AI-Driven Technology Decision Intelligence for Human-Centered Digital Transformation

The Technology Decision Intelligence Engine (TDIE): Integrating Outcome Prediction, Readiness and Risk Assessment, Multi-Objective Portfolio Optimization, Explainable AI, and Human-AI Collaborative Decision-Making. Organizations must repeatedly choose among competing technology investments - artificial intelligence (AI) adoption, cloud migration, cybersecurity, data platforms, enterprise system modernization, automation, and workforce training - under binding limits on budget, implementation capacity, skills, time, and risk tolerance. In practice these choices are fragmented across departments, driven by intuition or consultants, and judged by isolated metrics such as return on investment (ROI). This paper proposes the Technology Decision Intelligence Engine (TDIE), an AI-driven decision-support framework that treats technology selection as a constrained portfolio problem rather than a ranking problem. TDIE integrates ten components in a closed loop: an organizational data layer that combines structured data with document understanding; organizational diagnosis of maturity, gaps, and readiness; technology opportunity detection; machine-learning prediction of cost, benefit, adoption probability, and ROI; multi-dimensional readiness and risk assessment; multi-objective portfolio optimization that returns a Pareto set instead of a single score; explainable AI using feature attribution and counterfactuals; structured human-AI collaboration in which managers can accept, modify, or reject recommendations with reasons recorded; and a feedback loop that compares predicted with realized outcomes. The paper specifies a rigorous mathematical formulation, a hybrid AI architecture in which large language models are restricted to extraction and managerial explanation, a defensible dataset strategy that labels synthetic data as synthetic, ten falsifiable experiments, strong baselines including AHP, TOPSIS, and ROI-only ranking, and a full evaluation, ablation, and robustness protocol. Empirical validation is specified as a complete, falsifiable evaluation protocol, so that the framework can be implemented and tested exactly as described.