Beyond the Algorithm: Why Real Supply Chain Pros Still Matter
How Gen AI Can Transform the Importance of Supply Chain Expertise
Generative AI (GenAI) is no longer a concept that will happen in the future. It is already improving route optimization, demand forecasting, and supplier negotiations - applications that were previously the domain of deeply educated and experienced people. Although supply chain leaders have gained their experience working on the ground for many years, the new AI tools can scan through thousands of data points in seconds, suggest strategies, and identify risks before they occur. The real issue is: how does this transformation influence the role of human expertise in supply chain, procurement, and logistics?
When AI is the first line of defense
Supply chain professionals work under pressure and face challenges like a port closure or shortage of raw materials. In the past, they made decisions based on experience, historical data, and communication with other functions. New age AI models can not only detect these disruptions but can also recommend immediate course of actions. This means that AI may be able to solve some of the issues that were once the domain of supply chain managers.
But in order to gain from these AI based suggestions, the leaders still require a good understanding of the networks. For instance, an AI model may recommend to source from another supplier 200 miles away. Without human experts to assess the local regulations, quality assurance or the political environment, it might turn out to be a wrong decision. In other words, generative AI enhances the process of identifying the problems fast, but supply chain experts ensure that the solutions proposed are feasible and sustainable.
The transition from the knowledge workforce to the interpretive workforce
In this new environment, domain expertise is not about knowing the container rates or vendor lead times. It is about understanding how to use the AI outputs correctly and how to relate them to the overall business goals. The best supply chain leaders will be those who can understand how AI came up with a particular recommendation and how likely it is to be feasible in the real world.
For example, a procurement director can use an AI co-pilot that suggests a new contract negotiation strategy based on the current steel prices and shipping trends. The logic of the AI may seem flawless on the surface, but the procurement executive may know that a particular steel supplier has been unreliable in the past and, therefore, can reject the suggestions made by the AI or suggest some risk avoidance measures. Domain knowledge does not disappear during the AI boom; it transforms into a more strategic, more analytical function.
The new trends of upskilling and collaboration rather than obsolescence
The most probable scenario is the coexistence of humans and AI in the workplace rather than the possibility of AI taking over human roles. The generative AI can perform many menial tasks such as analyzing the last month’s shipping history or tracking stock levels in the market, while people work on the higher level functions such as planning, managing suppliers, or developing strategies for doing business across borders.
To build on these changes, organizations must invest in the learning of their people. A logistics leader may learn how to build or fine-tune AI models to provide better forecasting while a buyer can learn how to use advanced analytics to analyze the market. It is important to learn because it means that humans remain the primary decision makers.
What do you think about the evolving role of expertise in the age of Gen AI? Share your insights in the comments, and join the conversation in our online supply chain community, Chain.NET. Joining is free and only takes a few minutes: https://mygs.cc/chain
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