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Key developments:
- Executives are increasingly turning to AI to turn fragmented operational data into faster, more visible decisions, a shift driven by rising pressure to predict disruptions and manage costs.
- McKinsey’s 2024 survey shows 90% of senior supply‑chain leaders faced challenges last year, yet a 2025 follow‑up reveals only 19% have AI tools deployed at scale, indicating a gap between ambition and implementation.
- AI‑enhanced demand forecasting is improving stock placement and replenishment decisions, helping firms balance inventory costs against service levels.
- Gartner warns that by 2030 merely 5% of firms will automate at least a tenth of their planning decisions, underscoring the need for solid data, talent and governance foundations before broader rollout.
Why it matters:
- Better visibility and early‑warning analytics can reduce costly disruption cascades, turning reactive responses into proactive mitigation.
- The disparity between pilot projects and full‑scale deployment suggests many companies risk overspending on AI without gaining operational benefits.
- Supply‑chain planners must shift from data‑entry roles to "plan orchestrators," integrating AI insights while retaining human judgement for strategic network design.
What to watch next:
- Progress on closing the deployment gap: will the proportion of firms moving beyond pilots rise above the current 19%?
- Gartner‑focused readiness initiatives: will more organizations invest in data quality, decision‑ownership frameworks, and upskilling to meet the 5% automation benchmark?
- Emerging AI‑driven collaboration platforms that embed agents directly into ERP and logistics systems, potentially accelerating the move from visibility to autonomous, AI‑orchestrated supply chains. [[1, 2]]