Supply Chain Technology & Workforce
Supply-chain AI projects stall when workforce readiness and governance lag
· Source: Association for Supply Chain Management
New ASCM and Digital Supply Chain Institute research found that 83% of surveyed supply-chain leaders considered their employees unprepared to use AI effectively. Organizations describing their workforce as ready reported a 71% project-success rate, compared with 28% among the rest, but the small 107-response analytical sample makes the results directional rather than a market census.
Summary and practical context by RoadHouse Recruiting · Reviewed
Adoption remains concentrated in planning rather than execution
The study scored AI use across 13 supply-chain functions on a five-point scale, where five represented autonomous execution without human review. The average was 1.94. Demand planning led at 2.28 and supply and inventory planning followed at 2.12, while returns and reverse logistics ranked last at 1.59. FreightWaves reported that many organizations are still using AI mainly to generate analysis instead of allowing it to make routine operational decisions.
Skills, business cases and data quality were the leading obstacles
Workforce skills and resources and an unclear business case or return on investment each drew 33% of responses as top barriers; poor data quality followed at 25%. The report also found a steep participation gap below senior leadership: 82% of C-suite respondents said they lead, implement or scale AI initiatives, while only 12% of directors said they lead directly and 70% of staff said they were merely aware of the work.
Connected freight systems raise cybersecurity and accountability questions
Ninety-two percent of respondents expressed significant concern about cyber exposure through AI-enabled suppliers and trade partners, while the average organization reported using 2.3 of six standard safeguards. ASCM's practical recommendation is to start with one high-value process, involve the people who run it, establish measurable outcomes and keep a named person accountable for decisions. For brokers, carriers and shippers, that means defining human approval thresholds and exception handling before an AI tool can award freight, alter routes or communicate commitments.
Understanding the update
ASCM and DSCI released the underlying report October 14; October 23 reporting added a detailed interview and operational interpretation. The survey collected 144 responses, of which 107 qualified for analysis, and only 11 respondents were C-suite executives. The job-level findings are explicitly directional, and the reported success rates are self-reported associations rather than proof that workforce preparation alone caused better outcomes.
What it means for drivers and carriers
Freight operators evaluating AI should choose one measurable workflow, clean the underlying data, train dispatchers and planners, document cyber controls and assign a human owner before scaling. A polished demo or broad automation promise is not a substitute for an auditable operating process.
Questions to consider
- Which freight decision can be tested with a clear baseline for cost, speed and service quality?
- Who can approve exceptions or reverse an AI-generated carrier, route or inventory decision?
- What access, segmentation and incident-response controls protect connected carrier and supplier data?
Read the original reporting
This page provides an original summary and practical commentary. The linked source contains the full reporting. Older stories reflect information available on their published dates.
Read the source at Association for Supply Chain Management ↗ (opens in a new tab)