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In Mumbai–Pune IT firms, perceived AI infrastructure, cultural agility and employee readiness correlate strongly with self-reported AI value creation, while firm size does not; findings come from a small cross-sectional employee survey and cannot establish causality.

AI LEADERSHIP CAPABILITY AND HUMAN-AI COLLABORATION: AN ORGANISATIONAL DEVELOPMENT PERSPECTIVE IN THE INDIAN IT INDUSTRY
Jolly Sen Gupta, Ambrish Sharma · September 07, 2026 · International Journal of Computer Information Systems and Industrial Management Applications
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A survey of 110 employees at six Mumbai/Pune IT firms finds that perceived AI infrastructure maturity, collaborative organizational culture, and employee psychological readiness are positively associated with self-reported AI-driven value creation, while organizational size shows no significant effect.

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Artificial intelligence is reshaping leadership, teamwork, and organizational structures in India’s IT sector, particularly in Mumbai and Pune. This study examines how AI infrastructure maturity, employee psychological readiness, and organizational culture influence AI transformation success and value creation, with organizational size as a moderating factor. Using a quantitative approach, 110 employees from six major IT firms completed an 11-item, five-point Likert-scale survey. Data were analyzed using descriptive statistics, reliability tests, correlations, regression, structural equation modeling, ANOVA, and mediation analysis. AI infrastructure, organizational culture, and psychological readiness significantly predicted value creation (coefficients 0.44, 0.37, and 0.28), while organizational size was insignificant (0.11, p = .09). AI infrastructure was strongly associated with both organizational culture and value creation; psychological readiness supported value creation directly and indirectly. Findings highlight that technological maturity, workforce preparedness, ethical leadership, cultural agility, training, and accountable governance are critical for sustainable AI adoption. The study recommends prioritizing infrastructure and people capabilities over organizational expansion to maximize AI-driven value.

Summary

Main Finding

In a survey of 110 employees at six large Mumbai/Pune IT firms, maturity of AI infrastructure, a collaborative organizational culture, and employee psychological readiness each independently and significantly predict organizational value creation from AI. Organizational size did not significantly predict AI-driven value. Psychological readiness partially mediates the effects of both AI infrastructure and organizational culture on value creation.

Key Points

  • Sample and context: 110 respondents from six major Indian IT firms (TCS, Infosys, Tech Mahindra, LTIMindtree, Accenture, Persistent Systems) in Mumbai/Pune.
  • Primary predictors of value creation (standardized regression coefficients):
    • AI infrastructure: β = 0.44, t = 4.75, p < .001
    • Organizational culture: β = 0.37, t = 3.88, p < .001
    • Psychological readiness: β = 0.28, t = 3.05, p < .01
    • Organizational size: β = 0.11, t = 1.72, p = .09 (not significant)
  • Correlations (all p < .01):
    • AI infra — Org culture: r = 0.61
    • AI infra — Value creation: r = 0.59
    • Psychological readiness — Value creation: r = 0.62
  • Mediation: Psychological readiness partially mediates:
    • AI infra → Value (direct 0.32; indirect 0.12; Sobel z = 2.45, p < .05)
    • Org culture → Value (direct 0.28; indirect 0.09; Sobel z = 2.10, p < .05)
  • Between-firm variation significant (ANOVA F = 4.45, p < .01). Persistent Systems, TCS, LTIMindtree reported higher AI readiness and value; Tech Mahindra and Accenture trailed.
  • Measurement reliability: Cronbach’s α > .80 for core constructs (e.g., AI infra α = .88; Transformation success α = .90).
  • Main recommendations: prioritize AI infrastructure maturity, workforce training and psychological readiness, cultural agility, ethical leadership and governance over expansion of firm size as the route to AI-driven value.

Data & Methods

  • Design: Cross-sectional quantitative survey using 11-item 5‑point Likert scales across constructs (AI infrastructure, organizational culture, psychological readiness, value creation, transformation success).
  • Sample: 110 employees across six IT companies in Mumbai/Pune; sampling stratified on firm size and site strength (authors claim generalizability within sampled sector/region).
  • Analyses:
    • Descriptive statistics and reliability (means, SDs, Cronbach’s α)
    • Pearson correlations
    • Multiple regression predicting Value Creation (controls included organization size)
    • Structural Equation Modeling (SEM) for path coefficients and hypothesis tests
    • Mediation analysis with Sobel tests for psychological readiness as mediator
    • ANOVA for between-firm differences
  • Key limitations noted by authors: self-reported measures, single sector (IT) and two-city geographic scope, modest sample size.

Implications for AI Economics

  • Complementarity of capital and human capital: Returns to AI investments depend strongly on complementary organizational factors (infrastructure readiness + human psychological readiness + culture). Economic models of AI adoption should treat AI capital as complementary to (not substitutive of) managerial practices, training, and culture.
  • Heterogeneous firm-level returns: Significant between-firm variation implies that average returns to AI investment will be heterogeneous; policy and firm-level investment strategies must account for this cross-firm dispersion.
  • Scale is not sufficient: Organizational size was not a significant predictor of value from AI. Economically, this suggests diminishing role of scale per se and highlights organizational capabilities as the binding constraint on realizing productivity gains from AI.
  • Policy and public investment priorities: To maximize social returns to AI diffusion, subsidizing or incentivizing infrastructure upgrades alone is insufficient — investments in workforce reskilling, managerial capacity-building, and promoting collaborative cultures (and governance frameworks) will raise effective marginal returns to AI capital.
  • Measurement and evaluation: Empirical evaluations of AI adoption should include socio-technical indicators (psychological readiness, culture, governance) as moderators or mediators when estimating productivity or value-creation effects to avoid overestimating pure technology-driven returns.
  • Firm strategy: For firms deciding between scaling operations versus investing in AI capabilities, the evidence favors directing resources toward improving AI infrastructure maturity and people/culture investments to secure value creation from AI.

