MIT CISR

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106

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Aug 2026

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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.

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August 20, 202615 min

AI Value Creation: Five Provocative Propositions

Stephanie Woerner reads MIT CISR's August 2026 research briefing, which she co-authored with Ina Sebastian, Gayan Benedict, Nick van der Meulen, Martin Mocker, Peter Reynolds, and Peter Weill. See the text version and related content at https://cisr.mit.edu/publication/2026_0801_AIValuePropositions_Woerner. Abstract: AI adoption is now widespread—but adoption is not the same as value creation. AI foundation models and agents are capabilities that every one of your competitors can also leverage. The winners, then, may not be the fastest adopters, but the companies that most effectively turn AI into differentiated value. For this briefing our team crafted five propositions about effective management practices to drive AI business value, based on current MIT CISR research.

July 16, 202614 min

Mind and Hand: A Decade of Data Monetization Research

Nick van der Meulen reads MIT CISR's July 2026 research briefing, which he co-authored with Barb Wixom, Cynthia Beath, and the MIT CISR Data Research Advisory Board. See the text version and related content at https://cisr.mit.edu/publication/2026_0701_DataMonetization_VanderMeulenWixomBeathDataBoard. Abstract: Over a decade of collaboration between MIT CISR researchers and the MIT CISR Data Research Advisory Board has produced a research model that explains how organizations translate data assets into financial performance. The model traces a causal chain from core data capabilities through liquid data assets, organizational data democracy, and effective monetization initiatives to measurable business outcomes. Data Board leaders bring this model to life through three principles: (1) managing data assets as products with dedicated owners and lifecycles, (2) mobilizing data monetization across the organization, and (3) systematically realizing value through disciplined measurement and income statement accountability. Organizations embracing these principles unlock competitive advantage and maximize their data investment returns.

June 24, 202613 min

Designing Decision Rights for AI

Ina Sebastian reads MIT CISR's June 2026 research briefing, which she co-authored with Peter Weill, Thomas Haskamp, and Jan vom Brocke. See the text version and related content at https://cisr.mit.edu/publication/2026_0601_AIDecisionMatrix_SebastianWeillHaskampVomBrocke. Abstract: One of the biggest challenges companies face today is designing decision rights between humans and AI. For example, should AI be used in hiring, and if so, for which tasks? Should AI run marketing campaigns end to end, or augment human judgment? We identified two key dimensions—ambiguity and risk—that help determine how humans and AI that can complete tasks autonomously should share decisions. By mapping decisions across these dimensions, companies make explicit what stakes they’re willing to accept and who bears the consequences. As business decisions vary in ambiguity and risk, leaders need to adjust how AI participates in three activities: framing decisions, acting on them, and learning from the outcomes.

May 21, 202615 min

The Case for a Semantic Layer

Barb Wixom reads MIT CISR's May 2026 research briefing, which she co-authored with Hippolyte Lefebvre, Christine Legner, Nick van der Meulen, and Cynthia M. Beath. See the text version and related content at https://cisr.mit.edu/publication/2026_0501_SemanticLayer_LefebvreWixomLegnerVandermeulenBeath_Audio. Abstract: A semantic layer is a system of technologies and techniques that organizes and maintains a consistent and unified representation of data from various sources that is interpretable by humans and machines. MIT CISR research indicates that leaders are feeling pressure to increase their investments in semantic technologies and techniques that can store metadata and instantaneously make data assets more useful to business users, AI models, and AI agents. This briefing describes a target state for a semantic layer and offers three next steps that leaders can take to ensure their investments in semantic technologies and techniques pay off.

April 27, 202614 min

Leveraging Digital Colleagues for Enterprise Value

Stephanie Woerner reads MIT CISR's April 2026 research briefing, which she co-authored with Peter Weill. See the text version and related content at https://cisr.mit.edu/publication/2026_0401_DigitalColleagues_WeillWoerner. Abstract: Digital colleagues—AI-enabled systems that collaborate with humans to perform complex work—are rapidly transitioning from experimentation to an embedded enterprise capability. Unlike traditional automation or reactive AI assistants, digital colleagues integrate generative AI, agentic systems, machine learning, use of enterprise data, and embedded governance mechanisms to function as members of human teams. They may perform tasks autonomously, engage in dialogue, learn over time, operate continuously, and escalate consequential decisions to humans. This briefing describes digital colleagues, where value from their use is emerging, and the organizational choices that distinguish higher-performing enterprises.

March 19, 202617 min

Minimum Viable Governance for Generative AI

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.

February 19, 202615 min

Leading Digital Innovation

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.

January 15, 202615 min

Mapping the Generative AI Risk Space

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.

December 18, 202511 min

Enterprise IT Operating Models in the AI Era

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.

November 20, 202516 min

Data Liquidity Levers at Caterpillar

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.