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Generative AI boosts firms’ digital resilience in turbulent times but technical gains do not automatically improve worker‑facing supply‑chain outcomes. Firms must pair GenAI with training, participative governance and responsible practices to realize collaboration, trust and well‑being benefits.

Braving technological turbulence: generative artificial intelligence can build digital resilience in human-centric supply chains
· September 17, 2026 · Journal of Enterprise Information Management
semantic_scholar correlational low evidence 7/10 relevance Summary only summary available; pdf_status=not_found DOI Source

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Generative AI adoption is strongly associated with increased digital resilience under technological turbulence, but that resilience alone does not translate into improved human‑centric supply‑chain outcomes without complementary socio‑organizational practices.

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Human-centric supply chains (HCSCs) are increasingly vital as firms face growing disruptions while balancing efficiency with workforce well-being and collaboration. This study examines how generative artificial intelligence (GenAI) influences HCSC performance under technological turbulence. Anchored in Industry 5.0, the research positions digital resilience as a key organizational capability and investigates whether advanced AI technologies translate environmental uncertainty into people-focused supply chain outcomes through sociotechnical and dynamic capability perspectives. The study adopts a quantitative, deductive design using survey data from 276 manufacturing firms in the United Kingdom and Canada. Data were collected in early 2025 from managers involved in supply chain and digital transformation activities. The proposed relationships among GenAI, technological turbulence, digital resilience and HCSC performance were tested using partial least squares structural equation modeling (PLS-SEM). Measurement reliability, convergent validity and discriminant validity were rigorously assessed. The results reveal that GenAI has a strong positive effect on digital resilience. Technological turbulence does not directly enhance digital resilience but significantly drives GenAI adoption and directly improves HCSC performance. Digital resilience does not directly influence human-centric performance, and its mediating role is not supported. However, GenAI significantly mediates the relationship between technological turbulence and digital resilience, underscoring its central role in transforming environmental uncertainty into adaptive capacity. These findings indicate that technological capabilities alone are insufficient to achieve people-focused outcomes. The cross-sectional design limits causal inference, and reliance on single-informant perceptual data may introduce bias. The focus on manufacturing firms in two advanced economies may restrict generalizability. Future research should employ longitudinal designs, incorporate multiple respondents, and examine additional socio-organizational mechanisms, such as governance, culture and skills development that link AI adoption to human-centric supply chain outcomes. Managers should view GenAI as a strategic capability for building digital resilience rather than solely as an efficiency tool. While GenAI enhances adaptive capacity, achieving human-centric performance requires complementary organizational practices, including workforce upskilling, participative decision-making and transparent governance. Firms operating amid technological turbulence can leverage uncertainty to accelerate the adoption of responsible AI aligned with people-focused supply chain objectives. The findings suggest that digital resilience alone does not ensure improved collaboration, trust or workforce well-being. Achieving human-centric supply chains requires aligning AI adoption with social and organizational practices. Policymakers and industry leaders should promote responsible use of GenAI, workforce development and inclusive innovation to ensure that digital transformation contributes to sustainable livelihoods and equitable value creation. This study advances the literature by distinguishing generative AI from traditional AI and empirically examining its role in human-centric supply chains. It introduces technological turbulence as a contextual driver of AI-enabled capability development and challenges assumptions that digital resilience directly leads to human-centric outcomes. By integrating sociotechnical and dynamic capability perspectives, the study provides novel insights into AI-enabled, human-centric supply chains within Industry 5.0 contexts.

Summary

Main Finding

Generative AI (GenAI) strongly increases firms’ digital resilience and serves as the key channel through which technological turbulence is converted into adaptive digital capacity. However, digital resilience by itself does not translate into improved human-centric supply chain (HCSC) outcomes (collaboration, trust, workforce well‑being). Technological turbulence also directly improves HCSC performance and drives GenAI adoption, but achieving people‑focused outcomes requires complementary socio‑organizational practices alongside GenAI.

Key Points

  • Sample and context: survey of 276 manufacturing firms in the UK and Canada; data collected early 2025 from managers responsible for supply chain/digital transformation.
  • Core constructs: technological turbulence (environmental uncertainty), GenAI adoption, digital resilience (organizational capability), and HCSC performance (people‑centric outcomes).
  • Main statistical results:
    • Strong positive effect: GenAI → digital resilience.
    • Technological turbulence → GenAI adoption (significant).
    • Technological turbulence → HCSC performance (direct positive effect).
    • Digital resilience → HCSC performance (not significant).
    • Digital resilience did not mediate the turbulence → HCSC link; GenAI mediated turbulence → digital resilience.
  • Interpretation: GenAI is a strategic technological capability that builds adaptive capacity under uncertainty, but technical resilience alone is insufficient to generate human‑centric benefits.
  • Practical recommendations from authors: pair GenAI adoption with workforce upskilling, participative decision‑making, transparent governance, and responsible AI practices to realize HCSC objectives.

Data & Methods

  • Design: Quantitative, deductive, cross‑sectional survey.
  • Sample: 276 manufacturing firms across two advanced economies (UK, Canada); single‑informant responses from supply chain/digital transformation managers.
  • Period: Early 2025.
  • Analysis: Partial least squares structural equation modeling (PLS‑SEM).
  • Measurement: Reliability, convergent validity, and discriminant validity were assessed and reported.
  • Limitations noted by authors:
    • Cross‑sectional data limit causal claims.
    • Single‑informant perceptual measures may introduce common‑method bias.
    • Focus on manufacturing firms in two advanced economies limits generalizability.

