Ida Someh reads MIT CISR's July 2022 research briefing, which she co-authored with Barb Wixom and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath.

Abstract: Four characteristics of AI—unproven value, model opacity, model drift, and mindless application—make it challenging to get stakeholders to trust AI solutions. As a result, organizations that strive to become AI-powered adopt practices to produce AI solutions that are trustworthy. Over time, these practices build AI Explanation (AIX) capability: an emerging enterprise capability that arises from practices AI teams use to build stakeholder confidence in AI solutions. In this briefing, we first describe AIX capability and four sets of practices used to build it. We then draw on a case study about the AI journey of Microsoft to illustrate examples of the practices that company has leveraged.

Building AI Explanation Capability for the AI-Powered Organization

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.

Business Value from Digital Sustainability

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. 

Maximizing Returns from Data Monetization Strategies

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.

Digitally Savvy Boards: AI Update

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.

Shifting to a Product Mindset for Data

Stephanie Woerner reads MIT CISR's May 2025 research briefing, which she co-authored with Peter Weill. See the text version and related content at https://cisr.mit.edu/publication/2025_0501_DigitalPlatformDesigns_WoernerWeill.

Abstract: People use the term “digital platform” to mean lots of different things—an internal business capability, a business model, a technology, a business service, or a mindset, among other things. To help executives discuss, design, and manage digital platforms, we studied platform designs. We identified four dominant digital platform designs—platform as a service (PaaS), multisided marketplace, internal platform, and X as a service (XaaS)—and found that companies that effectively managed one or more of the designs had above-average financial performance. In this briefing, we describe the designs and share the financial performance of companies using each design.

Top-Performing Companies Reuse Four Digital Platform Designs

Ina Sebastian reads MIT CISR's June 2025 research briefing, which she co-authored with Stephanie Woerner and Peter Weill. See the text version and related content at https://cisr.mit.edu/publication/2025_0601_ANZInstitutionalPlatformTransformation_SebastianWoernerWeill.

Abstract: As business models become more AI-enabled, strategically designing, evolving, and reusing platforms that reflect a company’s core capabilities is key to driving growth and innovation. This briefing examines the platform transformation of ANZ Institutional, a division of the Australia and New Zealand Banking Group Limited (ANZ). By building robust, scalable internal platforms and connecting them to deliver revenue-generating digital services, ANZ Institutional redefined how it creates and delivers value—doubling its return on equity (ROE) between 2016 and 2024.

Platform-Enabled Business Transformation at ANZ Institutional

Martin Mocker reads MIT CISR's July 2025 research briefing, which he co-authored with Ina Sebastian. See the text version and related content at https://cisr.mit.edu/publication/2025_0701_MonetizingExDPs_MockerSebastian.

Abstract: To gain additional value from their digital platforms, some companies open them to external developers via APIs, thereby creating external developer platforms (ExDPs). Many companies struggle to monetize ExDPs, though. This briefing compares two distinct approaches to monetizing ExDPs: build-on, in which external developers create complementary solutions on top of the company’s platform; and build-in, in which external developers integrate capabilities  from the company’s platform into their own freestanding applications, often combined with data from other companies’ ExDPs. Using case studies of Salesforce and Schneider Electric, this briefing explores the distinct value, requirements, and outcomes of each approach to guide companies in selecting their ExDP monetization strategy.

Two Approaches to Monetizing External Developer Platforms

Stephanie Woerner reads MIT CISR's August 2025 research briefing, which she co-authored with Ina Sebastian, Peter Weill, and Evgeny Káganer. See the text version and related content at https://cisr.mit.edu/publication/2025_0801_EnterpriseAIMaturityUpdate_WoernerSebastianWeillKaganer.

Abstract: Last year, MIT CISR introduced a four-stage enterprise AI maturity framework to help leaders identify how to create value from AI (artificial intelligence) technology. A new MIT CISR survey has found that enterprises today are making significant progress in their AI maturity, with the greatest financial impact seen in the progression from stage 2, where enterprises build pilots and capabilities, to stage 3, where enterprises develop scaled AI ways of working. In this briefing we describe how enterprises mature from piloting AI to scaling it, illustrated by case studies of the Guardian Life Insurance Company of America and Italgas Group.

Grow Enterprise AI Maturity for Bottom-Line Impact

Nick van der Meulen reads MIT CISR's September 2025 research briefing, which he co-authored with Ida Someh, Barb Wixom, and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2025_0901_DataDemocracy_VanderMeulenSomehWixomBeath.

Abstract: Many organizations fail to realize value from their data investments, with only an average of 28 percent of employees drawing on reusable data assets. This briefing positions as a solution establishing a data democracy: an organizational state where all employees have data access, skills, motivation, and guidance for strategic use. We present a framework showing how specific connecting structures, such as centers of excellence and cross-functional teams, can amplify democratization practices. By intentionally designing this organizational state, leaders can increase the use of their organization’s available data assets, achieve greater employee empowerment, and maximize financial returns on their data investments.

Mobilize Your Data Democracy

Ina Sebastian reads MIT CISR's October 2025 research briefing, which she co-authored with Peter Weill, Stephanie Woerner, and Gayan Benedict. See the text version and related content at https://cisr.mit.edu/publication/2025_1001_BizModelsAIEra_WeillSebastianWoernerBenedict.

Abstract: MIT CISR anticipates that rapid advancements in AI will drive business model evolution over the next decade, and we propose that business models in the AI era will become increasingly outcome oriented and enabled by autonomous AI. In this briefing, we draw on our research from 2013 to 2025 involving 2,378 companies to describe how digital business models have developed over the last twelve years, and introduce a framework for AI-driven, real-time business models. We illustrate how companies have begun exploring these new business models with a case study of One New Zealand.

Business Models in the AI Era

A research center at the MIT Sloan School of Management, MIT CISR helps executives meet the challenge of leading dynamic, global, and information-intensive organizations.