Unlock the Full Potential of Supply Chain Talent With AI Upskilling

• 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?

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.

• 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.

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