General
The rise of self-learning AI in 2026 fleet operations
Richard Smith
19 Jan 2026
Last year saw Artificial Intelligence (AI) embedded widely throughout the transport sector, and by all accounts, 2026 will be a turning point, companies that are investing now in AI will have a distinct competitive advantage in an increasingly digital market. The ability for operators to continually improve their efficiency, resilience and speed of response to both challenges and opportunities will be the difference between success and failure.
For example, the global leader in AI supply chains, CH Robinson, who manages in the region of 37 million shipments per year has reported that it has integrated multiple agentic AI solutions to drive demonstrable results. The North American division alone receives around 600,000 rate quote enquires each year, historically, when these were handled manually only 60% to 65% were responded to, however by using agentic AI solutions the company can now respond to 100% of enquires with a response time of 32 seconds per quote. Agentic AI systems understand and respond to customer enquires without human intervention; they are designed to automate complex tasks, provide personalised experiences, and free up human workers to tackle more demanding challenges. Significantly, they can continuously improve their own performance through self-learning, unlike traditional AI, which requires human input for specific tasks.
CH Robinson goes on state that the “sophistication of customer response has increased exponentially” and as a result they are converting a third more freight enquires than was previously possible. Human resources had up to ten data points to use when responding to a quote, whereas the agentic AI tool has, “tens of thousands if not hundreds of thousands of data points available to them”.
The tool is also being used to optimise revenue management strategies, the manual pricing procedures that were previously used could have 30 to 90 day windows without any adjustments to market conditions, whereas agentic AI continually tests the pricing strategy instantly to ensure margins and volumes are reacted to instantly, resulting hundreds of adjustments per day as the system reacts continually to global supply and demand for freight.
These examples illustrate the scale of opportunities that AI solutions will provide for transport companies, agentic AI will rapidly become the foundation of software design, linking load planning, maintenance, warehousing, shipping, finance, capacity planning and data collection more closely and quickly than has previously been possible. We can expect the growing use of AI solutions by logistics companies to reshape the human resource requirements, with operational roles increasingly supported by AI systems, but also new roles will be created in data and system management.
Companies need to be mindful about the types of new roles that will be created with an AI working environment so they are better prepared for a changing market.