Authors :
Ravi Kumar Neelayapalem
Volume/Issue :
Volume 11 - 2026, Issue 3 - March
Google Scholar :
https://tinyurl.com/2psp3888
Scribd :
https://tinyurl.com/mr6thhbx
DOI :
https://doi.org/10.38124/ijisrt/26mar902
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Artificial intelligence is increasingly presented as a transformative technology capable of improving productivity,
decision-making, and innovation across sectors. However, many organizations experience difficulty translating experimental
AI initiatives into sustained operational outcomes. These challenges frequently arise not from technological limitations but
from gaps in organizational capability and human capital readiness.
Building upon the Human Capital Audit Framework introduced in the author’s earlier research, this study examines
the relationship between human capital governance and artificial intelligence adoption readiness. Through conceptual
analysis and illustrative case observations from Indian industry contexts—including manufacturing, retail, and service
sectors—the paper proposes a Human Capital AI Readiness Model that identifies key capability dimensions influencing
successful AI implementation.
The study argues that leadership readiness, workforce adaptability, process maturity, data governance capability, and
organizational learning culture collectively determine whether AI initiatives generate meaningful value. Artificial
intelligence therefore functions as a stress test for existing human capital governance systems. Organizations with unresolved
capability impairments often struggle to scale AI initiatives beyond pilot stages.
The paper contributes to the emerging literature on AI adoption by linking technology transformation with human
capital governance and proposes a diagnostic perspective for assessing AI readiness in organizations and economies.
Keywords :
Human Capital Audit, Artificial Intelligence Adoption, Organizational Capability, Workforce Readiness, Digital Transformation, AI Governance.
References :
- Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis. University of Chicago Press.
- OECD (2019). Skills and Productivity: The Role of Human Capital. OECD Publishing.
- World Economic Forum (2020). The Future of Jobs Report.
- Ravi Kumar Neelayapalem (2026). A Human Capital Audit Framework: Detecting Capability Impairment in Organizations and Economies. International Journal of Innovative Science and Research Technology.
Artificial intelligence is increasingly presented as a transformative technology capable of improving productivity,
decision-making, and innovation across sectors. However, many organizations experience difficulty translating experimental
AI initiatives into sustained operational outcomes. These challenges frequently arise not from technological limitations but
from gaps in organizational capability and human capital readiness.
Building upon the Human Capital Audit Framework introduced in the author’s earlier research, this study examines
the relationship between human capital governance and artificial intelligence adoption readiness. Through conceptual
analysis and illustrative case observations from Indian industry contexts—including manufacturing, retail, and service
sectors—the paper proposes a Human Capital AI Readiness Model that identifies key capability dimensions influencing
successful AI implementation.
The study argues that leadership readiness, workforce adaptability, process maturity, data governance capability, and
organizational learning culture collectively determine whether AI initiatives generate meaningful value. Artificial
intelligence therefore functions as a stress test for existing human capital governance systems. Organizations with unresolved
capability impairments often struggle to scale AI initiatives beyond pilot stages.
The paper contributes to the emerging literature on AI adoption by linking technology transformation with human
capital governance and proposes a diagnostic perspective for assessing AI readiness in organizations and economies.
Keywords :
Human Capital Audit, Artificial Intelligence Adoption, Organizational Capability, Workforce Readiness, Digital Transformation, AI Governance.