Large organizations increasingly aspire to become AI[foot]We define artificial intelligence (AI) as applied analytics models that have some level of autonomy.[/foot]-powered, but such an ambitious goal requires extraordinary capabilities. MIT CISR research into data monetization capabilities identified five capabilities—data science, data management, data platform, customer understanding, acceptable data use—that at advanced levels enable this goal.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and five AI project narratives published by MIT CISR.[/foot] In this briefing we describe how organizations build their data monetization capabilities to be strong enough to support AI initiatives. In MIT CISR’s July 2022 research briefing we will explore an AI-specific capability we call AI Explanation, or AIX—an emerging enterprise capability required for building trust in AI.
Organizations rely on the five data monetization capabilities to effectively monetize—i.e., generate economic returns from—their data.[foot]B. H. Wixom and L. Owens, “Digital Data Monetization Capabilities,” MIT Sloan CISR Research Briefing, Vol. XIX, No. 4, April 2019, https://cisr.mit.edu/publication/2019_0401_DataMonetizationCapabilities_WixomOwens.[/foot] A data monetization initiative requires the five capabilities regardless of whether it involves performance dashboards, enterprise reporting, business analytics, or artificial intelligence. An organization’s data monetization capabilities mature over time as the organization adopts more and more advanced practices associated with each capability, building on foundational practices with intermediate and then advanced practices.
The five capabilities are interdependent and need to be of similar maturity to collectively enable data monetization initiatives. Pursuing AI requires an advanced level of the data science capability, in which machine learning, specialized computational and statistical techniques (e.g., time series analysis), data scientist hiring and retention, and other advanced data science practices are in play. In addition, AI projects demand that the other four data monetization capabilities are across the board more advanced (see table 1).