Investigating the Role of Pre-Trained AI Models and Automation in Reducing Operational Costs and Enhancing Innovation in New Ventures

Abstract

New ventures often suffer from resource limitations, limited work force, and financial uncertainties, which affect their innovation and development processes. New developments in the field of pre-trained AI models and automated processes can be used to address these problems because they allow organizations to scale easily with relatively inexpensive solutions. The current study analyzes how pre-trained AI models and workflow automation affect cost savings and innovations in new ventures. For this research, a quantitative research methodology with a survey Structural Equation Modeling approach was used. Data were gathered from 312 founders, co-founders, operations managers, and tech leaders of AI ventures working for less than five years. The impact of the depth of integration of AI solutions and the degree of workflow automation on the variation of operational cost and innovation generation was analyzed. To test the hypotheses, reliability testing, confirmatory factor analysis, and structural equation modeling were conducted. The results show that the use of AI greatly contributes to increased efficiency and innovation capabilities. On average, the ventures saw a cost reduction of operations by 23.8%, where the largest cost reduction was seen in customer service automation (29.4%), creation of content and marketing (26.8%), and software development assistance (24.7%). Innovations were also positively affected as high mean values were seen in developing new product features (4.37), fast prototyping (4.29), and innovations in customer experiences (4.25). Additionally, it is seen that the ventures utilizing human expertise in tandem with AI saw better results in terms of innovation. It can be concluded from the research that using pre-trained AI models and automation technologies helps entrepreneurs gain a dual strategic benefit, as it both reduces the cost of operations and promotes innovation. The results contribute to the field of entrepreneurship studies through the application of Resource-Based View and Dynamic Capabilities theories.