Gartner 2024 Supply Chain Technology Adoption Report: AI Planning Benchmarks

Report Metadata

AI Planning Adoption Rates

Gartner's 2024 data shows a meaningful split between organizations that have moved AI planning tools into production versus those still evaluating or piloting. Across the planning function broadly, roughly 45% of respondents reported at least one AI-augmented planning capability in active production use — up from approximately 31% in the comparable 2022 survey. The remaining respondents were distributed across pilot (24%), evaluation (19%), and no-current-activity (12%) stages.

That headline number masks a significant function-level spread. Demand forecasting and demand sensing are the most deployed AI planning capabilities by a wide margin. Autonomous replenishment and AI-driven S&OP scenario generation lag considerably, with most deployments still confined to limited-scope pilots.

Deployment Maturity Distribution

Gartner segments deployment maturity into three tiers in this report: Augmented (AI improves planner decisions but planners remain in control), Assisted Autonomous (AI executes within defined parameters with exception-based human oversight), and Fully Autonomous (AI executes without routine human review). The distribution among organizations with at least one AI planning tool in production:

• Augmented tier: 67% of production deployments. Planners review AI-generated forecasts or recommendations before execution. This is the dominant pattern across demand forecasting and inventory optimization.
• Assisted Autonomous tier: 27% of production deployments. Common in replenishment for fast-moving, low-variance SKUs where exception thresholds are well-defined. Less common in new product introduction or highly seasonal demand patterns.
• Fully Autonomous tier: 6% of production deployments. Concentrated in narrow, high-volume, low-complexity replenishment scenarios — typically commodities or MRO. Not observed in demand forecasting or S&OP contexts in this survey population.

The 6% fully autonomous figure is consistent with governance constraints rather than capability limits. Several Gartner analyst notes from 2024 specifically flag that finance and audit stakeholders are the primary blockers of autonomous execution in procurement-adjacent planning decisions, not the underlying model performance.

Investment Intent: 12-Month Forward View

Respondents were asked about planned investment increases across AI planning capability areas over the 12 months following the survey. The data reflects stated intent, not confirmed budget allocation — Gartner's own commentary flags that stated investment intent historically runs 15–20% above actual spend in their supply chain surveys.

Demand forecasting AI leads investment intent by a significant margin. Supply and procurement planning AI trail, which aligns with the lower production deployment rates in those functions — organizations are not yet investing in capabilities they have not validated at the demand planning layer.

Top Adoption Barriers

Gartner asked respondents to rank their top three barriers to expanding AI planning adoption. The ranking below reflects first-choice selections; respondents could identify different barriers for different capability areas, so the totals do not sum to 100%.

Year-over-Year Trend (2022 → 2024)

Gartner has run comparable supply chain technology adoption surveys since at least 2020. The 2022-to-2024 comparison is the most direct given methodology alignment. Key directional changes:

• Production deployment of AI demand forecasting rose from ~38% to ~58% — the largest two-year gain of any planning capability tracked.
• Governance and accountability as a barrier increased from 24% to 38% between 2022 and 2024. Gartner attributes this to organizations moving from pilot to production and encountering real audit and accountability questions for the first time.
• Vendor lock-in concern nearly doubled (16% → 29%), consistent with organizations that have now lived through one or two vendor platform migrations and are more cautious about proprietary data models.
• S&OP / IBP AI scenario tool production deployment remained low (22%) despite high investment intent in prior years, suggesting the capability is harder to operationalize than demand forecasting despite vendor claims.

Maturity Segmentation by Company Size

Gartner breaks out results by revenue band in several sections of the report. The split between enterprise (>$5B revenue) and mid-market ($500M–$5B) is the most pronounced:

Mid-market organizations face a compounding problem: higher data quality deficits and less internal talent to address them, which explains why their production deployment rates are roughly 20–25 percentage points below enterprise peers despite similar stated investment intent.

Methodological Limitations of This Record

How to Use This Record

This benchmark record is most useful for two tasks: (1) positioning your organization's current AI planning deployment stage relative to peer data, and (2) identifying which barriers are common enough to plan for rather than treat as exceptional.

If your organization is in the evaluation or pilot stage for demand forecasting AI, the 58% production rate among survey respondents is a useful reference — but the more actionable data point is that data quality and ERP integration complexity are the barriers that have actually delayed or blocked deployments, not model performance or vendor capability gaps.

For S&OP and IBP scenario AI specifically, the gap between high investment intent and low production deployment (22%) is a signal worth examining before committing budget. The Gartner qualitative commentary points to organizational process maturity — specifically, whether the S&OP process itself is disciplined enough to consume AI-generated scenarios — as the primary constraint, ahead of technology readiness.