AI is giving supply chains faster analysis, broader visibility and greater automation. The workforce question is becoming more demanding as companies need people who can interpret those outputs, challenge weak recommendations and coordinate decisions across functions when the data does not provide a clear answer.
AI Skills Need Human Skills Beside Them
The expansion of artificial intelligence across planning, sourcing, manufacturing, warehousing and logistics is changing the skills required to run supply chains effectively. Technical capability matters, but workforce demand suggests employers are placing substantial weight on communication, adaptability and collaboration as technology becomes more deeply embedded in work.
The Q3 2026 Experis Tech Talent Outlook found AI modeling and application development was the most sought-after technical capability, cited by 34% of employers. AI literacy followed at 30%.
The human skills identified in the research were equally significant. Communication, collaboration and teamwork ranked highest at 41%, followed by professionalism and work ethic at 37%. Adaptability and willingness to learn were cited by 34%.
The combination reflects how AI actually enters a supply chain. A forecasting system can detect changing demand. A sourcing platform can surface supplier risks. Analytics can identify inventory imbalances. But those signals frequently cross organizational boundaries before a decision can be made.
A forecast revision can alter purchasing requirements, production schedules, inventory positions and transportation capacity. A supplier problem can affect manufacturing and customer service. An inventory recommendation may improve availability while increasing working capital.
AI can accelerate the analysis behind these decisions, but employees still have to understand the tradeoffs and coordinate the response.
That places greater value on people who can translate analytical outputs into decisions that other functions can understand and act on.
AI Literacy Becomes Part of Supply Chain Literacy
Treating AI capability primarily as the responsibility of technology teams risks creating a gap between the systems companies deploy and the people expected to use their recommendations.
Employees do not need to become AI developers. They do need enough knowledge to understand how an AI-supported recommendation was produced, what information influenced it and where its limitations may lie.
Demand planning illustrates the distinction. A model may detect a change in purchasing patterns and recommend an inventory adjustment. A planner may know that a promotion, unusual weather event or temporary change in customer behavior is influencing the data.
Neither source of information is automatically sufficient on its own. The quality of the decision depends on whether the organization can combine pattern recognition with commercial and operational context.
That makes AI literacy increasingly relevant to everyday supply chain work. Employees need to know how to frame questions, assess outputs, identify questionable recommendations and recognize situations where additional evidence or human review is warranted.
Data literacy matters as well. AI recommendations inherit weaknesses in the information used to produce them. Inconsistent master data, missing supplier information or poorly maintained product attributes can make sophisticated models less useful regardless of their technical capability.
As routine analysis becomes easier to automate, the work remaining with people can also become harder.
A system can compare suppliers across cost, lead time and performance data. Deciding whether a lower-cost source justifies greater geopolitical, quality or continuity risk involves competing objectives that may not reduce neatly to one metric.
Similar tensions exist in planning. An algorithm may recommend reducing inventory based on forecast demand while commercial teams expect an upcoming customer opportunity. Procurement may identify savings that create longer lead times. Logistics may optimize transportation costs at the expense of responsiveness.
These decisions require people who can understand multiple objectives and explain why one tradeoff deserves priority.
Training Has to Follow Technology Investment
The speed of technological change also makes hiring every required capability increasingly difficult.
The Experis research found 95% of employers were using some combination of approaches to address talent scarcity, with upskilling and reskilling existing employees the most commonly cited strategy.
That has a practical consequence for supply chain technology programs. Training employees to navigate a new AI-enabled application addresses only part of the requirement.
Teams also need clarity about how decision rights change when AI enters a process.
Organizations need to determine which recommendations can be executed automatically, which require human approval and which conditions should trigger escalation. Employees need to understand when they are expected to challenge a recommendation rather than simply accept it.
Without those boundaries, automation can create ambiguity rather than remove it. Workers may defer excessively to system recommendations, duplicate analysis manually or continue using old processes alongside the new technology.
Workforce development therefore needs to accompany process redesign rather than follow software deployment as a separate exercise.
The same principle applies to team composition. Not every employee needs identical capabilities.
One person may contribute deep knowledge of manufacturing or logistics. Another may have stronger analytical skills. Others may understand supplier relationships, customer behavior or cross-functional coordination.
Combining those strengths can be more useful than expecting every employee to possess advanced technical expertise alongside years of domain knowledge.
Experienced supply chain professionals can remain highly valuable by developing sufficient AI literacy to interrogate new tools effectively. Technical specialists can increase their contribution by understanding the operational context surrounding the data they analyze.
Experience May Become More Valuable With AI
AI could make institutional knowledge easier to apply rather than less relevant. Experienced planners, buyers and operators often recognize supplier behavior, demand anomalies and process constraints that are poorly captured in structured data. Companies that find ways to combine that knowledge with AI systems can improve the context behind recommendations while reducing the risk that valuable expertise disappears when experienced employees leave. That makes knowledge capture an increasingly important part of workforce and AI investment.
