Agent-Based AI Why Agents in Logistics Don't Fail Because of the Model, but Because of Operational Reality

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In logistics, agent-based AI is intended not only to analyze data, but also to assess what needs to happen next during the ongoing process, prepare decisions, and, in the best-case scenario, take immediate action. Whether this succeeds, however, depends less on the model’s performance than on the quality of the data it accesses.

For logistics companies in particular, the use of agents is a natural fit: They must check inventory daily, prioritize orders, and respond to delivery discrepancies. Many of these decisions are repetitive but must be made under significant time pressure. This has a direct impact on costs and service quality. According to a recent BCG survey, cost reduction and operational efficiency rank among the top drivers for AI adoption for nearly 80 percent of respondents. Many companies recognize this potential, but in day-to-day operations, they often lack the foundation to properly integrate agents into their processes. While information on inventory, orders, or discrepancies is available, it does not reach decision-makers quickly enough or in the right context. This gap cannot be closed by agent-based AI alone. First, a digital backbone is needed to bring data, processes, and decisions into a consistent operational context. Analysts refer to this as “converged workflows.”