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    How AI is Redefining Every Supply Chain Role in Your Organization

    AI is redefining every supply chain role in your organization right now.

    Not by eliminating, but by fundamentally changing what work looks like at every level —from coordinators to directors.

    If you're still writing job descriptions that mention "appointment scheduling," "carrier follow-up," and "data entry" as primary responsibilities, you're recruiting for a role that no longer exists. And you're about to face a talent gap that's harder to close than most supply chain leaders realize.

    The New Reality

    For decades, supply chain career progression followed a predictable pattern: graduates would start by learning the ropes through manual, repetitive tasks. Schedule appointments. Follow up with carriers. Enter data into transportation management systems.

    That career progression model is over.

    "AI isn't going to take your job," says Thomas Deakins, EVP of Alliances at Redwood Logistics and adjunct professor at the University of Tennessee, Knoxville. "Someone who knows how to leverage AI might."

    The tasks that used to define entry-level work are either already automated or will be within 12-24 months. Today's supply chain technology stack handles these functions automatically, often more accurately than humans ever could.

    So, if entry-level isn't about learning through repetition anymore, what is it about?

    What Universities Are Teaching Right Now

    Deakins teaches a course on TMS implementation. In one exercise, he gives students a rate file containing 10,000 lines of data with deliberate errors: misspelled cities, invalid addresses, and incorrect formatting. He gives them two minutes to find the anomalies.

    They can't. No human can manually scan 10,000 lines in two minutes.

    Then he shows them the solution: "You could have loaded this into ChatGPT and said, 'Find things that are not cities in the United States.'" The AI flags every error in seconds.

    But the exercise isn't about the technology trick. It's about teaching prompt engineering: the ability to frame problems in ways that AI can solve. It's about understanding data structures well enough to know what "correct" looks like. It's about critical thinking in an AI-augmented environment.

    "Most people use AI like they wouldGoogle," Deakins explains. "If we can really understand how to use GenAI from a prompt engineering standpoint, then we can truly get more out of the data."

    This is the new baseline supply chain professional at every level:

    • Prompt engineering as a core competency

    • Data validation and anomaly detection using AI tools

    • Scenario modeling and simulation-based decision making

    • Critical thinking about when to trust algorithmic outputs

    Today's supply chain students aren't learning todo the tasks. They're learning to validate, optimize, and improve the systems that do the tasks. And they're entering a workforce where many experienced professionals haven't made that same transition.

    The Talent Mismatch Nobody's Addressing

    Here's the uncomfortable reality:

    In 24 months, you'll be hiring supply chain graduates who speak the language of AI fluently. They'll understand prompt engineering. They'll know how to validate AI outputs. They'll think critically about when to trust algorithms and when human judgment matters more.

    Meanwhile, your current team probably doesn't have these skills. Not because they're not capable, but because they were never taught them.

    This isn't about replacing people. But it is about recognizing that a significant talent gap is opening, and it's widening faster than most training programs can address.

    Rachelle Yeingst from JBF Consulting, who led the conversation with Deakins, framed it clearly: "Would it be safe to say that one of the core focuses for the next generation is the ability to critically think? They have all of these tools available, but that doesn't replace the need to critically understand and interpret that data."

    The critical thinking required now is different. It's not just "can you analyze this data?" It's "can you frame the right questions for AI, validate its outputs, and know when to override its recommendations?"

    What to Do Now

    The transformation of supply chain work isn't coming. It's already here. And it affects everyone in your organization.

    • For hiring managers: Ask candidates about their experience with AI tools. Not whether they've used ChatGPT casually, but whether they understand how to use it systematically to solve supply chain problems.

    • For L&D leaders: Don't assume your current team can "figure out AI" on their own. They need structured learning around prompt engineering, data validation, and critical thinking in AI-augmented environments.

    • For operations leaders: Before implementing AI tools, audit your data quality. If your data is messy now, AI will just scale the mess. Do the foundational work first.

    • For executives: Insist on clear ROI measurement frameworks before approving AI investments. Remember: "AI-powered" is the new"blockchain-enabled." It requires scrutiny.

    The Bottom Line

    "Embrace AI, become the expert in your domain, and then leverage AI to help you make better decisions, to be more efficient, work smarter, not harder," Deakins summarizes.

    That's the promise of AI in supply chain: not replacing human expertise but amplifying it. Not eliminating jobs, but redefining what those jobs involve.

    But only if you approach it in the right order: expertise first, AI second.

    Every supply chain role is being redefined right now. Success requires combining deep supply chain knowledge with AI literacy and critical thinking about algorithmic decision-making.

    The companies that recognize this shift now will have a significant competitive advantage. The ones that don't will find themselves struggling to compete with teams that speak a language their organization never learned.

    The talent gap isn't just about hiring.It's about up skilling your existing workforce across all levels before the gap becomes unbridgeable. The question isn't whether your organization will adapt. It's whether you'll adapt fast enough.

    About the Conversation: This article is based on a webinar discussion between Thomas Deakins (EVP of Alliances at Redwood Logistics and Adjunct Professor at the University of Tennessee, Knoxville) and Rachelle Yeingst (JBF Consulting) on AI adoption in supply chain.

    Watch the full conversation

    Connect: Thomas Deakins | Rachelle Yeingst | JBF Consulting