Barb Wixom reads MIT CISR's April 2025 research briefing, which she co-authored with Nick van der Meulen and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2025_0401_DataProductMindset_WixomVanderMeulenBeath.

Abstract: Today, by adopting a product mindset for data, organizations hold people accountable for the payoff of data investments over time. Data assets and data solutions represent two distinct types of “data products.” Organizations can use three sets of product management practices to more effectively generate returns from their data assets and data solutions: practices for satisfying data consumers, practices for setting prices, and practices for sustaining profits. This briefing illustrates these practices using examples from information businesses.

Stephanie Woerner reads MIT CISR's March 2025 research briefing, which she co-authored with Peter Weill, Jennifer S. Banner, and James Moore. See the text version and related content at https://cisr.mit.edu/publication/2025_0301_SavvyBoardsUpdate_WeillWoernerBannerMoore

Abstract: In 2019, MIT CISR published findings from our research on the digital savviness of company boards that showed 24 percent of boards were digitally savvy and had associated performance premiums. In 2024, we repeated the 2019 board analysis with current companies and found that having a digitally savvy board based on our original criteria was no longer differentiating. However, updating the criteria to account for newer technologies produced results similar to the original analysis. In this briefing we compare the results of these two studies, share insights from interviews of non-executive board directors, and explore how companies use board committees to help manage their work.

Barb Wixom reads MIT CISR's February 2025 research briefing, which she co-authored with Nick van der Meulen and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2025_0201_DataMonetizationImpact_vanderMeulenWixomBeath.

Abstract: While organizations generally recognize data’s potential to drive business value, many still struggle to realize substantial financial returns from their data monetization strategies. Beyond selecting a strategy, we identified a more critical differentiator for success: data monetization impact. This briefing explores the strategy-specific practices that enable organizations to achieve this impact on strategic business outcomes, providing guidance for leaders seeking to maximize the return on their investments in data initiatives.

Ina Sebastian reads MIT CISR's January 2025 research briefing, which she co-authored with Stephanie Woerner, Peter Weill, and Daniel Woerner. See the text version and related content at https://cisr.mit.edu/publication/2025_0101_DigitalSustainability_SebastianWoernerWeillWoerner.

Abstract: Sustainability is a cost of doing business for many companies today—but it is also an opportunity for creating value. Companies only achieve value, however, by embedding sustainability goals into company strategy and then developing capabilities to advance these goals. Our survey analysis suggested that there are four distinct strategic sustainability goals: compliance and efficiency, customer and investor reputation, new revenue, and company purpose. Companies in the research that pursued strategic sustainability goals beyond compliance and efficiency saw bottom-line impacts such as greater EBIT, more revenues from innovation, and improved customer experience.

Stephanie Woerner reads MIT CISR's December 2024 research briefing, which she co-authored with Peter Weill and Ina Sebastian. See the text version and related content at https://cisr.mit.edu/publication/2024_1201_EnterpriseAIMaturityModel_WeillWoernerSebastian.

Abstract: With so much excitement and hype around AI, we pursued research that would help leaders make sense of the chaos and understand how enterprises create value with AI. This briefing describes the MIT CISR Enterprise AI Maturity model, which depicts four stages of enterprise AI maturity we identified based on a 2022 MIT CISR survey of 721 companies. We found that financial performance improved at each stage, and we pinpointed capabilities an enterprise needs as it progresses through the stages.

Barb Wixom reads MIT CISR's November 2024 research briefing, which she co-authored with Cynthia Beath and Ja-Naé Duane. See the text version and related content at https://cisr.mit.edu/publication/2024_1101_HighPerformanceDataMonetization_WixomBeathDuane.

Abstract: Top-performing organizations invest in three factors that amplify the financial impact of data monetization: CEO-level data leadership, data value management, and data lifecycle measurement. These high-performance factors establish an organizational culture conducive to maximized data monetization. This briefing defines the three factors and then illustrates them using a case study of technology and services company Wolters Kluwer.

Stephanie Woerner reads MIT CISR's October 2024 research briefing, which she co-authored with Peter Weill, Chris Foglia, and Dorothea Gray. See the text version and related content at https://cisr.mit.edu/publication/2024_1001_MITCISR50Years_WeillWoernerFogliaGray.

Abstract: This briefing celebrates the 50th anniversary of the MIT Center for Information Systems Research (CISR). For 50 years MIT CISR has been a trusted partner of senior IT executives—both learning from them and helping them to create more value in their companies. In the past decade MIT CISR has broadened its engagement to top management teams, digital and data leaders, and boards of directors. Weaving together recollections from long-time business and academic colleagues with responses from a ChatGPT query, we describe the five pillars of MIT CISR’s reputation and include seven lessons learned over the years.

Nick van der Meulen reads MIT CISR's September 2024 research briefing, which he co-authored with Barb Wixom. See the text version and related content at https://cisr.mit.edu/publication/2024_0901_GenAI_VanderMeulenWixom.

Abstract: As generative AI (GenAI) becomes more prevalent, organizations are implementing it in two distinct ways: as broadly applicable tools to enhance individual productivity, and as tailored solutions to achieve strategic business objectives. Based on a series of three consecutive virtual roundtable discussions with data and technology executives on the MIT CISR Data Research Advisory Board, this briefing describes both approaches and highlights their unique challenges and management principles for success.

Peter Weill reads MIT CISR's August 2024 research briefing, which he co-authored with Elizabeth van den Berg, Jason Birnbaum, and Maxime de Planta. See the text version and related content at https://cisr.mit.edu/publication/2024_0801_RealTimeBusiness_WeillvanderBergBirnbaumdePlanta.

Abstract: In a study with Insight Partners, we found that companies operating in the top quartile versus the bottom quartile of “real-time-ness” had more than 50 percent higher revenue growth and net margins—a huge premium. The top-quartile companies automated processes and enabled fast decisions by employees at all levels using trusted and easily accessible data. Real-time decision-making enables digital customer journeys that are more seamless, empowered employee experiences, and increased business agility. This briefing describes what it takes to become a real-time business. We dig into the performance premium and illustrate the journey of becoming a real-time business with a case study of United Airlines.

Barb Wixom reads MIT CISR's July 2024 research briefing, which she co-authored with Ida Someh and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2024_0701_CemexScalingAI_WixomSomehBeath.

Abstract: In recent years, large established organizations have been growing business value by increasing the volume of AI models they have in production, an activity we call scaling AI. MIT CISR research has identified that scaling AI is the result of a learning journey during which an organization learns how to deploy, proliferate, and industrialize AI models. Ideally, the organization builds data liquidity, develops workforce savviness, and leverages scarce resources along the way to achieve AI at scale, which is the state at which organizations cost effectively manage large volumes of interconnected models in production. In this briefing, we describe the AI scaling learning journey at Cemex, a large global construction materials company headquartered near Monterrey, Mexico.