Towards an Evaluative AI Framework for Hypothesis-Driven Strategic Decision-Making in SMEs

This research presents an approach based on the Evaluative AI paradigm, where AI not only provides recommendations after a decision is made but also actively assists humans throughout the decision-making process. The framework focuses on making responsible, transparent, and participatory decision intelligence feasible for small and medium-sized enterprises (SMEs) without requiring huge budgets or complex infrastructures.

The approach involves three key components:

• Evidence-based co-reasoning: AI supports human decision-makers in evaluating data and evidence
• Hypothesis testing: AI assists in exploring strategic options through scenario analysis
• Context-aware reasoning: AI provides decision support that considers organizational context, constraints, and objectives

This work is part of an Industrial PhD at the Universitat Oberta de Catalunya (UOC) within the Doctorats Industrials program of the Generalitat de Catalunya, following the Design and Creation methodology (Oates, 2006).

Small- and medium-sized enterprises (SMEs) face dynamic and competitive environments where resilience and data-driven decision-making are critical. Despite the potential benefits of artificial intelligence (AI), machine learning (ML), and optimization techniques, SMEs often struggle to adopt these tools due to high costs, limited training, and restricted hardware access. This study reviews how SMEs can employ heuristics, metaheuristics, ML, and hybrid approaches to support strategic decisions under uncertainty and resource constraints. Using bibliometric mapping with UMAP and BERTopic, 82 key works are identified and clustered into 11 thematic areas. From this, the study develops a practical framework for implementing and evaluating optimization strategies tailored to SMEs’ limitations. The results highlight critical application areas, adoption barriers, and success factors, showing that heuristics and hybrid methods are especially effective for multi-objective optimization with lower computational demands. The study also outlines research gaps and proposes future directions to foster digital transformation in SMEs. Unlike prior reviews focused on specific industries or methods, this work offers a cross-sectoral perspective, emphasizing how these technologies can strengthen SME resilience and strategic planning.

Recommended citation: Molina-Abril, G., Calvet, L., Riera, D. (2026). Towards an Evaluative AI Framework for Hypothesis-Driven Strategic Decision-Making in SMEs. In: Martínez, L., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2025. IDEAL 2025. Lecture Notes in Computer Science, vol 16238. Springer, Cham. https://doi.org/10.1007/978-3-032-10486-1_34

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