For years, the conversation about technology in supply chain has focused on what machines can do better than people. Automation could move products faster. Analytics could improve forecasting. Digital platforms could provide greater visibility. Now, artificial intelligence is promising to accelerate nearly every part of the operation.
But as AI becomes more capable, an interesting shift is taking place: the skills employers value are not becoming exclusively more technical. Human capabilities are becoming more important alongside them.
That is particularly relevant for supply chain leaders. The industry is adopting increasingly sophisticated technology while operating in an environment where disruption, uncertainty and competing priorities have become routine. AI can process enormous amounts of information, identify patterns and automate repetitive work. It cannot eliminate the need for people who understand the business context behind that information or who can determine what to do when conditions change.
The next workforce challenge, then, is not choosing between human talent and technology. It is developing people who can make technology more useful.
The skills behind AI adoption
Recent workforce data illustrates this changing equation. The Q3 2026 Experis Tech Talent Outlook found AI modeling and application development to be the most sought-after technical capability, cited by 34% of employers. AI literacy followed at 30%.
But the human skills employers prioritized are just as revealing.
Communication, collaboration and teamwork ranked as the most critical human capability, cited by 41% of employers. Professionalism and work ethic followed at 37%, while 34% pointed to adaptability and willingness to learn.
That combination matters. Employers are not simply looking for people who understand AI. They need people who can work across functions, communicate what technology is telling them, challenge outputs when necessary and adjust as both technology and business conditions evolve.
Those capabilities are especially important in supply chain, where decisions rarely exist in isolation.
A forecasting change can affect procurement, production, inventory and logistics. A supplier disruption can quickly become a customer problem. A transportation delay may require coordination across operations, sales and finance. Technology can help identify the issue faster, but resolving it still requires people to interpret information, understand tradeoffs and coordinate a response.
AI literacy is becoming a workforce skill
One of the biggest mistakes organizations can make is treating AI knowledge as something needed only by IT teams, data scientists or technology specialists.
As AI becomes embedded into planning, sourcing, manufacturing, warehousing and logistics platforms, more employees will interact with it as part of their everyday jobs. They do not all need to become AI developers. They do need enough AI literacy to understand what the technology can and cannot do.
That means knowing how to ask better questions of AI systems, recognize questionable outputs and understand when human judgment should override a recommendation. It also means understanding the data behind AI-driven decisions.
Consider demand planning. An AI system may identify a shift in purchasing patterns and recommend adjusting inventory. But an experienced employee may know that a temporary promotion, unusual weather event or customer behavior is distorting the data.
The value comes from combining the system's ability to identify patterns with the employee's understanding of context.
This is why AI literacy should increasingly be viewed in the same way organizations once approached digital literacy. It is becoming a baseline capability across functions rather than a specialized technical skill.
The human skills paradox
There is a paradox emerging as workplaces become more technologically sophisticated: the more organizations automate, the more valuable distinctly human capabilities can become.
When technology handles more routine analysis and administrative work, employees are left with decisions that tend to be more ambiguous and consequential.
Should the company switch suppliers when the lowest-cost alternative carries greater geopolitical risk? Should an AI-generated forecast override the judgment of a planner who has worked with a customer for a decade? How should procurement explain a sourcing decision to operations when the cheapest option is not the most resilient one?
These are not purely technical questions.
They require communication, judgment, collaboration and the ability to weigh competing priorities. They also require employees who are comfortable making decisions in situations where there may not be one objectively correct answer.
This helps explain why communication and teamwork remain so highly valued even as AI capabilities climb employers' skills lists.
Stop looking for the perfect hire
The changing skills equation also has implications for how supply chain organizations address talent shortages.
Trying to hire employees who arrive with every technical capability and every human skill an organization needs is increasingly unrealistic. Technology is changing too quickly, and many of the roles organizations will need several years from now are still evolving.
Employers appear to recognize this. The Experis research found that 95% of employers are using some combination of strategies to address talent scarcity, with upskilling and reskilling existing employees the most commonly cited approach.
For supply chain organizations, that means workforce development should increasingly happen alongside technology investment.
When a company introduces a new AI-enabled planning system, for example, training cannot stop with teaching employees how to use the software. Organizations should also help workers understand how their roles are changing because of it.
What decisions will technology make? Which decisions remain with employees? What new information will workers have access to? How should they challenge an AI recommendation? How will teams collaborate differently when routine tasks are automated?
Answering those questions turns technology implementation into workforce transformation.
Build teams around complementary strengths
Supply chain leaders should also reconsider how they think about the composition of teams.
The objective does not have to be finding every capability in every employee. Strong teams can combine different strengths.
One employee may bring deep operational knowledge. Another may have advanced analytical or AI skills. Someone else may excel at supplier relationships, communication or cross-functional coordination.
The goal is to build teams where those capabilities reinforce one another.
That approach also creates more pathways for existing workers. A strong supply chain professional does not necessarily need to become a data scientist to remain valuable in an AI-enabled workplace. Someone who understands operations deeply and develops enough AI literacy to work effectively with new tools may become more valuable, not less.
Likewise, a highly technical employee who learns to communicate insights clearly and understand operational realities can have a greater impact than someone with technical expertise alone.
The competitive advantage is the combination
There will always be a temptation to view new technology through the lens of replacement: What jobs can it eliminate? How many tasks can it automate? How much labor can it remove?
That framing misses a larger opportunity.
The organizations that gain the most from AI will be those that redesign work around what people and technology each do best. AI can provide speed, scale and pattern recognition. People provide context, judgment, creativity, relationships and accountability.
Supply chains need both.
As technology becomes more accessible, simply having AI will become less of a differentiator. Many organizations will eventually have access to similar tools and capabilities.
The greater competitive advantage will come from the workforce surrounding those tools: people who know how to use AI, when to trust it, when to question it and how to turn its insights into better decisions.
The future of supply chain talent is therefore neither human nor technological. It belongs to organizations that become better at combining the two.
