IN Brief:
- Gartner surveyed 303 supply chain leaders between 5 May and 11 June 2026.
- Forty-seven per cent reported AI-linked workforce reductions averaging 10.5% over two years.
- Sixty-six per cent of respondents reported headcount reductions concentrated at entry level.
Gartner reports that 47% of surveyed supply chain leaders said their organisations had reduced headcount over the preceding two years, primarily because of AI, with cuts averaging 10.5% among those reporting reductions. The study draws on responses from 303 people involved in their organisation’s AI strategy across North America, Western Europe, Asia-Pacific and Latin America. These are survey responses from organisations already using AI, not verified payroll records or a representative measure of employment across every supply chain business.
Entry-level roles account for a disproportionate share of the reported contraction. Gartner says 66% of supply chain leaders surveyed indicated that headcount reductions were concentrated at that level, more than twice the proportion reporting reductions at other levels. That does not mean two-thirds of entry-level supply chain jobs have disappeared. The percentage describes respondents identifying where workforce changes are occurring, and it should not be presented as a sector-wide employment rate.
The survey was conducted between 5 May and 11 June 2026. All participants represented organisations that had integrated AI into workflows to some extent and had visibility of, or influence over, the AI strategy affecting those processes. This selection makes the results relevant to organisations using the technology, but it cannot measure the prevalence of AI-related reductions among businesses without AI deployments. The data cannot independently isolate AI from other influences on staffing decisions such as business volumes, cost pressure and organisational restructuring. Respondent selection and the absence of a matched control group limit any claim that the figures represent a causal estimate of AI’s net effect on industry employment. Gartner explicitly reports what supply chain leaders attributed to AI.
The operational changes behind AI adoption differ across supply chain functions. Forecasting systems can produce demand estimates or identify inventory anomalies, while procurement and logistics tools can consolidate records, surface exceptions and support routine decisions. These capabilities may reduce manual processing where work is repetitive and well defined. They can also create new review, data-quality and governance tasks, particularly where the cost of an incorrect purchasing or transport decision is substantial. Gartner’s survey does not provide a job-by-job breakdown that would support a quantified allocation of reductions across those functions.
The contraction at the lower end of the workforce presents a succession problem. Supply chain teams have traditionally developed experienced planners, procurement specialists and operations managers from colleagues who first worked in transaction processing, coordination and support. If those entry points narrow, there may be fewer employees with direct experience of supplier problems, warehouse constraints and practical operating decisions. Management positions still require judgement built from such work even where their future systems use more automation.
Gartner identifies a subgroup of high-performing supply chains, defined as the top quarter of respondents on AI integration, AI-enabled automation and business outcomes. In that subgroup, entry-level work is being redesigned towards more domain knowledge, data analysis and consequential decision-making instead of remaining centred on repetitive tasks. Those characteristics describe a correlation within Gartner’s surveyed organisations, not an experimental finding that redesigning junior roles causes better business results.
The analyst firm recommends pairing technically capable junior employees with experienced colleagues, making AI use visible to managers and assigning responsibility for automated outputs. Testing assumptions and input data is particularly important in supply chain operations because supplier lead times, transport constraints and inventory records change. A recommendation generated from stale stock information can cause a purchasing or allocation error even if the software processes its model correctly. Human oversight is therefore an operational control, not simply a training exercise.
Earlier Gartner research provides context but uses different samples and questions. A February 2026 survey found that 55% of respondents expected agentic AI to reduce the need for entry-level hiring; the October results concern staffing reductions already reported over the previous two years. A June analysis of job advertisements also identified rising demand for supply chain roles requiring AI skills. Expectations, reported organisational changes and vacancies measure different outcomes and should not be combined into a single employment trend.
The findings concern reported staffing changes in organisations already deploying AI. They do not establish economy-wide job losses, the net employment effect of automation, or how many logistics, procurement and warehousing roles will exist in later years. Gartner’s practical concern is that reducing entry-level positions without redesigning the remaining roles and development routes could leave companies short of people capable of supervising automated systems and managing complex supply decisions.
