Artificial Intelligence, Lean Method and Startup Product Scaling
3 Puzzles about AI and innovation:
- Why most companies have not seen benefits from their AI investment?
- The greater the novelty of an innovation, the less likley there is reliable data available. So can AI really work for innovation?
- Does AI replace experimentation methods in creating innovation, or complement?
This paper is to answer the above questions.
Abstract: Despite variability in returns, many startups have adopted artificial intelligence (AI) to develop new products. We examine the impact of AI capability on startup product innovations, as well as the role of lean methods in explaining some of the variabilities in the return on AI investments. We identify the startups’ internal and external AI capabilities based on the company’s usage of AI in processing internal or external information. Using a novel dataset of about 2,000 startups in China from 2011 to 2020, we find that companies that adopt general AI capability (either internal or external AI) create more innovative products, including both novel products (i.e., products that are new to the market) and incremental products (i.e., new versions of existing products). Moreover, AI investments complement lean methods in product innovations. Separating general AI into internal and external AI capabilities, we find that lean methods complement external AI capabilities in developing novel products, and internal AI capabilities in developing incremental products. Our further analyses show that using lean methods to provide experimentation data and using AI to improve the efficiency of experimentations creates a virtuous cycle that can help alleviate market uncertainties in developing novel products and facilitate product iterations in developing incremental products. These findings are consistent in both software companies and companies that develop physical products.