Timeless AI as a Governance-Anchored Strategic System for Sustainable Business and Industrial Management

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Authors: Ahmed Sidrat Rahman Ayon
Cited by: 0

Abstract

Artificial Intelligence (AI) is increasingly embedded in both strategic business management and industrial operations, promising unprecedented efficiency, predictive capability, and operational optimization. However, organizations face a recurring challenge: many AI initiatives fail to create sustained value. These failures are rarely due to technological inadequacy. Instead, they arise from short-term implementation, weak governance, insufficient accountability, and misalignment with sustainability objectives. Treating AI as a temporary tool or optimization project leads to fragmented adoption, operational risk, and erosion of stakeholder trust. This article introduces Timeless AI, a conceptual framework for governance-anchored, sustainability-aligned AI that functions as a permanent managerial system. Timeless AI integrates strategic business management, industrial operations, governance mechanisms, and sustainability objectives into a single framework. The study draws on strategic management theory, industrial operations literature, and corporate governance principles to illustrate how AI can provide long-term competitive advantage when institutionalized rather than deployed opportunistically. The framework emphasizes accountability, transparency, ethical compliance, and lifecycle sustainability. Governance structures coordinate AI-enabled strategy, operational execution, and sustainability outcomes. The article concludes with managerial and policy implications, providing actionable guidance for organizations seeking to transition from short-term AI adoption to enduring, value-generating, governance-driven AI systems.

Fields of Study & Subjects

Impact of AI and Big Data on Business and Society Information Technology Governance and Strategy Artificial Intelligence Applications

Publication details

Journal / source
Mendeley Data
Publication date
16 February 2026
Language
EN
Document type
Dataset