AI-Driven Innovation: Leveraging Big Data Analytics for Innovation

Aniekan Essien

Chapter 6 in Innovation Analytics:Tools for Competitive Advantage, 2023, pp 119-137 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Technological advancement and data ubiquity have resulted in the rapid and continuous development of data computing and processing tools, hardware, and software, which have impacted all facets of human endeavor. Artificial Intelligence (AI) is increasingly being applied in non-routine tasks that were earlier performed by humans alone. Despite the wave of AI in automation felt in other industries, the innovation sector is yet to feel a widespread adoption of AI in the innovation process. This chapter builds on existing literature to conceptualize the application of innovation analytics — referring to the application of AI-enabled, data-driven insights, algorithms, and visualizations within the innovation process. It is argued that AI has the potential to play a vital role in fostering innovation by driving key aspects within the innovation process. Using a hypothetical example of a tech start-up, we show an example of how infusing AI/big data analytics can serve as key enablers/triggers in the overall innovation process. Further, the chapter explicates the benefits and limitations of using AI in innovation and concludes by providing some implications of applying this technique in the innovation process.

Keywords: Innovation; Analytics; Business Management; Product Development; Process Innovation (search for similar items in EconPapers)
JEL-codes: O31 O32 O33 (search for similar items in EconPapers)
Date: 2023
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