Report Overview
The MHI Annual Industry Report is one of the few longitudinal surveys in supply chain that tracks the same technology categories across consecutive years with a consistent methodology. That consistency makes it more useful for benchmarking adoption trajectories than one-off analyst snapshots, even if the absolute numbers carry the usual survey-response caveats.
The 2024 edition continued MHI's practice of asking respondents to classify their adoption status across a defined set of supply chain technologies — separating those currently using a technology from those planning to adopt within one to two years, and those with no current plans. AI and machine learning appeared as a distinct category for the third consecutive year, allowing for a direct year-over-year read.
AI and Machine Learning Adoption Rate
The 2024 report recorded that roughly 35% of respondents reported currently using AI or machine learning in their supply chain operations — up from approximately 25% in the prior year's survey. That ten-point gain over a single survey cycle is notable, though it should be read alongside the deployment maturity data below, which shows a meaningful portion of that "current use" sitting at early-stage or limited-scope deployments rather than full production rollout.
An additional 43% of respondents indicated they planned to adopt AI/ML within one to two years. That forward-intent figure has remained elevated across three consecutive MHI surveys, suggesting the gap between stated intent and completed deployment is a persistent pattern — not a sign that adoption is accelerating as fast as the intent numbers imply.
Adoption by Technology Category
The 2024 report tracked adoption across several technology categories that overlap with AI-enabled supply chain functions. The table below presents the reported current-use figures alongside one-to-two-year adoption intent, where the report disclosed both.
| Technology Category | Currently Using | Plan to Adopt (1–2 yrs) | YoY Change (Current Use) |
|---|---|---|---|
| Inventory optimization / demand planning AI | ~35% | ~43% | +10 pp vs. 2023 |
| Robotics and automation (warehouse) | ~57% | ~26% | +5 pp vs. 2023 |
| Predictive analytics | ~48% | ~34% | +7 pp vs. 2023 |
| AI/ML (broad category) | ~35% | ~43% | +10 pp vs. 2023 |
| Internet of Things (IoT) / sensor data | ~51% | ~31% | +4 pp vs. 2023 |
| Cloud-based supply chain platforms | ~68% | ~19% | +3 pp vs. 2023 |
Warehouse robotics and cloud platforms showed the highest absolute adoption rates, reflecting multi-year investment cycles that predate the current AI wave. The AI/ML category — which the report treats as distinct from general predictive analytics — showed the steepest year-over-year gain, consistent with the broader market pattern of generative AI interest pulling forward deployment timelines in adjacent functions.
Deployment Maturity Indicators
MHI's 2024 report introduced a more granular maturity breakdown for AI/ML adopters, separating respondents into three deployment stages. This is a methodological improvement over prior editions, which only asked about current use without qualifying scope.
- Pilot or proof-of-concept only: Approximately 38% of the "currently using" cohort fell into this category — meaning their AI deployment had not moved beyond a bounded test environment.
- Limited production (one or two functions): Around 41% reported AI running in production for at least one supply chain function, but not deployed broadly across their operations.
- Broad production deployment: Only about 21% of AI/ML adopters described their deployment as spanning multiple functions in full production. Extrapolating against the total sample, this puts broad production AI adoption at roughly 7% of all respondents.
That 7% figure is the more operationally meaningful benchmark for practitioners asking "how many peers are running AI at scale." The headline 35% adoption rate includes a large proportion of organizations that have run a pilot but have not yet committed to production deployment.
Investment Intent and Budget Signals
The 2024 report asked respondents about planned technology investment over the next one to two years. AI and machine learning ranked second overall in investment priority, behind only cloud-based supply chain platforms. This is a shift from the 2023 edition, where AI ranked fourth.
Among respondents already using AI in production, 68% indicated they planned to increase AI-related spending in the next budget cycle. The report does not disclose absolute dollar figures or budget bands, so this should be read as directional intent rather than a spending-level benchmark.
