Study Metadata
| Field | Value |
|---|---|
| Publisher | Forrester Research |
| Report type | Primary survey benchmark |
| Survey cycle | 2024 (fielded Q2–Q3 2024) |
| Respondent profile | Supply chain, operations, and technology decision-makers at enterprise organizations |
| Geographic scope | North America, Western Europe, Asia-Pacific (weighted toward NA/WE) |
| Minimum company size | Organizations with >$500M annual revenue (enterprise segment) |
| Primary topics | AI investment intent, current adoption state, deployment maturity, implementation barriers |
| Methodology disclosure | Partial — sampling frame and weighting methodology not publicly released in full |
Adoption Rate Findings by Function
Forrester's 2024 benchmark segmented AI adoption across five primary supply chain functions. The headline finding is that reported adoption varies significantly by function — demand planning and warehouse operations lead, while procurement AI and logistics optimization trail by a measurable margin. The gap between "piloting" and "in production at scale" is pronounced across all functions.
| SCM Function | Actively piloting or in production (%) | Evaluating / planning within 12 months (%) | No current plans (%) |
|---|---|---|---|
| Demand planning / forecasting | ~58% | ~24% | ~18% |
| Warehouse operations / automation | ~54% | ~27% | ~19% |
| Inventory optimization | ~47% | ~30% | ~23% |
| Procurement / sourcing | ~38% | ~33% | ~29% |
| Logistics / transportation optimization | ~35% | ~31% | ~34% |
Investment Intent Data
The investment intent section of the Forrester benchmark is arguably more operationally useful than the adoption-state snapshot, because it captures where budget allocation is heading over a 12–18 month horizon rather than reflecting a point-in-time self-assessment.
Forrester's 2024 data shows that supply chain AI investment intent is holding or increasing across all functions compared to the prior year wave, with the sharpest uptick in procurement AI and control tower / visibility applications. Demand planning AI, which already shows the highest current adoption, shows the most moderate investment intent growth — consistent with a function where early adopters have already committed budget and are now focused on scaling rather than initial deployment.
- Procurement AI investment intent: highest year-over-year increase in the 2024 wave, with roughly one-third of respondents reporting planned budget increases specifically for AI-assisted sourcing and supplier risk tools.
- Control tower / supply chain visibility: strong investment intent signal, driven in part by disruption events in 2023–2024 that exposed gaps in real-time inventory and logistics visibility.
- Demand planning AI: investment intent is stable but not accelerating — the function has the highest existing adoption, and incremental investment is shifting toward model governance and integration depth rather than net-new tooling.
- Warehouse robotics and automation: investment intent remains high in absolute terms but growth rate has moderated compared to the 2022–2023 period, reflecting capital constraints and longer hardware procurement cycles.
- Agentic AI for supply chain decisions: Forrester's 2024 wave includes an early-stage signal category for autonomous or semi-autonomous decision AI; roughly 18% of respondents report active evaluation, with most activity concentrated in inventory replenishment and freight procurement use cases.
Deployment Maturity Distribution
Forrester uses a four-tier maturity model in this benchmark: Exploring, Piloting, Scaling, and Optimizing. The distribution matters because the headline adoption percentages obscure how many organizations are genuinely running AI in production versus running a proof-of-concept that has not advanced.
| Maturity Tier | Forrester Definition | Approx. Share of Respondents (2024) |
|---|---|---|
| Exploring | Evaluating vendors, building business case, no active deployment | ~22% |
| Piloting | Running one or more proofs-of-concept or limited-scope deployments, not yet in production at scale | ~31% |
| Scaling | At least one AI application in production, expanding scope or adding functions | ~29% |
| Optimizing | Multiple AI applications in production, focus on model governance, integration, and continuous improvement | ~18% |
The combined Piloting + Exploring share (~53%) being larger than Scaling + Optimizing (~47%) is a meaningful signal. Despite years of vendor marketing claiming widespread AI adoption, the majority of enterprise respondents in this panel have not yet reached sustained production deployment across even a single supply chain function.
The Optimizing tier — where organizations have multiple AI applications in production and are focused on governance and integration — represents roughly one in five respondents. That figure is consistent with other 2024 analyst surveys and suggests the gap between early movers and the median enterprise is widening rather than closing.
Top-Ranked Implementation Barriers
Forrester asked respondents to rank their top barriers to AI deployment in supply chain. The barrier rankings are consistent with what practitioners report in other surveys, but the relative weighting in the 2024 Forrester data shifts from prior years in two ways: data quality concerns have risen relative to budget constraints, and organizational change management has overtaken technology integration complexity as the second-most-cited barrier.
| Barrier | Rank (2024) | Rank (2023 wave) | Notes |
|---|---|---|---|
| Data quality / data readiness | 1 | 1 | Consistent top barrier across both years; cited by majority of respondents not yet at Scaling tier |
| Organizational change management | 2 | 3 | Moved up — reflects difficulty getting planners and operators to adopt AI recommendations |
| Technology integration complexity | 3 | 2 | Dropped one position; still a top-three barrier for organizations on legacy ERP stacks |
| Unclear ROI / business case | 4 | 4 | Stable; more acute in procurement and logistics than in demand planning |
| Talent / skills gaps | 5 | 5 | Stable; cited more often by mid-market respondents who appear in the enterprise-adjacent fringe of the panel |
| Vendor selection complexity | 6 | 6 | Stable; higher in warehouse automation than in planning software |
Data Quality as the Persistent Constraint
The persistence of data quality at rank one across both survey years is not a surprise, but the Forrester 2024 data adds granularity: the specific data problems cited most often are inconsistent item master data across ERP instances, missing or unreliable historical demand records for new SKUs, and inadequate supplier lead time data in procurement systems. These are not abstract data governance problems — they are specific integration and data engineering tasks that precede any AI model deployment.
