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.

What’s Next: Top Performers Are Becoming Real-Time Businesses

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

Barb Wixom reads MIT CISR's November 2025 research briefing, which she co-authored with Joaquin Rodriguez, Gabriele Piccoli, and Cynthia Beath. See the text version and related content at https://cisr.mit.edu/publication/2025_1101_DataLiquidityLevers_WixomRodriguezPiccoliBeath.

Abstract: Organizations with big aspirations for leveraging AI technology increasingly need data assets with high data liquidity (i.e., ease of data asset reuse and recombination) to achieve their aims. This briefing draws on the multiyear journey of global manufacturer Caterpillar to illustrate how companies can successfully execute foundational data liquidity tactics by managing three data domain practice areas we call data liquidity levers. These areas—data architecture, data preparation, and data permissioning—function as levers to increase or limit data liquidity.

Data Liquidity Levers at Caterpillar

Alan Thorogood reads MIT CISR's December 2025 research briefing, which he co-authored with Stephanie Woerner. See the text version and related content at https://cisr.mit.edu/publication/2025_1201_EntITOperatingModels_ThorogoodWoerner.

Abstract: Newer digital technologies such as artificial intelligence are blurring the lines between the IT function and business units, offering new approaches to the enterprise IT operating model. As part of these approaches, many top performers are increasing both speed and scale, relying on modularity and reuse architected by strong IT leadership. In this briefing, we examine the pressures on enterprise IT operating models and propose a framework to help enterprise leaders make strategic choices. We also examine the differences in platform use and innovation among the models.

Enterprise IT Operating Models in the AI Era

Nick van der Meulen reads MIT CISR's January 2026 research briefing, which he co-authored with Hippolyte Lefebvre and Barb Wixom. See the text version and related content at https://cisr.mit.edu/publication/2026_0101_GenerativeAIRisk_VanderMeulenLefebvreWixomLegner.

Abstract: Generative AI (GenAI) risks have multiplied as organizations progress from GenAI tools and solutions to AI agents—amplified by massive, decentralized demand. But not all risks are alike. This briefing maps the GenAI risk space, identifying which components of GenAI implementations give rise to risk: from training data and foundation models through prompts, outputs, and use decisions. RAG and AI agents expand this space further. Drawing on 62 interviews with data and technology executives, we distinguish embedded risks—inherited with the technology—from enacted risks, which emerge from organizational choices. Each type of risk requires a different response. Leaders should inventory their GenAI deployments, assign accountability, and establish audit trails that log prompts, outputs, and human interventions.

Mapping the Generative AI Risk Space

Nils Fonstad reads MIT CISR's February 2026 research briefing, which he co-authored with Martin Mocker and Jukka Salonen. See the text version and related content at https://cisr.mit.edu/publication/2026_0201_LeadingDigitalInnovation_FonstadMockerSalonen_Audio.

Abstract:  To survive and thrive in volatile business conditions, organizations increasingly need digital innovation, the process of taking an idea that uses digital technologies from inception to impact. Yet most organizations struggle with digital innovation, as their approach consumes resources out of proportion to the impact it delivers. Our research has found that organizations that innovate successfully rely not on a single heroic executive or centralized unit to lead digital innovation. Rather, they leverage three distinct but complementary types of leaders: (1) initiative leaders, (2) shared resource leaders, and (3) portfolio leaders. This briefing describes how each type works, illustrated with examples from successful innovators.

Leading Digital Innovation

Nick van der Meulen reads MIT CISR's March 2026 research briefing, which he co-authored with Jennifer Jewer and Nadège Levallet. See the text version and related content at https://cisr.mit.edu/publication/2026_0301_GenAIGovernance_VanderMeulenJewerLevallet.

Abstract: Traditional technology governance assumes stable technologies, predictable consequences, and manageable demand. Generative AI (GenAI) upends these assumptions: its pace of adoption outstrips centralized review capacity, while the technology itself transforms faster than conventional governance mechanisms can adapt. This briefing introduces minimum viable governance, a framework designed to match the pace of GenAI while enabling the organization to sense and seize opportunities. Drawing on an in-depth case study and prior MIT CISR research, we identify four characteristics of minimum viable governance that leaders can apply across governance domains.

Minimum Viable Governance for Generative AI

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.

What’s Next: Top Performers Are Becoming Real-Time Businesses

MIT CISR

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