Artificial Intelligence StartUp Preference Ranking by Circular Intuitionistic Fuzzy PROMETHEE

The artificial intelligence (AI) startups' remarkable growth rate requires the use of different types of evaluation techniques to ensure that they will thrive. This research intends to strengthen the evaluation process for AI startups through the implementation of Multi-Criteria Decision Making (MCDM) techniques. The study framework comprises a set of special criteria that are meant to analyze the diverse and complicated aspects of AI startups. These criteria include team competence, market size, competitive analysis, innovation, expected target audience, marketing strategy, and social impact. During the study, five AI startups are evaluated, by means of applying the constructed Circular Intuitionistic Fuzzy PROMETHEE (CIF-PROMETHEE) methodology to generate preferences that reveal strengths and areas for improvement of each startup. The fallback reports aim to provide actionable recommendations that will help startups focus on key areas for improvement. This method not only serves as a tool for individual startups in their strategic business planning, but it also aims at developing a reliable and data-driven AI startup environment. The analysis indicates the suitability of MCDM as a structured framework for holistic evaluation that eases the growth of AI startups with sustainability.