Constructing Actionable Insights: The Missing Link Between Data, Artificial Intelligence, and Organizational Decision-making

Arisa Shollo, Robert D. Galliers

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationResearch Handbook on Artificial Intelligence and Decision Making in Organizations
EditorsIoanna Constantiou, Mayur P. Joshi, Marta Stelmaszak
Number of pages19
Place of PublicationCheltenham
PublisherEdward Elgar Publishing
Publication date2024
Pages195–213
Chapter11
ISBN (Print)9781803926209
ISBN (Electronic)9781803926216
DOIs
Publication statusPublished - 2024
SeriesResearch Handbooks in Business and Management

Keywords

  • Actionable insights
  • Data-driven decision making
  • Data-intensive technologies
  • Analytics
  • Big data
  • Artificial intelligence

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