Supply-chain AI adoption is rising, but fragmented data is limiting measurable value

New research from Inspectorio shows that active use of supply-chain AI among retail supply-chain leaders rose to 40% in 2026, compared with 24% in 2024. Adoption is accelerating, but measurable business impact is not keeping pace.

The main barriers are organizational rather than technological. Inspectorio identifies budget conflicts, integration complexity and limited change-management capacity among the leading constraints. DC Velocity also highlights poor data quality, fragmented systems and weak cross-functional integration as recurring reasons AI initiatives fail to deliver expected results.

The report connects AI adoption with wider challenges in traceability, compliance, sourcing and product integrity. Only 21% of organizations report a holistic, multi-tier traceability strategy, while most remain reactive to regulatory requirements. Compliance pressure is increasing even as the proportion of respondents reporting larger compliance budgets fell from 75% in 2025 to 50% in 2026.

Tariff-driven sourcing changes add another layer of risk. When organizations move production to new suppliers or countries, they may lose sustainability data, approved processes and compliance infrastructure built in the previous network. The operational cost of changing geography can therefore extend well beyond unit price or tariff exposure.

Operational perspective: AI value depends on a connected decision foundation. Product, supplier, purchase-order, quality, compliance and traceability data must be aligned before models can reliably support sourcing or product-integrity decisions.

Organizations should define which decisions AI supports, which data is authoritative, who validates outputs and how recommendations are executed across procurement, quality, compliance and supply-chain teams. A model deployed over disconnected data may increase activity without improving control.