Constructing actionable insights: the missing link between data, artificial intelligence, and organizational decision-making

In their attempt to create social and economic value, organizations are embracing data-driven decision making and investing in data-intensive technologies. The promise is that these investments will lead to better decisions being made. Advocates often cite the “objectivity” of data and algorithms, the “strength of large numbers” and the processing power of these technologies. Nonetheless, while important, such technological advances - by themselves - don’t guarantee better decisions. This applies, also, to the newest AI technologies, where users need to make sense of ML-based predictions or when prompting Large Language Models (LLMs) with a view to gaining insight and acting accordingly. Recently, the concept of ‘actionable insight’ has surfaced to describe insight outcomes that facilitate action. In this chapter, we showcase the construction of actionable insights for a programmed, operational decision using data analytics. Through this case, we demonstrate how organizations construct actionable insights through conceptualization, contextualization and subjectification processes. We also show that the construction of actionable insights is an emergent and ongoing human endeavor and go on to consider implications for future research.