📄 Abstract
Agentic artificial intelligence — AI systems that plan, act and complete multi-step tasks with limited human intervention — is diffusing through enterprises far faster than earlier generations of enterprise software, yet the productivity gains organisations report from it remain narrow and uneven. Industry evidence collected between 2022 and 2026 places the share of organisations that convert agentic-AI deployment into measurable, enterprise-level value at somewhere between four and twenty-four per cent, depending on measurement method, while national readiness indices place most organisations only in early or intermediate stages of preparedness. This paper reviews the emerging multidisciplinary literature on organisational AI-readiness practice — examined here across three dimensions: organisational culture, manager support and workflow redesign — and its relationship to the productivity gain organisations actually realise from agentic-AI adoption, as distinct from the gain they anticipate or report as adopted. Building on the technology-organisation-environment framework, dynamic-capabilities theory and the productivity J-curve literature on general-purpose technologies, the paper develops a conceptual framework in which the depth of agentic-AI integration mediates the relationship between readiness practice and realised productivity gain, moderated by sector exposure, firm scale and national labour-market context. Five formal propositions are advanced from a structured review of thirty-eight cited works, of which the paper classifies twenty-seven as substantively engaging at least one of the three readiness dimensions, singly or jointly, and eleven as foundational theoretical grounding rather than dimension-specific evidence. Particular attention is given to India's IT-BPM sector, where labour-cost structure and the readiness gaps identified in national adoption indices interact distinctively with agentic-AI economics. The paper closes with an agenda for primary empirical research — particularly longitudinal and matched-pair designs — needed to move the field from association to causal estimation of the readiness–productivity relationship.
🏷️ Keywords
📚 How to Cite:
Dr. N Subbukrishna Sastry, Dr. Manjula Mallya M , ORGANISATIONAL AI-READINESS PRACTICE AND THE REALISED PRODUCTIVITY GAP FROM AGENTIC AI , Volume 13 , Issue 10, October 2026, EPRA International Journal of Socio-Economic and Environmental Outlook(SEEO) , Pages: 8 - 19 ,