Limitations to keep in mind when using these results for economic inference: modest sample size, reliance on self-reports, single industry and two-city focus—so external validity beyond Indian IT firms is uncertain.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional self-reported survey with a small, non-random sample (N=110) from six firms; associations are reported (correlation/regression/SEM) but no exogenous variation, temporal ordering, or strategies to address common-method bias or omitted variables, so causal claims are not supported. Methods Rigorlow — The authors use standard survey instruments (α reported) and appropriate summary statistics, correlations, regressions, SEM and mediation tests, but the sample size is small for SEM, sampling appears non-random/convenience, key covariates and controls are not reported, and there is no design or analysis to support causal identification or to mitigate common-method/self-report bias. SamplePrimary data: cross-sectional self-administered Likert survey of 110 employees across six major IT firms operating in Mumbai and Pune (TCS, Infosys, Tech Mahindra, LTIMindtree, Accenture, Persistent Systems); no respondent demographics reported; sampling described as based on firm size/location but appears non-random and convenience-based. Themeshuman_ai_collab org_design skills_training GeneralizabilityLimited to IT sector employees in two Indian metro regions (Mumbai/Pune); may not generalize to other industries or geographies., Includes only six (largely large) firms—may not represent SMEs or public sector., Small sample size (N=110) limits external validity and statistical power for complex models., Self-reported measures susceptible to social desirability and common-method bias; outcomes are perceptual rather than objective productivity/wage/firm-performance metrics., Cross-sectional design prevents inference about causal direction or long-run effects.

Claims (11)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI infrastructure maturity positively predicted organizational value creation among employees of the sampled Indian IT firms (standardized β = 0.44, p < .001). Organizational Efficiency positive Organizational value creation associated with AI adoption
Reading fidelity high
Study strength medium
n=110
β = 0.44
0.3
Organizational culture positively predicted organizational value creation (standardized β = 0.37, p < .001). Organizational Efficiency positive Organizational value creation associated with AI adoption
Reading fidelity high
Study strength medium
n=110
β = 0.37
0.3
Employee psychological readiness for AI positively predicted organizational value creation (standardized β = 0.28, p < .01). Organizational Efficiency positive Organizational value creation associated with AI adoption
Reading fidelity high
Study strength medium
n=110
β = 0.28
0.3
Organizational size was not a statistically significant predictor of organizational value creation (β = 0.11, p = .09). Organizational Efficiency null_result Organizational value creation associated with AI adoption
Reading fidelity high
Study strength medium
n=110
β = 0.11, p = .09
0.3
AI infrastructure maturity was positively correlated with organizational culture (r = 0.61, p < .01). Organizational Efficiency positive Organizational culture
Reading fidelity high
Study strength medium
n=110
r = 0.61
0.3
AI infrastructure maturity was positively correlated with value creation (r = 0.59, p < .01). Organizational Efficiency positive Organizational value creation
Reading fidelity high
Study strength medium
n=110
r = 0.59
0.3
Psychological readiness was positively correlated with value creation (r = 0.62, p < .01). Organizational Efficiency positive Organizational value creation
Reading fidelity high
Study strength medium
n=110
r = 0.62
0.3
Differences in transformation-related outcomes and preparedness across the six firms were statistically significant (F = 4.45, p < .01). Organizational Efficiency mixed Transformation performance and AI preparedness across firms
Reading fidelity high
Study strength medium
n=110
F = 4.45, p < .01
0.3
Psychological readiness partially mediated the relationship between AI infrastructure and value creation, with a direct effect of 0.32 and an indirect effect of 0.12 (Sobel z = 2.45, p < .05). Organizational Efficiency positive Organizational value creation
Reading fidelity high
Study strength low
n=110
Direct effect β = 0.32; indirect effect β = 0.12; Sobel z = 2.45
0.15
Psychological readiness partially mediated the relationship between organizational culture and value creation, with a direct effect of 0.28 and an indirect effect of 0.09 (Sobel z = 2.10, p < .05). Organizational Efficiency positive Organizational value creation
Reading fidelity high
Study strength low
n=110
Direct effect β = 0.28; indirect effect β = 0.09; Sobel z = 2.10
0.15
The sampled firms reported moderate-to-high mean levels of AI infrastructure, transformation success, value creation, psychological readiness, and organizational culture, with means ranging from 3.54 to 3.89 on a five-point scale. Organizational Efficiency positive Self-reported AI infrastructure, transformation success, value creation, psychological readiness, and organizational culture
Reading fidelity high
Study strength low
n=110
Means ranged from 3.54 to 3.89 on a 1–5 scale
0.15

Notes