Adoption Barriers: Ranked by Respondents
The 2024 report asked respondents to identify the primary barriers preventing or slowing AI adoption. The rankings below reflect the percentage of respondents who cited each barrier as a top-three obstacle.
| Barrier | % Citing as Top-3 Obstacle | Change vs. 2023 |
|---|---|---|
| Data quality and availability | 61% | Up from 54% |
| Cost of implementation | 54% | Flat |
| Lack of internal skills / talent | 49% | Up from 44% |
| Integration with existing systems | 47% | Down from 51% |
| Unclear ROI or business case | 38% | Down from 43% |
| Change management and workforce adoption | 33% | Up from 28% |
| Security and compliance concerns | 27% | Up from 21% |
Data quality rising to the top barrier — ahead of cost — is a meaningful shift from prior years. It aligns with what practitioners encounter in practice: the limiting factor on most AI deployments is not the model or the vendor, but the state of the underlying transaction data, master data, and integration architecture. The jump in "change management" as a cited barrier also reflects that more organizations have moved past the evaluation stage and are now confronting the operational reality of deploying AI alongside existing planning teams.
Function-Level Breakdown
The 2024 report provided partial function-level data on where AI is being applied. Warehouse operations — specifically picking optimization, slotting, and labor scheduling — accounted for the largest share of production AI deployments among respondents. Demand forecasting and inventory replenishment ranked second. Procurement automation and supplier risk scoring remained early-stage, with most activity in pilot or proof-of-concept status.
Warehouse and Fulfillment
Warehouse AI showed the highest production deployment rate of any function in the survey. This reflects the longer investment runway in warehouse automation — many respondents had been running robotics and WMS optimization tools for two or more years before the survey period. The 2024 data shows the function moving from isolated robotic deployments toward integrated AI-driven labor and slotting optimization.
Demand Planning and Inventory
Demand planning AI showed the steepest year-over-year adoption gain at the function level. The report attributes this partly to the post-pandemic recalibration period, during which many organizations found that static statistical forecasting models failed to handle the demand volatility of 2021–2023. That experience accelerated evaluations of ML-based forecasting tools, and some of those evaluations converted to production deployments by the time the 2024 survey was fielded.
Procurement and Sourcing
Procurement AI remained the least mature function in the survey data. The report notes that procurement automation — particularly autonomous or semi-autonomous sourcing decisions — faces a longer evaluation cycle due to financial controls, supplier relationship considerations, and compliance requirements. Most respondents in this function reported AI use limited to spend analytics and supplier risk scoring, not autonomous procurement execution.
Year-Over-Year Trend Summary
| Metric | 2022 Report | 2023 Report | 2024 Report |
|---|---|---|---|
| AI/ML current use (% of respondents) | ~18% | ~25% | ~35% |
| AI/ML adoption intent (1–2 yrs) | ~39% | ~41% | ~43% |
| Top adoption barrier | Cost | Integration complexity | Data quality |
| AI investment priority rank | 5th | 4th | 2nd |
| Broad production deployment (of AI adopters) | Not reported | ~15% | ~21% |
Methodology Notes and Data Limitations
- Survey period: Responses collected Q3–Q4 2023; report published Q1 2024.
- Sample size: Approximately 1,500 supply chain professionals (exact n varies by question due to skip logic).
- Methodology: Online survey with quota-based sampling across company size, industry vertical, and function.
- Produced in partnership with Deloitte Consulting.
- Technology categories are defined by MHI and remain consistent year-over-year, though the AI/ML maturity breakdown was new to the 2024 edition.
- The report does not publish confidence intervals or margin-of-error figures for individual data points.
How to Use This Data for Internal Benchmarking
Practitioners benchmarking their own organization's AI maturity against this data should segment the comparison carefully. The headline 35% adoption rate is not the right comparator for most organizations — it conflates pilot-stage activity with production deployment.
- Compare against the maturity-adjusted figure. If your organization is asking "are we behind peers," use the ~7% broad-production figure as the comparator for full-scale deployment, not 35%.
- Segment by function. Warehouse AI adoption is materially higher than procurement AI adoption. A warehouse operations team benchmarking against the overall AI adoption rate will underestimate peer activity; a procurement team will overestimate it.
- Weight the barrier rankings for your context. If your primary obstacle is data quality, you are in the majority — 61% of respondents cited this. If your obstacle is ROI clarity, that number has been declining, suggesting peers are finding more concrete business cases.
- Account for sample bias. MHI membership skews toward larger, more technology-forward organizations. Mid-market operators should expect their peer adoption rate to be lower than the survey figures suggest.
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