Supply chain AI readiness: 83% say their workforce isn't ready, plus the 13-function scorecard from ASCM and DSCI
A new ASCM-DSCI report finds 83% of supply chain leaders say their employees aren't ready to use AI. Supply chain AI readiness explained: function scores, barriers, success rates, Turkey data and a checklist.
In AI projects, the bottleneck is often people, not technology. The "People Paradox in Supply Chain AI Integration" report, released on October 14 by the Association for Supply Chain Management (ASCM) and the Digital Supply Chain Institute (DSCI), puts numbers on it: 91% of organizations plan to use AI to augment workers, yet 83% say their employees are unprepared to use AI effectively [1]. If you are planning for supply chain AI readiness, here are the findings in tables, a comparison with Turkey and a practical checklist.
Who was surveyed?
The findings are based on 107 qualified respondents across all levels, from C-suite to frontline staff. Organizations range from under $25 million to $10 billion or more in annual revenue and span industrial manufacturing, consumer packaged goods, pharma and health care, and transportation and logistics. North America accounts for 62% of respondents and Europe for 13% [1]. FreightWaves reports the survey collected 144 responses, 107 met the analysis criteria, and only 11 respondents were C-suite, so the report labels its job-level results as directional [2].
Supply chain AI readiness: the gap between ready and not ready
| Indicator | Value | Source |
|---|---|---|
| Plan to use AI to augment workers | 91% | [1] |
| Say employees are unprepared to use AI effectively | 83% | [1] |
| AI project success rate, the 17% with a ready workforce | 71% | [2] |
| Success rate, everyone else | 28% | [2] |
| Share of the unprepared still in pilot or planning | about 64% | [2] |
| Expect AI to directly replace workers | 7% | [2] |
| Expect AI to move people to higher-value decisions | 51% | [2] |
| Call their deployments "somewhat successful" | 55% | [1] |
Quotable stat: In the ASCM-DSCI survey, organizations with an AI-ready workforce report a 71% AI project success rate, versus 28% for everyone else [2].
AI maturity across 13 functions: 1.94 out of 5
The report scored AI adoption across 13 core supply chain functions from 1 (no AI use) to 5 (autonomous execution without human review). The average was 1.94 [2].
| Function | Score (1-5) | Note |
|---|---|---|
| Demand planning | 2.28 | Highest [2] |
| Supply and inventory planning | 2.12 | Planning leads [2] |
| Average of 13 functions | 1.94 | The "short leash" era [2] |
| Returns and reverse logistics | 1.59 | Lowest [2] |
Planning functions lead and execution-heavy functions trail. The report calls this keeping AI on a "short leash" and says predictive tools are used as "little more than digital spreadsheets" [2]. ASCM CEO Abe Eshkenazi told FreightWaves AI is used "much more on the analysis and on the predictions as opposed to the doing", calling it "more of a glorified spreadsheet" [2]. For where warehouse agents stand, see our piece on supply chain AI agents and digital workers.
The real barriers: not executive skepticism
According to the press release, the biggest obstacles are not executive skepticism or lack of buy-in but four business hurdles: an unready workforce, an inability to trust AI to act autonomously, cybersecurity concerns, and difficulty proving ROI [1].
| Barrier | Share of respondents [2] |
|---|---|
| Lack of workforce skills and resources | 33% |
| Unclear business case / ROI | 33% |
| Poor data quality | 25% |
| Executive skepticism | Not among the main hurdles |
The gap by level is striking: 82% of C-suite respondents lead, implement or scale AI initiatives, versus 12% of directors. Half of directors, 51% of managers and 70% of staff say they are only aware of them [2]. Eshkenazi's ERP comparison is worth remembering: "The technology solution can't improve bad process and it can't improve bad data." [2]
Cyber risk: your supplier's supplier
92% of respondents expressed significant concern about cybersecurity exposure through AI-enabled suppliers and trade partners, and the average organization had adopted 2.3 of six standard cybersecurity practices [2]. Eshkenazi traces the exposure to the post-pandemic visibility push that connected suppliers, carriers and brokers on shared platforms, which exposes companies to ransomware risk in tier-three and tier-four vendors. On a conference panel in San Antonio, food distributors described a compromised carrier email that led to a fraudulent pickup of a cold-storage load [2]. We covered device-level risk in barcode scanner security in the warehouse.
A view from Turkey: 9.7% AI use in transport and storage
Anadolu Agency's summary of TurkStat's "Artificial Intelligence Statistics 2026" shows 14% of Turkish enterprises use AI; the rate is 63.7% in information and communication but 9.7% in transport and storage (in Turkish) [3]. Logistics sits below the national average, which suggests the ASCM-DSCI lesson may apply even more strongly to Turkish logistics firms: buying technology is easier than preparing the team. For a concrete use case, see our AI freight audit article.
10-point supply chain AI readiness checklist
Compiled from the report's findings and Eshkenazi's recommendations [1][2]:
- Pick one high-value process (a key customer process, supplier or route) and involve the people who run it from the start [2]
- Measure your starting point and set measurable outcomes [2]
- Measure ROI by response time (how fast you recognize a disruption and act), not just headcount [2]
- Fix broken processes and dirty data first, using value stream mapping and data analysis [2]
- Let AI handle routine, parameter-bound transactional decisions; keep customer commitments, sourcing and major disruptions with people [2]
- Make sure every AI-driven decision has an owner inside the organization [2]
- Move directors and middle managers from "aware" to "implementing" [2]
- Train exception spotting and sanity checks so staff don't lose analytical fundamentals [2]
- Add email verification and pickup controls to carrier and supplier communication [2]
- Put the cybersecurity practices of AI-enabled suppliers into contracts and audits [2]
Frequently asked questions
Where is AI used most in supply chain management?
In planning. In the ASCM-DSCI report demand planning scores 2.28 out of 5 and supply and inventory planning 2.12; returns and reverse logistics are last at 1.59 [2].
Why do supply chain AI projects stay in pilot?
The top barriers are lack of workforce skills and resources and an unclear business case (33% each), followed by poor data quality (25%) [2].
Will AI replace logistics workers?
Only 7% of respondents expect AI to directly replace workers; 51% expect it to move people toward higher-value decision-making [2].
How widely is AI used in Turkish logistics?
According to TurkStat, 9.7% of enterprises in transport and storage use AI, compared with 14% of all enterprises [3].
How should supply chain AI ROI be measured?
Eshkenazi calls headcount reduction too simple a measure; a better one is response time, how quickly a company can recognize a disruption, weigh alternatives and act [2].
References
- Digital Supply Chain Institute (DSCI), "New ASCM and DSCI Research Finds 83% of Supply Chain Leaders Say Their Workforce Isn't Ready for AI", October 14, 2026. https://dscinstitute.org/supply-chain-ai-workforce-readiness-report/ (Accessed: October 25, 2026)
- FreightWaves, Thomas Wasson, "Workforce gaps keep supply chain AI stuck in pilot mode", October 23, 2026. https://www.freightwaves.com/news/ascm-dsci-supply-chain-ai-workforce-readiness (Accessed: October 25, 2026)
- Anadolu Ajansı, Mertkan Oruç, "Türkiye yapay zekayı en çok bilişimde, en az inşaatta kullanıyor" (TurkStat AI Statistics 2026), October 3, 2026 (in Turkish). https://www.aa.com.tr/tr/bilim-teknoloji/turkiye-yapay-zekayi-en-cok-bilisimde-en-az-insaatta-kullaniyor/4076782 (Accessed: October 25, 2026)
