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Respondents who considered their workforce ready reported much stronger results. But the survey does not show that readiness caused success.
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A report from the Association for Supply Chain Management and the Digital Supply Chain Institute suggests workforce readiness is a major divide in supply chain AI deployment, but its survey data cannot show that readiness causes project success. Adoption remains early: respondents averaged 1.94 on a five-point scale across 13 functions, and roughly two-thirds of those who said their workforce was unready were still planning or piloting. For operators, the findings point to skills, process quality and accountable decision-making as constraints to address before expanding AI authority.
Demand planning had the highest adoption score at 2.28; returns and reverse logistics ranked lowest at 1.59.
Skills and resources and an unclear business case or ROI each topped the barriers list at 33%; poor data quality was cited by 25%.
Forty-six percent expected faster decisions and shorter planning cycles to deliver the biggest payoff over the next two years; these are forecasts, not measured gains.
Supply chain organizations that consider their workers ready for AI report far more successful projects than those that do not: 71% versus 28%. Yet 83% of surveyed professionals say their employees are unprepared to use AI effectively. The findings, detailed by FreightWaves on October 23, put workforce capability alongside the technology itself as a deployment concern.
The Association for Supply Chain Management (ASCM) and the Digital Supply Chain Institute released The People Paradox in Supply Chain AI Integration on October 14, 2026. Its analysis used 107 qualifying responses from 144 collected.
Workforce readiness is self-reported, and the survey cannot establish which causes which: readiness or project success. As MarketScale noted in its October 25 coverage, other differences, such as cleaner data or larger budgets, could also help explain the association.
About 64% of respondents who did not consider their workforce ready remained in the pilot or planning stage. AI adoption averaged 1.94 across 13 supply chain functions on a five-point scale: 1 meant no AI use, while 5 meant autonomous execution without human review.
Demand planning led at 2.28, followed by supply and inventory planning at 2.12. Returns and reverse logistics ranked last at 1.59.
ASCM CEO Abe Eshkenazi told FreightWaves that AI could handle routine transactions within defined limits. People should retain higher-risk decisions about customer commitments, sourcing and major disruptions, he said. Greater AI authority requires confidence that he said is currently lacking.
Eshkenazi rejected headcount reduction as too simple a measure of AI’s return. He instead recommended tracking response time: how quickly a business recognizes a disruption, considers alternatives and acts.
Among respondents, 46% anticipated the biggest payoff over the next two years from faster decisions and shorter planning cycles. Those are expected benefits, not measured gains. Eshkenazi also warned that technology cannot repair poor processes or bad data.
Only 7% of respondents expected AI to replace workers directly, while 51% expected people to move toward higher-value decision-making.
Building reports and working through spreadsheets gave supply chain professionals practice spotting exceptions and questioning whether numbers made sense, Eshkenazi said. He warned that automating those tasks can push people into decision-making roles before they understand the fundamentals.
His starting recommendation: choose one high-value process, such as a customer process, supplier or route, and involve the people who run it. Establish the starting point and measurable outcomes before expanding.
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