Balancing intelligence and intuition: a human-AI decision support model for strategic technology adoption in SMEs

This article presents a novel decision-support framework, Hybrid AI-Augmented Decision Optimization (HAI-HDM), designed to accelerate and improve technology adoption in small and medium enterprises (SMEs). HAI-HDM bridges artificial intelligence and human expertise to deliver context-aware, data-driven technology recommendations custom-made to the unique challenges of SMEs. The framework has five core components: data acquisition and preprocessing, artificial intelligence (AI)-powered technology ranking, human-AI decision integration, explainable recommendation generation, and adaptive learning. To support analytical insights, HAI-HDM utilizes machine learning algorithms. For transparency and confidence, it integrates explainable AI (XAI) tools SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), making the foundation behind each recommendation interpretable. A key feature is its dynamic weighting mechanism, which adjusts the stimulus of human judgment and AI confidence based on the degree of vagueness and corporate context. The framework also employs reinforcement learning, leveraging feedback from real-world adoption situations to continuously improve its recommendations. A case study focused on cloud technology adoption establishes the practical effectiveness of HAI-HDM, showing how it aligns technological choices with professional constraints and strategic goals. By combining analytical power with human insight, the framework not only supports informed decision-making but also promotes greater trust and accountability in AI-driven strategic planning.