Change Management Rising as a Barrier
The movement of organizational change management from third to second place in the 2024 barrier rankings reflects a shift in where organizations are getting stuck. In 2022–2023, most organizations were blocked before deployment. By 2024, more organizations have reached the point of having an AI tool in limited production — and are then discovering that demand planners override model recommendations, warehouse supervisors revert to manual processes, and procurement teams distrust AI-generated supplier risk scores. The technology is deployed; the workflow adoption is not.
Year-Over-Year Trend: 2023 vs. 2024 Wave
Forrester has fielded this benchmark in consecutive years, allowing directional trend comparison. The 2024 wave shows measurable movement at the top and bottom of the maturity distribution, with the Exploring tier shrinking and the Optimizing tier growing — but the Piloting tier has not contracted at the same rate, suggesting organizations are entering the pipeline faster than they are advancing through it.
- Exploring tier: contracted approximately 5 percentage points year-over-year, as more organizations have moved to active piloting.
- Piloting tier: largely stable or slightly expanded — new entrants from the Exploring tier are roughly offsetting organizations advancing to Scaling.
- Scaling tier: grew approximately 4 percentage points, driven primarily by demand planning and warehouse automation functions.
- Optimizing tier: grew approximately 3 percentage points; growth concentrated in organizations that were already at Scaling in 2023 and have since added governance infrastructure.
Function-Specific Investment Concentration
Forrester's 2024 data breaks out investment concentration by function in a way that is useful for benchmarking where your organization's AI budget allocation sits relative to peers. Demand planning and warehouse automation continue to absorb the largest share of supply chain AI investment dollars, but the gap between these leading functions and procurement AI is narrowing.
| SCM Function | Share of Total Supply Chain AI Budget (2024 est.) | Direction vs. 2023 |
|---|---|---|
| Demand planning / forecasting | ~28% | Stable |
| Warehouse automation / robotics | ~25% | Slight decrease |
| Inventory optimization | ~17% | Stable |
| Procurement / sourcing AI | ~15% | Increasing |
| Logistics / TMS optimization | ~10% | Stable |
| Control tower / visibility | ~5% | Increasing |
Limitations of This Benchmark for Practitioner Use
The Forrester benchmark is useful for directional orientation but has specific limitations that practitioners should account for when using it to justify internal investment decisions or benchmark their organization's maturity.
- Enterprise bias: The respondent profile skews toward large enterprises (>$500M revenue). Organizations in the $50M–$500M range will find the adoption and maturity figures optimistic relative to their peer group.
- Self-reporting distortion: Adoption state is self-reported. "In production" in this survey includes narrow deployments (e.g., AI-assisted demand sensing for one product category) alongside broad production rollouts. The figures overstate deployment depth.
- Technology vendor influence: Forrester's supply chain practice has commercial relationships with technology vendors. While the benchmark methodology is designed to be independent, practitioners should cross-reference with non-vendor-adjacent sources such as MHI's annual report or ASCM's supply chain technology survey.
- Geographic weighting: North America and Western Europe are overrepresented. Adoption rates in Asia-Pacific markets, particularly in manufacturing-heavy industries, may differ materially from the benchmark figures.
- Point-in-time snapshot: The 2024 survey was fielded in Q2–Q3 2024. Given the pace of market change, some investment intent figures may not reflect decisions made after Q3 2024, including responses to tariff changes and geopolitical disruptions that accelerated in late 2024 and into 2025.
How to Use This Data in Practice
The most defensible use of Forrester's 2024 benchmark figures is as a framing device in internal conversations, not as a precise competitive benchmark. Telling a CFO that roughly half of enterprise supply chain organizations are actively piloting or in production with demand planning AI is a useful context-setter. Using the exact percentage as a precision claim in a business case is not.
The barrier rankings are more operationally useful than the adoption percentages. If your organization is stuck at the Piloting tier and the Forrester data shows data quality and change management as the top two barriers, that is a diagnostic signal — not just a market statistic. It points to where internal effort should concentrate before the next vendor evaluation cycle begins.
For organizations in the Scaling or Optimizing tier, the investment intent data by function is more relevant than the adoption-state snapshot. Where peers are directing incremental AI budget — procurement AI and control tower applications, based on the 2024 data — reflects where the market sees the next productivity gains, and is worth factoring into roadmap prioritization.
Comments
Join the discussion with an anonymous comment.