Hybrid Intelligence in the Innovation Process: Benchmark- and Utility-Based Selection of Proprietary Generative AI Models for Design Thinking in SMEs

Abstract

Small and medium-sized enterprises (SMEs) increasingly rely on generative AI to strengthen innovation processes while operating under tight resource and compliance constraints. Building on recent work on Hybrid Intelligence, this study presents a benchmark- and utility-based method to select enterprise-ready proprietary large language models (LLMs) for Design Thinking in EU-based SMEs. Using publicly available data from the Artificial Analysis Intelligence Index v3.0, the Hugging Face LMarena Leaderboard, and GDPR-aligned compliance criteria, we shortlist GPT-5.1, Gemini 3 Pro, Claude 4.5 Sonnet, and Magistral Medium 1.2. A two-stage utility analysis (unweighted and weighted) shows that Gemini 3 Pro consistently achieves the highest overall utility, particularly when reasoning quality, reliability, and speed are prioritised, followed by GPT-5.1. The analysis provides a transparent, replicable selection framework to support SMEs in adopting AI-assisted Design Thinking and outlines a practical foundation for orchestrating multiple models across innovation phases.

Keywords: Hybrid Intelligence, Generative AI, Large Language Models, EU Compliance, Benchmarking, Utility Analysis, Design Thinking, SMEs

DOI: 10.54941/ahfe1007201