There is much excitement about the disruption that AI is likely to bring to traditional business workflows, especially to supply chains that deliver everything from staples to luxury goods. The move to AI promises improvements in inventory planning, manufacturing, and delivery, yet the contribution of AI agents to operations has beenPhillips limited so far, and for companies not already on a digitalisation path it offers no shortcuts. Digital tools that automate algorithms, machine learning and data management have existed for years; many so‑called agents are simply rebranded base Parece‑level programs that rely on the mathematical models already in use.

Despite the hype, AI has been applied to supply‑chain tasks for decades – route planning, procurement, inventory management and exception detection. New capability comes from faster data handling and continuous model updates, allowing real‑time optimisation. For example, Danijel Lolic of Formic describes how real‑time data lets a firm sort thousands of apples in minutes, enabling decisions that rival decades of experience.

Large language models add value by interpreting operational data into natural‑language summaries, speeding decision‑making and enabling complex scenario analyses without manual labour. These overlays help firms respond more swiftly to disruptions.

Leading firms are now pursuing deeper transformation: autonomous, self‑learning systems that are more resilient and responsive. Maria Jesús Saénz of MIT says the journey requires a new mindset, long‑term investment, and supplier collaboration, with technology designed around a clear vision ofFinally the desired supply‑chain outcome.