents and responding to emails. Within this group, 43% spend more than 40% of their time on these tasks, while 31% spend between 25% and 40%. Only a small minority (7% of respondents) manage to keep such manual work below 10% of their workload.

“It is clear that logistics professionals spend far too much time on repetitive manual tasks. These inefficiencies are exactly what AI is meant to solve, yet the industry is not ready to let it. The reason is simple: the data foundations are not there. The industry’s real challenge is to put its house in order to build data it can trust.  Only then will AI be able to live up to its potential,” says Gritz.