A Conceptual Model for AI-Based Technology Management in Smart Engineering Enterprises

Abstract

The integration of Artificial Intelligence (AI) into engineering enterprises has dramatically outpaced the evolution of traditional technology management (TM) frameworks. Conventional TM models—often linear, static, and heuristic-based—fail to leverage the dynamic, data-driven capabilities of Industry 4.0 and 5.0 environments, leading to a dangerous "strategy latency" where decision-making lags behind technological innovation. This article proposes a novel conceptual model for AI-Based Technology Management (AI-TM) in smart engineering enterprises. By synthesizing recent literature (2021–2026) and applying systems theory and dynamic capabilities frameworks, the study develops an integrative architecture where AI agents act as the central nervous system for technology planning, acquisition, deployment, and governance. Unlike legacy models that rely on periodic audits, the proposed model shifts TM to a continuous, real-time adaptive cycle driven by algorithmic sensing and automated seizing. Theoretical contributions include the redefinition of "technology assessment" as a continuous stochastic prediction function rather than a discrete deterministic audit. Managerial implications offer a detailed roadmap for transitioning from digitized to cognitive enterprises, emphasizing the need for a "Strategic Digital Twin." A validation strategy utilizing the Delphi method is proposed to test the model's robustness against expert consensus.