MIT CISR

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108

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Oct 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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October 15, 202613 min

Enduring Lessons from Fifty-Two Years of MIT CISR Research

Stephanie Woerner reads MIT CISR's October 2026 research briefing, which she co-authored with Peter Weill. See the text version and related content at https://cisr.mit.edu/publication/2026_1001_EnduringLessonsfrom52Years_WeillWoerner. Abstract: Founded in 1974, the MIT Center for Information Systems Research (CISR) will cease operations on October 31, 2026. Throughout fifty-two years as a practice-oriented research center, MIT CISR has conducted hundreds of projects studying how top-performing enterprises generate business value from technology. Technologies change, but the essential management practices endure: adapt your business model, rethink your operating model, focus on critical success factors, build data capabilities, clarify governance, and invest in learning and change—and then repeat. In this final briefing, we discuss six of the center’s most impactful research insights, summarizing the takeaways we’ve published and then extrapolating their relevance to 2027.

September 22, 202612 min

Establishing Guardrails on the AI Roller Coaster

Alan Thorogood reads MIT CISR's September 2026 research briefing, which he co-authored with Peter Reynolds and Asif Gill. See the text version and related content at https://cisr.mit.edu/publication/2026_0901_AIRollerCoaster_ThorogoodReynoldsGill. Abstract: A surge of energy, investment, and optimism is driving organizations to adopt artificial intelligence (AI). However, these efforts carry risks, including wasted resources from redundant investments and added operational complexity and costs. Like a roller coaster, AI initiatives require carefully designed safeguards to move fast but avoid calamity. As these initiatives proliferate throughout the organization, implementing unique controls for every AI application becomes unsustainable. Drawing on interviews, this briefing identifies four domains where guardrails matter most for managing AI and effective controls, illustrated by a case vignette about fintech Tunic Pay.

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.