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TRUVACE RECORD VERSION
record: TRV-2026-2715
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:13:32.240293Z
status: published
lens: g_space
sector: business
headline: A cross-contextual meta-analysis of agentic AI in retail and supply chain operations: efficacy and implementation insights
dek: Abstract Agentic artificial intelligence (AI) systems are increasingly deployed in retail and supply chain operations, yet the empirical evidence for their efficacy has never been systematically quantified. This meta-analysis synthesizes 19 studies (total $$N = 5{,}292$$ ) to quantify the impact of agentic AI on operational performance and examine how contextual factors shape outcomes. Using a random-effects model with restricted maximum likelihood (REML) estimation and the Hartung-Knapp (HK) adjustment for conf…
gain_title: A meta-analysis of 19 studies (N = 5,292) found agentic AI in retail and supply chain operations improved operational performance on average, with a pooled Hedges' g of 1.58 (95% CI 0.26 to 2.89).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 19 studies (N = 5,292) found agentic AI in retail and supply chain operations improved operational performance on average, with a pooled Hedges' g of 1.58 (95% CI 0.26 to 2.89).
gain_evidence: the pooled Hedges’ $$g = 1.58$$ (95% CI $$[0.26,\ 2.89]$$ , $$p = 0.022$$ )
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooling 19 studies with 5,292 participants in total found that agentic AI systems in retail and supply chain operations improved performance on average. The pooled effect was a Hedges' g of 1.58 (95% CI 0.26 to 2.89, p = 0.022).

The average hides enormous differences between studies. Heterogeneity was near-total (I² = 99.5%), and the 95% prediction interval of -4.20 to 7.36 means that in a new setting the true effect could range from substantial harm to exceptional benefit. The authors say the pooled figure should not be read as a general implementation expectation.

Evidence for differences between operational domains was weak. Retail, supply chain, e-commerce and inventory differed under a common-effect model, but not significantly under the random-effects model (p = 0.081). A limit meta-analysis that adjusts for selective reporting lowered the estimate to g = 1.26 (p = 0.066).
limitation: The studies were extremely heterogeneous (I² = 99.5%) and showed meaningful selective reporting, and the e-commerce estimate rests on only three studies, so the pooled effect should not be read as a general expectation.
tag: Evidence-backed gain
key_points: The analysis pooled 19 studies with a combined N of 5,292, using a random-effects model with REML estimation and the Hartung-Knapp adjustment. | The pooled Hedges' g was 1.58 (95% CI 0.26 to 2.89, p = 0.022). | Heterogeneity was I² = 99.5% and τ² = 7.18; the 95% prediction interval ran from -4.20 to 7.36. | Differences between four operational domains (retail, supply chain, e-commerce, inventory) were not significant under the random-effects model (Q = 6.73, p = 0.081). | The e-commerce estimate rests on only three studies, so the authors treat it as descriptive. | A limit meta-analysis reduced the estimate to g = 1.26 (p = 0.066), pointing to meaningful selective reporting. | The authors advise domain-specific pilots, separating simulation-based from real-world evidence, and attending to differences in AI architecture.
rundown: The authors say the empirical evidence on agentic AI in retail and supply chains had not been systematically quantified before. Their meta-analysis covers 19 studies and 5,292 participants in total, using a random-effects model with restricted maximum likelihood estimation and Hartung-Knapp confidence intervals.

The pooled gain of g = 1.58 is statistically significant, but the spread between studies is extreme. With I² of 99.5% and a prediction interval of -4.20 to 7.36, a new deployment could see either substantial harm or exceptional benefit.

Domain comparisons proved inconclusive. A common-effect model showed differences between retail, supply chain, e-commerce and inventory (Q = 935.45, p < 0.001), but the random-effects model, which the authors consider more appropriate, did not (p = 0.081).

After adjusting for selective reporting, the estimate fell to g = 1.26 (p = 0.066). The authors call for real-world field experiments, architecture-specific evaluations, publication of null results and validated quality frameworks.
sources:
- peer_reviewed | Industrial Artificial Intelligence | https://doi.org/10.1007/s44244-026-00039-3 | 2026-10-24
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