Unlock the Full Potential of Supply Chain Talent With AI Upskilling

The future of operational excellence depends on AI-ready talent. By upskilling their teams, supply chain leaders secure resilience and accelerate growth. 

To excel, supply chain talent must make the shift from AI openness to AI action

In a recent Gartner survey, supply chain workers whose organizations have deployed AI in the supply chain in the past year revealed that while 94% of respondents are open to using AI, only 36% know how to integrate it into their workflows. 

While the vision for AI in the supply chain is vast, the reality is that employees are unlikely to learn AI on their own. Supply chain leaders who proactively address the AI skills gap will drive greater efficiency, innovation and business results.

For more practical actions supply chain leaders can take now to lead an AI-powered function, join the complimentary webinar: Building AI as the Foundation for Supply Chain Competitive Advantage

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    Build an AI-ready workforce to ensure supply chain’s ability to compete and adapt

    Establishing AI literacy throughout the supply chain gives your workforce a shared language for collaboration and excellence. It is important to create an AI upskilling environment that moves the needle on business performance by taking the following actions:

    Build an AI upskilling strategy that delivers business results

    • Address the “What’s in it for me?” for employees. Communicate that AI upskilling isn’t just an option, it’s a necessity for those who want to remain competitive and future-proof their careers. The opportunity costs of inaction are twofold: The enterprise risks being outpaced by competition, missing out on efficiency gains and struggling to sustain performance. Employees risk missing out on career advancement, losing relevance and jeopardizing both individual and organizational success.

    • Define what success looks like. Help your organization lean into change by focusing on outcomes, such as increased productivity, internal mobility, retention and employee engagement. Then tie these outcomes to business strategy and supply chain goals..

    • Align AI learning with business needs to achieve the selected outcomes. Bring clarity and direction to the AI upskilling journey and pressure-test your strategic alignment by asking the following:

      • Does my AI upskilling strategy align seamlessly with overall business goals?

      • Will AI upskilling advance our supply chain goals?

      • Does the strategy emphasize both the technical skills and the soft skills needed to grow and innovate?

    Establish AI literacy learning pathways

    AI upskilling helps employees understand what AI is, how it works at a high level and how it impacts their work and industry. An effective AI learning pathway should accomplish the following: 

    • Upskill AI literacy for all, coding for a few. At a high level, successful AI upskilling means employees can use AI effectively, safely and adaptively in their jobs. Only a small percentage of the workforce (data scientists, data engineers or advanced AI users) will need to learn coding or building AI agents. The rest will simply need to know how to work alongside AI tools (such as chatbots, automation or AI-driven analytics), how AI can impact workflow or decision making, and how to interpret AI outputs with confidence and ease.

    • Define learner personas to meet various AI skill needs. Once you’ve determined the levels of upskilling needed for various learners, create personas that address specific motivations, skill levels, challenges and learning styles. Then tailor AI training to these personas for real impact. For example:

      • An “AI explorer” might be a frontline worker who needs to learn the basics of AI to stay relevant and feel confident using AI tools in their day-to-day role.

      • An “AI strategist” might be a leader of supply chain AI transformation initiatives who needs training to develop and champion a vision for AI adoption.

    • Embed AI adoption in workflows. Knowledge alone doesn’t create skills or drive strong performance. Real improvement comes from practice and hands-on experience. Prioritize opportunities for employees to apply their newfound AI skills on the job — for example, by encouraging them to use GenAI to draft performance review summaries or to share AI-generated insights from meeting notes.

    Make AI upskilling stick

    Investing in AI isn’t just a “shift” but rather a “lift” in changing ways of working. Change doesn’t happen overnight — but in today’s unpredictable supply chain environment, agility is essential. The following are some ways to build long-term talent agility and help your organization thrive.

    • Foster agility through accountability. Leaders who consistently demonstrate desired behaviors turn abstract change initiatives into visible, credible actions. This builds trust, sets the tone and makes it easier for everyone to follow — making changes stick.

      Leaders could also model accountability by sharing personal experiences using AI tools. This could look like AI upskilling in personal development plans, and actively participating in learning events or pilot programs alongside teams.

      Set manager expectations to build learners’ psychological safety. Leaders must buy into the vision of AI upskilling to ensure their employees will take it seriously. Equip managers appropriately by giving them the resources and tools they need to:

      • Prioritize when there is a time conflict

      • Manage up and level-set expectations

      • Measure skills proficiency and provide actionable feedback

      • Support individualized learning paths to meet diverse needs

    What to do next

    By virtue of its rich data assets and highly repeatable processes, the supply chain is inevitably moving toward an AI-driven future. Leading CSCOs will pursue a two-pronged approach by delivering AI-enabled value today through narrow, practical use cases while also building the blueprints that establish the foundations for tomorrow’s AI-driven supply chain. This is critical to delivering on the mission critical priority of architecting the AI driven supply chain. 

    The steps in that journey include:

    • Becoming part of the enterprise‑level AI conversation by overcoming peers’ underestimation of supply chain’s strategic role through increased AI literacy, stronger cross‑functional competencies and partnerships that drive joint‑win deployments.

    • Building a supply chain AI roadmap that balances long‑term transformation and near‑term ROI by assessing AI maturity, identifying and prioritizing use cases, anticipating risks and aligning investments to measurable outcomes.

    • Building an AI‑ready workforce by preparing leaders and frontline teams for the uneven impact of AI across functions, redesigning workflows, roles and organizational structures, and linking upskilling, hiring and partnering to supply chain business needs.

    • Developing an AI‑enabled trading partner ecosystem through shared data frameworks, integrated workflows and transparent governance that supports trust, collaboration and joint value creation across the end‑to‑end network.

    • Enabling AI‑driven workflow and process change by understanding how AI transforms business processes, decision making and operating models through decision augmentation and automation.

    • Establishing a robust data foundation for AI by applying AI‑powered tools to improve data quality, adopting modern architectures such as lakehouses and data fabrics, and using natural‑language interfaces to accelerate insight generation and decision support.

    • Reconciling the desired AI future state with the existing supply chain technology stack by defining integration strategies, evaluating vendor capabilities and making informed build‑versus‑buy decisions across plan, source, make and deliver.

    For more on how Gartner helps drive success on this and other mission critical priorities for CFOs, speak to us today.

    AI upskilling FAQs

    What are the advantages of AI upskilling in the supply chain?

    AI upskilling in the supply chain helps employees and leaders gain the knowledge and skills to use AI in their work. This allows them to improve operations, solve problems and make better decisions with the help of AI. Success could look like employees using AI-driven insights for decision making or using AI to solve business problems.

    What are the key components of AI literacy?

    AI literacy is the ability to effectively and responsibly use AI in a business, societal and role-based context. It includes understanding the fundamentals of AI, technology and applications; analytical and algorithmic methods at a high level; data and knowledge sources; risks; and ethical considerations.

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