BY TARUN AGRAWAL
Artificial intelligence (AI) is no longer a futuristic concept—it is a present-day reality that is transforming supply chains across various industries. From predictive analytics to autonomous decision-making, AI is changing how organizations manage complexity, respond to disruptions, and build resilience. But as we embrace automation, a critical question arises: Are we overlooking the human factor?
In a Delphi study of Swedish and American manufacturers, we (Chalmers University of Technology and Auburn University) are exploring this issue. Drawing on insights from experienced professionals—both users and providers of AI-based automation—we are studying the dual nature of AI use, its transformative potential, and its unintended consequences.
AI in the Supply Chain: Beyond Efficiency
AI is increasingly being used to improve demand forecasting, risk assessment, procurement, and contract management. Unlike traditional automation, which follows predefined rules, AI learns from data, adapts to new circumstances, and offers predictive and prescriptive insights. The experts in the aforementioned study described AI as a ”transformative mechanism” that enhances decision-making and organizational intelligence. It’s not just about replacing manual tasks—it’s also about improving strategic thinking and responsiveness. One participant in the study noted that: ”AI improves resilience by monitoring global events such as macroeconomics, geopolitics, and natural disasters, and by offering real-time recommendations on how to avoid disruptions.” Another participant noted that: ””AI helps identify patterns we wouldn't otherwise see, streamlines procurement, and reduces manual work." This transition from rule-based automation to adaptive intelligence marks a new era in supply chain management: an era in which machines not only perform tasks but also actively shape decisions.
The Unintended Consequences of Implementing AI
Despite all its potential, AI also poses risks. The Delphi study highlights several unintended consequences that could make supply chains less resilient:
• Reduced human involvement: As AI takes over routine tasks, interaction with people may decrease, for example in areas such as supplier negotiations and customer service.
• Loss of skills: Overreliance on AI can lead to a loss of critical thinking, negotiation, and problem-solving skills.
• The “Black Box” Syndrome: Many users accept AI-generated decisions without understanding the logic behind them, which highlights the need for transparency and accountability.
• Technological dependence: Companies can become overly dependent on AI systems, making them vulnerable when those systems fail or behave unpredictably.
An expert in the study issued the following warning: ”Misuse or misunderstanding of AI can be just as harmful as not using it at all”.” These concerns underscore the need for a balanced strategy—one that integrates human oversight and control with technological innovation.
Collaboration Between Humans and AI: A Strategic Call to Action
The study shows that the successful implementation of AI depends not only on technology, but also on how people interact with it. Experts foresee a shift in the roles of the workforce—from task execution to strategic oversight, ethical judgment, and system monitoring. In the future, those working in various roles within the supply chain will need new skills such as: data literacy, an understanding of algorithms, and the ability to collaborate in hybrid human-machine teams. As one respondent put it: ””We need people who can create alongside AI, not just passively use the technology." This requires the development of models for collaboration between humans and AI—models in which human judgment is preserved while leveraging the capabilities of AI. Organizations must reevaluate job roles, training programs, and leadership strategies to prepare for this change.
AI and Resilience: A Double-Edged Sword
AI can improve supply chain resilience by enabling early warning systems (EWS), scenario modeling, and rapid response to disruptions. It supports the identification of weak links, accelerates decision-making, and improves coordination among partners. When AI is strategically aligned with an organization’s goals, it becomes a powerful enabler of agility and adaptability. However, AI’s potential is diminished by new risks. The same systems that increase speed can, if not properly managed, lead to errors. Study participants expressed concerns about becoming overly dependent on AI systems, the vulnerability of algorithms to manipulation or errors, and the potential loss of organizational knowledge when automation replaces manual processes. Ethical issues related to data protection, bias and distortion in algorithms, and regulatory compliance were also raised. An expert’s reflection: ”We need secure integration services and clear strategies for managing digital risks—otherwise, technology will become more of a burden than an asset.”
Insights from the Delphi Study
The Delphi method used in the study is a structured communication technique that gathers expert opinions through several rounds of questioning. In the first round, participants answered open-ended questions about their perceptions of AI, its role in supply chains, and the challenges associated with implementing AI. Key themes that emerged:
• AI is viewed as a cognitive tool that supports decision-making, not merely the automation of tasks.
• Successful AI implementation requires human understanding, strategic thinking, and ethical oversight.
• Data quality, system integration, and digital governance are major obstacles.
• The workforce needs to evolve to meet new demands for working in hybrid teams alongside AI systems.
The study also revealed a lack of clear understanding of how AI systems work—which leads to blind trust in the data provided by the algorithms. This ”black box syndrome” poses a serious threat to organizational resilience.
Workforce Transformation and Skills Development
Experts consistently emphasized that AI implementation will reshape the roles and responsibilities of supply chain personnel. Operational roles are expected to shift toward monitoring, managing deviations, and strategic oversight. Tasks such as supplier negotiations, procurement, and routine customer service are likely to become increasingly automated through tools like generative AI and chatbots. While efficiency may increase, human interaction in many transactional areas will decrease. Or, as one expert noted:
”People will increasingly act as supervisors and improvers, not just as task performers. They will take on more strategic roles—using their ethical judgment and creativity and overseeing systems.”
The experts also predicted a significant shift in the skills required for future supply chain roles. Traditional competencies—such as negotiation and knowledge of manual processes—may become less important, while the demand for data literacy, coding, algorithmic understanding, and interpersonal collaboration in hybrid human-machine teams will increase. Skills such as strategic thinking, systems communication, and ethical reasoning will become increasingly important. Or, as one respondent put it: ”We need people who can collaborate with AI, not just use it passively.”
The Next Step in the Delphi Study
The study is still ongoing, and future roundtables are planned to examine how AI affects different management levels—senior, middle, and operational—and which skills are expected to increase or decrease as a result of AI-driven automation. Experts will be asked to rank skill categories and identify the negative consequences of AI implementation. This will help build a more nuanced framework for human-AI integration in supply chains and guide organizations in building a workforce equipped with the skills needed for the future.
Practical Implications
For supply chain leaders, the message is clear: AI should not replace people—it should redirect people toward more value-adding activities, such as management, strategic decision-making, and creative problem-solving.
Organizations must:
• Design transparent and accountable AI systems.
• Invest in employee skills development so that they are able to collaborate with AI.
• Promote a culture of ethical oversight and continuous learning.
• Develop frameworks for human-AI interaction that promote resilience and responsiveness.
By doing this, we can create supply chains that are not only smart but also adaptable, resilient, and people-centered.
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Tarun Agrawal is an Associate Professor and Senior Lecturer in the Department of Supply and Operations Management at Chalmers University of Technology.
