From Entrepreneurial Intuition to Algorithmic Intelligence: A Conceptual Framework for AI-Driven Decision-Making and Startup Performance

Abstract

Artificial intelligence (AI) is transforming the way entrepreneurs make decisions by sifting through vast amounts of data, identifying patterns, predicting market trends, and proposing a range of potential scenarios for decision-making – all of which is particularly important when dealing with startups, which often find themselves facing uncertainty, limited resources, information gaps, and a pressing need for quick decision-making. Even as AI becomes ubiquitous, the theoretical knowledge on how AI uptake is associated with improved entrepreneurial decision making, and improved startup performance more broadly, is spread across several streams.

Nevertheless, the theoretical knowledge about how AI uptake relates to improved entrepreneurial decision making and, in turn, improved startup performance is still theoretically divided into several streams despite the diffusion of AI. This conceptual paper combines knowledge from entrepreneurship, information systems, strategic management and organizational decision-making research to create an integrative framework for understanding entrepreneurial decision making using artificial intelligence. The paper draws on the concepts of Technology–Organization–Environment (TOE) theory, Dynamic capabilities theory, Resource-based view of the firm, and Human–AI complementarity theory to conceptualize the organizational mechanism of AI-driven entrepreneurial decision-making capability (AI-EDMC) as how technological resources are transformed into entrepreneurial decisions and performance outcomes. It categorizes the factors related to the adoption of AI, the

AI-EDMC, the outcomes at the decision level, the outcomes at the startup level, the factors of the adoption barriers, and the boundary condition of human-AI complementarity and develops 14 theoretically grounded propositions linking these constructs. The main thesis is that sustainable advantage comes not from eliminating entrepreneurial intuition and relying on algorithms instead, but by combining algorithmic intelligence with entrepreneurial judgment. The framework proposes empirical tests to verify the findings and a research agenda for research on AI for entrepreneurialism, especially in resource-constrained and emerging startup ecosystems.