Mr. Samarnath Jha, CEO, Accex Supply Chain, notes that AI has practical applications in demand forecasting, document processing, shipment tracking and the identification of potential disruptions.
According to a September 2026 McKinsey survey, nearly 90 per cent of shippers have adopted at least one transportation AI use case. At Accex, OneView brings transport and warehouse operations into a shared view, helping teams identify patterns, forecast exceptions and make informed decisions. It also paves the way for deeper use of AI to improve service quality, manage costs and increase efficiency.
The Trade-Offs No Algorithm Settles
In logistics, one learns to look beyond the immediate numbers. Holding a shipment for consolidation may reduce transportation costs, but the client’s delivery commitments may take the front seat. Sending a vehicle immediately may protect service levels but increase costs. Logistics teams face such choices every day, and the quality of a decision is shaped by the balance between commercial implications and the practical realities of execution. AI can speed up the analysis of information, while the experience of the team helps determine the most appropriate course of action for a given situation.
Foundations First, Investment Next
One of the biggest challenges for logistics companies is the availability of information, which teams find fragmented across systems, customers and operating partners. Data security and the availability of people who can work with these tools are concerns as well. Companies, in this view, need to get their operational data and systems in order before making significant investments in AI. Accex has invested in technology to bring transport and warehouse operations closer through OneView. A digital foundation also places supply chain players in a better position to assess where AI can make a practical contribution to service quality, cost and productivity. With the foundation in place, attention turns to control: how every AI recommendation is reviewed, audited and approved.
