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AI literacy is emerging as workplace capital that can widen organizational inequality unless HR and management deliberately train, support and reward inclusive AI practices; without intervention, small initial advantages can amplify into persistent career gaps.

The New Digital Divide: The New Digital Divide: A Perspective of AI Literacy as Workplace Capital and Organizational Inequality
Sam Bodunrin, Jemilat Alayinde · August 06, 2026 · International Journal Administration Business and Organization
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The paper argues that AI literacy functions as a form of workplace capital that can translate access to AI into unequal productivity, visibility, and career outcomes, producing an "AI literacy divide" within organizations.

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In the business world, workplace relationships, employee work performance, and business decision-making are all currently being transformed by the broad diffusion of generative Artificial Intelligence (AI). This explosion focuses on productivity effects; however, it is unaccompanied by studies on whether divergent degrees of AI expertise can produce novel labour inequities. This perspective relied on prior work in the fields of AI literacy, the digital divide, human capital, organizational justice, and human-AI teams. This article conceptualizes the AI Literacy Divide. It argues that literacy in AI is becoming a type of workplace capital that mediates whether an individual can translate access to AI to enhance productivity, work quality and career progression. High-AI literacy workers could be relatively privileged recipients of a variety of rewards associated with AI-enabled work, while low-AI literacy workers could be relatively excluded or disadvantaged relative to their work counterparts possessing equivalent levels of non-AI-based work knowledge. We presented a framework on the origins and effects of the AI Literacy Divide and discussed the practical implications for HR management, human capital management, organisational equity, and future research.

Summary

Main Finding

The paper theorizes that AI literacy functions as a form of workplace capital that converts mere access to AI tools into differential productivity, visibility and career rewards. Because organizational processes (training, managerial support, culture, etc.) mediate the translation of AI literacy into outcomes, an "AI literacy divide" can emerge and be amplified over time (a Matthew effect), producing persistent workplace and labour-market inequalities even among workers with similar domain knowledge.

Key Points

  • Concept: AI literacy = the capacity to understand, critically evaluate, responsibly apply and integrate AI tools in domain work. Treated here as workplace capital (akin to human capital).
  • Process pipeline: AI Access → AI Literacy → AI Utilisation Capability → Productivity Enhancement → Career Visibility → Unequal Workplace Outcomes.
  • Moderators that shape the gap: Organizational Training (quality, relevance, accessibility), Managerial Support (promotion, coaching, resources), Learning Opportunities (ongoing mentoring), Digital Confidence (self‑efficacy), Organizational Culture (innovation, psychological safety, knowledge sharing).
  • Feedback/Matthew effect: Early advantages in AI literacy lead to more opportunities/resources → stronger literacy → widening inequality.
  • Employee typology:
    • AI-enhanced: strong domain expertise + high AI literacy → highest gains.
    • AI-dependent: low critical evaluation, heavy reliance on AI → short-term productivity but risk of fragility.
    • AI-excluded: domain expertise but low AI literacy/opportunity → long-term disadvantage.
  • Organizational justice: Lack of AI literacy among some workers can erode perceived fairness and trust in AI-driven decisions.
  • HR implications highlighted: job analysis and selection, differential learning & development, revised performance reviews, recognition/reward tracking, workforce planning focused on reskilling, and transparent employee-relations processes.

Data & Methods

  • Approach: conceptual/theoretical synthesis. The authors build a framework by integrating literatures on AI literacy, digital divide, human capital, human–AI collaboration, and organizational justice.
  • Evidence base: draws on recent empirical and conceptual studies (e.g., Long & Magerko 2020; Cetindamar et al. 2024; Liu et al. 2025; Noy & Zhang 2023; Call et al. 2026) to motivate components of the model and to show that AI literacy is associated with better job performance and productivity gains.
  • Methodological status: no primary empirical data or formal econometric tests in the paper. The contribution is a conceptual model and a set of propositions and HR-practice prescriptions. Authors call for empirical validation across sectors and organization types.

Implications for AI Economics

  • Recasts skill‑biased technological change: AI-driven productivity gains will not be distributed simply by task exposure; heterogeneity in AI literacy will shape who benefits — amplifying returns to certain skills and creating new dimensions of comparative advantage.
  • Wage and rent distribution: AI literacy as workplace capital suggests potential wage premiums for AI-literate workers and greater rent capture by already advantaged employees/firms. This could lead to wider within‑firm and cross‑firm wage dispersion.
  • Labor market sorting and mobility: Firms that invest in training and supportive management will retain and grow AI-enhanced talent; others may see talent flight or create two-tier workforces. Human-capital accumulation models should incorporate organizational mediation of skill returns.
  • Complementarity vs substitution: The framework emphasizes complementarity (human domain expertise + AI literacy) as the route to sustained gains. Models of automation should distinguish between exposure to AI tools and the capacity to use them critically; substitution predictions will differ by worker type.
  • Aggregate productivity and inequality: Firm-level feedback loops (Matthew effects) imply that AI could increase aggregate productivity while also increasing inequality across workers and firms, potentially affecting aggregate consumption and growth dynamics via distributional channels.
  • Measurement and empirical strategy recommendations:
    • Develop validated measures/scales of AI literacy (build on Cetindamar et al. 2024; Liu et al. 2025).
    • Use firm-level rollouts of AI tools for difference-in-differences or event-study designs to estimate causal effects of access conditional on measured literacy.
    • Instrumental-variable approaches or randomized training interventions to separate selection into AI use from training effects.
    • Task-based decomposition (à la Acemoglu & Restrepo) to quantify complementarity between AI and human tasks by literacy strata.
    • Panel analyses linking AI-literacy metrics to wages, promotions, and performance ratings to estimate returns to AI literacy.
  • Policy and firm-level interventions relevant to economics:
    • Subsidized training or tax incentives for employer-provided AI literacy programs to reduce market failures in human-capital investment.
    • Support for measurement/credentialing of AI literacy to reduce information asymmetries in hiring and wage setting.
    • Regulation and standards to ensure transparent, fair allocation of AI-enabled opportunities (to mitigate organizational amplification of inequality).
  • Research agenda points for AI economics:
    • Quantify the wage premium of AI literacy across sectors and occupations.
    • Model dynamic accumulation of AI literacy and feedback loops within firms and implications for long-term inequality.
    • Study heterogeneous firm responses (training-intensive vs laissez-faire) and resulting labour-market structures.
    • Examine macro effects of unequal AI literacy diffusion on aggregate productivity growth, labor force participation, and income distribution.

