From Human Experts to AI Agents: Agentic AI for Knowledge-Intense Problem-Solving in Logistics?

Abstract

Logistics planning is a complex and complicated problem solving process requiring a wide range of knowledge and competence. A planning person or team represents a particular set of knowledge, skills, experience and personal attitudes. His, her or their level of competence decides about success or failure of the planning and about the quality of its results. With this, logistics planning is knowledge-based and knowledge-creating. Because of this, Knowledge Management can provide tremendous support to the planning person. Even though, there is a wide variety of (software) tools applicable to solve specific sub-problems throughout the planning process, human expertise is the key to develop creative planning solutions so far. Latest developments with regard to Artificial Intelligence (AI) might change this picture. AI is one of the most promising technological advancements, but also one of the most ambivalently discussed topics these days. It has surpassed humans at a number of tasks already and continues in doing so at new tasks in an increasing rate. Because of this, AI is capable to assist humans in many different ways. The latest boost of development moved AI application from those sophisticated communicators or task-oriented AI to intelligent agents or agentic AI allowing for autonomous, goal-driven decision-making. Hybrid approaches combine those advanced AI capabilities with human oversight, offering context-aware adaptability without full autonomy. In this way, artificial intelligence and human intelligence join forces in teams, for example, to solve complex problems creatively. Against this background, the paper analyses agentic AI with regard to its potential to support creative problem solving in a knowledge-intense field like logistics planning. Will AI agents become co-workers in logistics planning teams or can agentic AI design logistics solutions autonomously? With this, the paper focusses on chances and challenges coming with the highly dynamic evolution of AI within a complex, knowledge-intense application area where human experience, problem-solving capability and creativity is decisive. It contributes to discussions about necessary adjustments in how organizations manage their knowledge considering artificial and human intelligence.