AI Workforce Transformation Not Uniform Across Supply Chain Jobs
Introduction
Artificial intelligence is not just a technological transition; it's a workforce transition. Open source models, large language models, and platforms that can code in most any language have evened the playing field in a number of ways. We've mentioned the AI analytics supply chain ladder in our last podcast, which outlines an approach for all employees and leaders across the workforce to advance their capabilities. But we haven't discussed how AI's impact will be felt. Where, when, and how AI begins to shift the landscape is a topic that needs exploring further. We'll discuss how it will impact things, what individuals and employers can do to adjust, and what to expect next. This actually might be more of an ai workforce transformation than simply a transition. But it could be too soon to tell.
Who Feels AI's Impact First and Why
It's hard to pinpoint exactly when artificial intelligence will fully reshape the supply chain workforce. Progress is clearly accelerating a lot faster than many anticipated. But even so, we're likely years away from seeing AI directly impact more than half of all employees in the field. That's due in part to the structure of the workforce and the nature of the work: a pyramid where change typically reaches leadership and knowledge workers faster than frontline and operational roles. The impact will be greater and faster in bigger companies than in smaller.
The most engaged and ambitious professionals and organizations are the early adopters. They are learning how to apply AI in their work, create value, and climb the analytics ladder faster than their peers. What's changing fast is that the gap between early adopters and everyone else is starting to close because the tools are no longer reserved for big companies or highly technical teams. With open-source models, affordable platforms, and plug-and-play integrations, even smaller organizations can now experiment with AI workforce transformations at low cost.
That shift means companies of all sizes—and their people—will soon be in motion, whether they're ready or not. The spread of AI capability won't be evenly distributed, but the pace is picking up quickly.
Mid Market Advantage and the Entry Level Squeeze
Larger enterprises will likely feel the biggest impact first. They have the resources and scale to operationalize AI across functions. This will lead to these companies restructuring their work and roles and teams. Some will go faster than we would think, and some may regret it, but the risk/reward dynamics are worth it for many. This will lead to job loss and, in many cases high quality talent on the market. Smaller companies may actually offer one of the most important silver linings in this transition. For employers, there's a chance to accelerate their own AI learning curve by hiring professionals who bring experience from more advanced or mature environments. For individuals, especially those displaced by enterprise restructuring, these organizations may become launch pads for broader roles, faster growth, and deeper exposure to AI-influenced decision-making.
Still, one group at real risk is entry-level talent. College graduates coming into the workforce now may find fewer "blue-chip" roles available at large companies. Automation and AI are reshaping many of the structured, repetitive roles that used to serve as stepping stones. Those already in the market and willing to lean into AI have a better shot at adapting and growing in their role with their organization, leveraging their experience as a differentiator. But for new grads, the best bet may be a more intentional approach—targeting midsize companies and frontline operations roles that provide real-world exposure and skill development. As we've noted in related work, these roles build foundational capabilities that are far more likely to endure in an AI-enabled supply chain.
Playbooks for Employers and Professionals
This is not a time for employers or employees to sit still. Adaptation is the only real option to avoid being left behind. A few key actions stand out:
For individuals:
- Upskill now. Learn how to use generative AI, machine learning tools, and decision-support platforms relevant to your function. No matter where you are, this requires ongoing focus on learning
- Rethink your targets. Consider mid-market firms and operational roles where you can learn quickly and contribute broadly.
- Stay networked. The landscape is changing fast, and connections can help you navigate opportunities that aren't yet advertised. Even if you are not looking now, network with an eye on these different paths
For employers:
- Build capability through hiring. Consider bringing in talent from AI-forward companies to infuse new skills and mindsets.
- Pilot and scale AI responsibly. Focus on where AI can enhance—not just replace—human talent in supply chain, logistics, and planning.
- Develop internal pathways. Create space for current employees to learn, experiment, and grow with AI—especially in operations and frontline settings.
What Comes Next
As we think about the bigger picture, here are a few more dynamics to keep in view:
- New roles will emerge, but they're not yet obvious. History suggests new jobs will eventually replace many that are lost, but clear examples haven't yet surfaced at scale. At its foundation, roles are morphing, the best are seeing planners do more planning, and analysts do more planning as AI creates time and reduces waste
- Growth still matters. Without strong economic demand, even the best technology won't prevent job displacement in some sectors. Ensure your AI
- Industry mobility will be critical. Workers who are able and willing to shift sectors may have better long-term prospects than those who stay in stagnant fields.
- Location plays a big role. Urban and innovation-heavy areas may attract investment and jobs, while legacy manufacturing regions may struggle to keep up. Use your location to your advantage if you are in the right place, and create a plan to differentiate if you are in rural areas.
- Hype creates noise. Media attention can exaggerate the short-term impact of AI, making it harder for organizations and workers to separate signal from noise. Find trusted individuals and sources and read their posts as a fast follower.
- Not all AI is the same. There's a significant difference between tools like ChatGPT and the deeply embedded industrial AI that transforms planning, production, and distribution at scale.
- Help finding good talent is there if you need it. A common refrain in a lot of our writings is to engage with supply chain talent professionals who can not only save you time but also work full-time at vetting candidates in a time when Ai can make every resume appear to be a perfect fit for the position for which you're hiring.
Possible Obstacles to Change
AI represents a massive and rapid sea change impacting the supply chain job market in real time. It's hard to think of an analogous occurrence that both limited access to the labor market while throwing open a door to technological advances available to almost anyone. Yes, sounds counterintuitive. But, not everyone is technologically savvy enough to find their entry point on the AI ladder, especially in supply chain. It's long been understood that a large percentage of tenured supply chain employees are of the boomer generation. Their pending exit from the workforce may make them less apt or interested in rapid adoption of artificial intelligence. While their retirements could pull a lot of institutional knowledge from supply chain, it could also open the door to more growth from younger generations looking to access the supply chain talent pipeline.
There are also some questions as to the viability of AI from a sustainable view. The server capacity necessary to support artificial intelligence is massive. A Bloomberg article from May of 2025 found that nearly two-thirds of new data centers built or in development since 2022 are in places that have water supply issues. These capacity issues have been known for a while and don't seem to have slowed the proliferation of AI use or the construction of data centers to support accelerated use. However, it is worth mentioning as a possible obstacle to change and impact on the time frame of full-scale adoption.
Conclusion
In short, the AI era is not just coming—it's already underway. But the impact will unfold unevenly. The choices we make now—about where we work, what we learn, and how we lead—will shape how we thrive in the years ahead. There's still time to climb the analytics ladder. But we all need to get moving. The impacts may not be uniform, but they will occur, regardless. Enterprise-sized budgets will lead the way with early adopters driving change at these firms. However, access is not limited, and a lot of mid-sized firms will also be able to recruit younger, more tech-savvy talent to help drive this shift. An aging supply chain workforce and environmental impacts could slow this shift, but so far, AI is a behemoth with universal access and appeal whose impact will be felt across industries.