Overall, the paper prompts economists to treat AI literacy as an important, measurable determinant of who captures the gains from AI — and to incorporate organizational mediation and feedback dynamics into empirical and theoretical models of AI-driven labour market change.

Assessment

Paper Typetheoretical Evidence Strengthn/a — This is a conceptual/perspective article that synthesizes prior empirical and theoretical work to propose a framework; it presents no original causal tests or new empirical estimates. Methods Rigorn/a — No empirical methods, data, or formal model estimation are used—this is a literature-based conceptual framework without systematic review, preregistration, or robustness checks. SampleNo primary data or sample; the paper is a conceptual synthesis drawing on existing literature (literature cited includes both empirical studies and prior conceptual work). Themesskills_training inequality human_ai_collab org_design productivity GeneralizabilityNo empirical validation: framework is hypothetical and untested across organizations or sectors, Organizational heterogeneity (size, industry, national context) not empirically addressed, Cited empirical studies may be concentrated in particular sectors or high-income contexts, limiting external validity, Individual differences (e.g., baseline skills, job tasks) and labor market institutions that shape outcomes are not modeled or measured

Claims (13)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI literacy functions as a form of workplace capital that mediates whether employees can translate access to AI into improved productivity, work quality, and career progression. Organizational Efficiency positive Productivity, work quality, and career progression associated with AI use
Reading fidelity high
Study strength speculative
not reported
0.02
Providing employees with access to generative AI does not by itself create an equal workplace advantage because employees differ in their ability to judge AI accuracy and bias, combine AI output with domain expertise, and apply the output responsibly. Inequality negative Equality of employee benefits from workplace AI access
Reading fidelity high
Study strength speculative
not reported
0.02
AI literacy is associated with better employee work performance. Organizational Efficiency positive Employee job or work performance
Reading fidelity high
Study strength medium
not reported
0.12
Generative AI literacy can positively influence employees' job performance and self-efficacy in creativity. Creativity positive Job performance and creative self-efficacy
Reading fidelity high
Study strength medium
not reported
0.12
AI literacy may account for a larger share of differences in individual workplace outcomes than AI access when access to AI is widespread. Inequality mixed Differences in individual benefits and outcomes from AI use
Reading fidelity high
Study strength speculative
not reported
0.02
AI can amplify disparities in individual employee output, producing increased returns for workers with stronger human-capital capabilities. Inequality negative Differences in individual employee output and returns to capabilities
Reading fidelity high
Study strength medium
not reported
0.12
Generative AI increases productivity when it is accompanied by appropriate human capacity to leverage it. Organizational Efficiency positive Productivity from generative-AI use
Reading fidelity high
Study strength medium
not reported
0.12
The proposed AI Literacy Divide framework links AI access to AI literacy, AI-utilization capability, productivity enhancement, career visibility, and unequal workplace outcomes. Task Allocation mixed Productivity enhancement, career visibility, and differences in appraisals, advancement, pay, opportunities, and careers
Reading fidelity high
Study strength speculative
not reported
0.02
Organizational training, managerial support, learning opportunities, digital confidence, and organizational culture can either widen or narrow AI-literacy-related inequality gaps. Inequality mixed Workplace inequality associated with differences in AI literacy
Reading fidelity high
Study strength speculative
not reported
0.02
Positive initial AI-related outcomes can trigger a Matthew Effect in which access to opportunities, resources, and support further strengthens AI literacy and increases organizational inequality over time. Inequality negative Accumulation of AI-literacy advantages and organizational inequality over time
Reading fidelity high
Study strength speculative
not reported
0.02
Employees with high AI literacy and strong domain expertise are expected to be the most competitive and most likely to benefit from AI-enabled work systems. Employment positive Competitiveness and benefits from AI-enabled work
Reading fidelity high
Study strength speculative
not reported
0.02
Employees with low critical-evaluation ability who frequently use AI may experience higher short-term productivity but are likely to become overly reliant on AI output and fall behind without intervention. Skill Obsolescence mixed Short-term productivity and longer-term disadvantage associated with AI dependence
Reading fidelity high
Study strength speculative
not reported
0.02
Low AI literacy can reduce workers' perceptions of transparency in AI-powered decision-making and ultimately erode trust in those decisions. Ai Safety And Ethics negative Perceived transparency and trust in AI-powered decisions
Reading fidelity high
Study strength speculative
not reported
0.02

Notes