Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Science and Information Technology
Gopal Yuvaraj
Unknown Affiliation
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Workforce Unit Abstraction for Governing Hybrid Human and Artificial Intelligence Operations Gopal Yuvaraj
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1811

Abstract

Enterprise service organizations increasingly deploy artificial intelligence agents alongside human workers. Yet, incumbent workforce management frameworks remain anchored to a purely human labor model, rendering AI agents invisible to capacity planning, performance attribution, and governance enforcement. This article addresses that conceptual gap through a design science research methodology, introducing three constructs as reusable primitives for hybrid workforce platform design. The Workforce Unit Abstraction defines a unified seven-attribute operational schema applicable to both human workers and AI agents, enabling consistent representation across planning, scheduling, and governance systems. The Hybrid Capacity Model extends demand-to-supply planning across heterogeneous workforce pools, resolving a multi-objective allocation problem that simultaneously optimizes cost, quality, and risk constraints. Governance-bound autonomy constrains AI Workforce Unit actions within a five-level, policy-enforced autonomy ladder supported by six mandatory governance controls. Together, these constructs provide a coherent reference model that closes the documented gaps in hybrid workforce management, including scheduling inefficiencies of up to 28%, attribution failures in 68% of organizations, and governance ambiguity responsible for 61% of hybrid workflow failures. The framework establishes a principled vocabulary for designing enterprise service platforms that manage human and artificial intelligence labor responsibly, transparently, and at scale.
Co-Authors