From algorithm to workforce: Preparing supply chain leaders for the AI literacy era

Artificial intelligence (AI) is rapidly reshaping supply chains, evolving from niche optimization algorithms into collaborative, real-time decision-making partners. Decades ago, logistics teams relied on static models to forecast demand or plan routes. Today, Generative AI, large language models, and predictive analytics perform those tasks in seconds across entire networks and ecosystems.

At the 65th Plains Mountain Business Conference (PMBC), held at the University of Nebraska Omaha in October, panelists from academia and industry both emphasized that AI is now everyone’s responsibility in supply chain leadership. As one academic panelist remarked, “If you do not know AI, you are not going to get hired.” It is not just the domain of data scientists or IT departments. Leaders must now understand how to collaborate with AI tools, guide their ethical use, and prepare a workforce to integrate intelligent automation into operations.

Why AI literacy is now core to supply chain strategy

AI literacy is the ability to understand, interpret, and use AI tools ethically—it is fast becoming a core competency and an essential business skill. According to McKinsey, Generative AI could add $2.6 to $4.4 trillion in annual global economic value, primarily from supply chain and manufacturing efficiencies. This gap is not a technology problem but a leadership challenge: cultivating AI fluency at every level of the supply chain. Recent industry commentary echoed this sentiment. As one PMBC speaker put it, “AI is evolving from a tool to a collaborative teammate, one that challenges how we think, decide, and create value.”

AI literacy means more than knowing how to use a chatbot or generate a dashboard. It involves understanding how machine learning models function, interpreting their predictions, knowing when to trust or challenge their outputs, and spotting bias or data quality issues. Global education trends reflect this shift. The University of Florida’s “AI Across the Curriculum” initiative integrates AI learning across all majors, including business and supply chain, emphasizing that all students should graduate with foundational AI competence.

For supply chain professionals, AI literacy translates to fluency in tools like Microsoft Copilot, warehouse robotics, or AI-powered demand sensing. Whether it involves using generative AI to analyze supplier contracts or leveraging computer vision to monitor fulfillment accuracy, familiarity with intelligent tools is now an integral part of daily work.

Establishing guardrails: The role of AI governance

As AI becomes ubiquitous, clear governance frameworks are indispensable. At the PMBC panel, business leaders expressed concern over the rapid spread of “shadow AI”—when employees use unapproved AI tools without oversight, often risking the exposure of sensitive data. One executive warned, “Without clear governance, shadow AI will become the new shadow IT—risking data leaks and eroding organizational trust.” The message was clear: leadership must be proactive, not reactive, in defining responsible AI use.

This is not hypothetical. In 2023, Samsung banned ChatGPT after employees leaked proprietary code into the chatbot. Similar concerns prompted Apple, JPMorgan, and Verizon to restrict the use of Generative AI internally.

Effective governance involves defining:

• Which AI tools are approved.
• What data may be input or processed.
• Who is accountable for AI-generated decisions.
• How to audit models for bias and transparency.

Leading organizations are formalizing these policies. For instance, IBM launched its AI Ethics Board to guide the global use of AI, emphasizing transparency and trust. Microsoft’s Responsible AI Standard provides detailed principles for developing and deploying AI responsibly—many of which apply to supply chain use cases.

PMBC attendees emphasized that governance should not stifle innovation, but rather enable the responsible use of AI. Providing secure, enterprise-grade AI platforms and clear training enables innovation while safeguarding stakeholder interests.

Building the hybrid workforce: T-shaped skills

As AI augments supply chain work, organizations are shifting focus from purely technical expertise to T-shaped skill sets—deep domain knowledge complemented by broad, cross-functional adaptability.

In practice, this means a demand planner might pair deep forecasting expertise with working knowledge of data modeling or AI interface design. A procurement lead might understand contract analytics and also know how to structure queries for large language models.

The World Economic Forum projects that 39% of core job skills will change by 2030, primarily driven by the impact of AI and big data. The top emerging competencies are analytical thinking, technological literacy (including AI), and adaptability, with upskilling and reskilling as well as the ability to work with intelligent systems deemed critical for both organizations and workers.

Organizations fostering these hybrid skills see tangible benefits. Unilever’s global upskilling initiative trained over 23,000 employees in AI and data tools, resulting in a 25% increase in project efficiency. Similarly, Deloitte’s internal AI academies help teams apply AI across procurement, logistics, and supplier performance.

To support T-shaped development, companies can:

• Encourage job rotation across departments
• Sponsor AI workshops or certifications
• Integrate project-based learning into performance reviews
• Promote cross-functional collaboration between data, operations, and strategy teams

This does not mean every supply chain manager must become a programmer, but understanding how AI generates insights and how to apply them critically is essential.

Academic-industry collaboration: Building the pipeline

To meet future workforce demands, supply chain programs must embed AI into their curricula. Encouragingly, this is already underway. Schools like MIT, Georgia Tech, and Ohio State have launched new courses in AI for supply chain, generative AI applications, and ethical automation.

Ohio State’s Fisher College of Business offers “Generative AI and the Future of Supply Chain,” training professionals to evaluate, implement, and ethically govern GenAI for forecasting, inventory, and contract analysis. The goal is to graduate professionals who can both operate and question intelligent systems.

Meanwhile, companies are collaborating with universities to co-create programs. Thompson  discussed how Blue Yonder partners with the University of Arkansas to integrate real-world AI use cases into classroom instruction. Guest speakers, internship programs, and live case simulations ensure students graduate with skills aligned to market needs.

Professional associations, such as the Association for Supply Chain Management (ASCM) and the Council of Supply Chain Management Professionals (CSCMP), are also incorporating AI modules into their certifications. The CPIM 8.0 update, for instance, includes AI forecasting and data-driven sales and operations planning (S&OP) processes. This ensures mid-career professionals also gain access to current tools and thinking.

Such partnerships represent a mutual investment. Academia gains relevance; industry builds a future-ready talent pipeline.

Preserving the human element

A key theme from the PMBC panel was that as AI automates repetitive tasks, professionals will have greater capacity to focus on creativity, critical thinking, and leadership. By emphasizing empathy, ethics, and purpose, leaders can ensure AI augments—rather than replaces—human potential. As one PMBC panelist said, “The time saved by automation should be invested back into people—mentorship, innovation, and relationships.”

According to the World Economic Forum’s 2025 report, about half of employers plan to reorient their businesses around AI, roughly two-thirds intend to hire talent with specific AI skills, and 40% expect to reduce headcount where tasks can be automated. In supply chains, this could look like:

• Using AI to propose optimal routes, while human managers balance customer needs and ethical considerations.
• Letting AI generate supplier risk scores, while leaders negotiate terms with nuance and relationship-building.

Conclusion: Readiness is a competitive advantage

As supply chains evolve into intelligent ecosystems, the organizations that thrive will be those that cultivate AI literacy, responsible governance, and human adaptability—not in isolation, but in tandem. This transformation cannot rest solely with HR or IT. It demands cross-functional leadership, alignment between academia and industry, and a culture that prizes continuous learning. From the classroom to the warehouse to the boardroom, the real question is not whether AI will change the supply chain, but how ready leaders are to guide that change.

About the author

Corrine Chen is an educator, researcher, and former industry executive with over a decade of hands-on experience in supply chain management, procurement, and innovation. She teaches supply chain management courses at the University of Nebraska Omaha. Corrine’s work bridges academia and practice, with published research, applied projects, and a passion for empowering the next generation of supply chain professionals. She can be reached at [email protected].

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