| Keywords [eng] |
Generative AI, Non-Technical Founders, MVP Development, Lean Startup, AI Tools, Entrepreneurship, Product Development, Innovation, Product-Market Fit, Product-Relying, Startups |
| Abstract [eng] |
Study Overview: This study discusses the role of Generative AI tools in revolutionizing the MVPs production process of non-technical founders. Using AI tools such as ChatGPT, GitHub Copilot, and Midjourney, non-technical entrepreneurs can have the chance to automatize previously technical processes, such as coding, designing, and content creation. This research will make them more creative and innovative and generate products with fewer resources. The study covered the non-technical founders using the said AI tools, challenges they face, and advantages obtained, and the influence of AI on decision-making, product cycles, and achievement of product-market fit. Study’s Aim: The main objective of the research project was to examine how Generative AI would influence the development of MVP among non-technical founders. It also inquired about the ability of these founders to reduce the time of their development, do additional iterations, and create new products with the help of AI tools. Moreover, this research quantified the effect of AI-generated MVPs on product-market fit, entrepreneurial decision-making, and the entrepreneurial ecosystem in general. Methodology: The study design used in this paper assumes qualitative research, where semi-structured interviews with 15-20 non-technical founders who are on the MVP stage of using AI technology will be conducted. Besides, the 3-5 startup case studies are examined to present more detailed information about the challenges and opportunities of AI implementation in entrepreneurship. The thematic analysis is a data analysis tool that allows identifying significant patterns and themes in the adoption and use of AI tools in the creation of MVP and its effects. Findings and Discussion: These findings indicate that AI tools can be extremely helpful to non-tech founders, as they can be faster and less expensive in creating MVP, as well as be more creative. The utilization of AI, though, has its own problems such as shallow product validation and dependence on AI which could lead to products that are shallow. Among critical themes that can be derived based on the information is the necessity of the needs-based application of AI tools, and the two-sided outcomes of the AI on empowerment (speed and innovation) and the risk of oversimplification. Even though AI will allow founders to create and develop more efficiently, it is not immune to continuous learning, as they will have to process AI-generated outputs and make them efficient. Conclusion: The research identified that Generative AI devices possess an overwhelmingly beneficial influence on the speed of MVP that can be successfully applied by non-technical founders to test and iterate on products with little technical knowledge. However, the research also warns of the potential drawbacks of the excessive use of AI, including the ethical concerns, the problem of intellectual property, and the danger of the loss of technical knowledge. The results indicate that the policy of non-technical founders is to find the balance between the advantages of using AI and being aware of the limitations of AI to achieve sustainability and quality on a long-term level. Suggestions: 1. Non-technical founders are advised to invest in implementing AI tools in their workflow to accelerate the development of MVP without losing essential control over the tool to prevent the superficial validation of the product. 2. Incubators and investors must provide training and facilities to assist the founders in creating a balance between the use of AI and technical skills to secure product quality. 3. The future research must consider how generative AI affects the innovation of products across different industries to define how AI can be applied to other situations. |