Implications for AI Economics

  • Investment and diffusion
    • GenAI functions as a strategic capability that firms adopt under technological turbulence; economists should view GenAI investment decisions as responses to uncertainty as well as productivity motives.
    • Returns to GenAI investment are mediated: GenAI raises digital resilience (adaptive capacity), but this may not automatically produce human‑centered welfare gains without organizational complements.
  • Complementarity with human capital and institutions
    • Findings emphasize complementarity: GenAI adoption needs workforce upskilling, governance, and participatory practices to produce improvements in trust, collaboration, and worker well‑being.
    • Policies that subsidize AI alone may underdeliver unless paired with training, labor market supports, and governance standards.
  • Labor markets and distributional effects
    • Because digital resilience alone does not improve human‑centric outcomes, there is risk of productivity gains without commensurate improvements in worker welfare—potentially increasing distributional tensions.
    • Research and policy should focus on who captures gains from AI-enabled resilience (managers, workers, suppliers).
  • Measurement and evaluation
    • Economists should broaden measurement beyond productivity to include social outcomes (trust, collaboration, well‑being) when evaluating AI’s economic impact.
    • Causal identification (e.g., longitudinal, quasi‑experimental designs) is needed to estimate net welfare effects of GenAI adoption and the value of complementary policies.
  • Future research directions relevant to AI economics
    • Longitudinal studies to trace dynamic returns to GenAI and whether digital resilience leads to human‑centric outcomes over time.
    • Multi‑informant and objective performance measures to reduce bias.
    • Analysis of heterogeneity across sectors, firm size, and institutional contexts (emerging vs advanced economies).
    • Cost‑benefit and distributional analyses that quantify investments in AI plus complementary practices (training, governance) versus AI alone.
    • Exploration of policy levers that accelerate responsible, inclusive AI diffusion (training subsidies, governance standards, incentives for participatory implementation).

Overall, the study signals that GenAI adoption under uncertainty can raise firms’ adaptive capacity, but AI economics must account for organizational complementarities and distributional consequences to assess welfare and policy needs accurately.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional, single‑informant survey data prevent credible causal identification; reported effects are associations and the authors acknowledge limits to causal inference and potential common‑method bias. Methods Rigormedium — The study uses an appropriate sample size (n=276), established constructs, and tests for reliability/validity and PLS‑SEM for model estimation, but relies on perceptual single‑respondent measures, cross‑sectional design, and mediation tests that cannot establish causality. SampleSurvey of 276 manufacturing firms in the UK and Canada (data collected early 2025); single informant per firm (managers responsible for supply chain/digital transformation); cross‑sectional, perceptual measures. Themeshuman_ai_collab adoption org_design GeneralizabilityManufacturing firms only — may not apply to services or mixed industries, Two advanced economies (UK, Canada) — limits applicability to emerging markets or different institutional contexts, Single‑informant, manager perceptions — may not reflect objective firm outcomes or worker perspectives, Cross‑sectional snapshot — dynamic effects over time (adoption, learning) not observed, Sample size moderate (n=276) — limits ability to explore fine-grained heterogeneity (firm size, sector subtypes)

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Generative AI adoption has a strong positive effect on firms' digital resilience. Organizational Efficiency positive Digital resilience, defined as an organizational adaptive capability.
Reading fidelity high
Study strength medium
n=276
0.3
Technological turbulence significantly increases GenAI adoption among manufacturing firms. Adoption Rate positive Firm adoption of generative AI.
Reading fidelity high
Study strength medium
n=276
0.3
Technological turbulence has a direct positive effect on human-centric supply chain performance. Team Performance positive Human-centric supply chain performance, including collaboration, trust, and workforce well-being.
Reading fidelity high
Study strength medium
n=276
0.3
Digital resilience does not have a statistically significant effect on human-centric supply chain performance. Team Performance null_result Human-centric supply chain performance, including collaboration, trust, and workforce well-being.
Reading fidelity high
Study strength medium
n=276
0.3
Digital resilience does not mediate the relationship between technological turbulence and human-centric supply chain performance. Team Performance null_result Human-centric supply chain performance as related to technological turbulence through digital resilience.
Reading fidelity high
Study strength medium
n=276
0.3
GenAI mediates the relationship between technological turbulence and digital resilience. Organizational Efficiency positive Digital resilience arising through GenAI adoption under technological turbulence.
Reading fidelity high
Study strength medium
n=276
0.3
Digital resilience alone does not automatically produce human-centric benefits such as improved collaboration, trust, or workforce well-being. Worker Satisfaction null_result Collaboration, trust, and workforce well-being within human-centric supply chain performance.
Reading fidelity high
Study strength medium
n=276
0.3
The authors recommend pairing GenAI adoption with workforce upskilling, participative decision-making, transparent governance, and responsible AI practices to achieve human-centric supply chain objectives. Governance And Regulation positive Human-centric supply chain outcomes, including trust, collaboration, and worker well-being.
Reading fidelity high
Study strength speculative
n=276
0.05

Notes