Visual artificial intelligence as a methodological frontier in entrepreneurship research: a roadmap and empirical demonstration

Abstract Visual artificial intelligence (AI) is beginning to enter entrepreneurship research but adoption remains scarce, even as recent advances in vision-language models and AI-assisted coding are substantially lowering the technical barriers to entry. Combined with the abundance of visual data now available, visual AI opens new opportunities to study questions previously out of reach, while also raising methodological and ethical challenges. This paper provides a structured roadmap for using visual AI in entrepreneurship research: We map visual data sources, outline visual AI approaches, identify research insights, and propose an ethics audit. Ultimately, we illustrate the application of this visual AI pipeline with an example study: a facial image classification model distinguished between entrepreneur and non-entrepreneur portrait images in a platform-specific dataset with 79.5% accuracy, far outperforming human experts, raising important methodological and ethical questions. Plain English Summary Entrepreneurial life is inherently visual, from pitch decks and startup logos to founder emotions in fundraising presentations, workspaces, product designs, and satellite maps of innovation hubs, yet this rich visual panorama has been largely ignored by researchers. This paper provides a step-by-step guide for using visual AI in entrepreneurship research: how to find and select visual data, which AI methods fit which research questions, and how to build in ethical safeguards from the start. Such research comes with real ethical challenges around privacy, consent, algorithmic bias, and the risk of reinforcing existing biases and stereotypes, all of which the paper addresses throughout. To show what this looks like in practice, we include an example study in which an AI model classified professional portrait images as belonging to entrepreneurs or non-entrepreneurs with 79.5% accuracy, far outperforming human experts. This result may reflect differences in professional self-presentation and platform-specific conventions that are imperceptible to the human eye, but it also illustrates how easily such findings can be misinterpreted or taken out of context in ways that raise serious ethical concerns. The key implication for researchers is that visual AI can open genuinely new empirical territory, but responsible use demands that ethical considerations are treated as part of the method, not an afterthought.