A Data-Driven Framework for Technology Selection in Entrepreneurial Technology Strategy: An AI-Based Predictive Approach

In the rapidly evolving landscape of technological innovation, startups face growing pressure to make informed decisions regarding the adoption of emerging technologies such as artificial intelligence, cloud computing, blockchain, and Internet of Things (IoT) platforms. Traditional methods of technology selection often rely on intuition or limited experience, which can lead to suboptimal outcomes in fast-paced and volatile markets. This study proposes a data-driven framework leveraging artificial intelligence (AI) to support predictive and evidence-based decision-making for technology selection in startups. The framework integrates structured datasets, predictive algorithms (including Random Forest and Gradient Boosting), and key performance indicators (KPIs) to assess and prioritize technological options. By applying the model to real-world startup case scenarios, the research demonstrates how AI can enhance the accuracy, speed, and strategic value of technology-related decisions. The proposed framework aims to bridge the gap between technological opportunity and business agility by enabling startups to select technologies that align with both their strategic goals and operational capacities. This study contributes to the intersection of AI, innovation management, and entrepreneurship by providing a replicable approach that startup founders and innovation managers can adopt to minimize risk and maximize technological fit.

کلیدواژه ها:

Artificial Intelligence ، Technology Selection ، Startups ، Predictive Analytics ، Cloud Computing ، Innovation Management