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MIT Sloan School of Management

@MITSloan

Insights for your work from MIT experts

 Cambridge, MA
mitsloan.mit.edu
Joined September 2007
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Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)Photo by MIT Sloan School of Management (@MITSloan)
Artificial intelligence is fueling innovation across industries, but it’s also introducing unanticipated risks and consequences for organizations. In this landscape, traditional enterprise risk management frameworks are no longer sufficient. mitsloan.mit.edu/ideas-made-…
The 5 forces creating urgency around ethical AI adoption | MIT Sloan

The 5 forces creating urgency around ethical AI adoption | MIT Sloan

In his book “Boundaries of Tolerance,” MIT Sloan School of Management senior lecturer Jeffrey Saviano argues that ethical AI strategy and governance are the defining leadership challenges of our time.

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Since every organization has access to the same models, productivity gains alone are table stakes; lasting advantage comes from combining those resources with agentic AI to create new products, services, and business models that rivals cannot replicate. mitsloan.mit.edu/ideas-made-…
Stop debating automation vs. augmentation | MIT Sloan

Stop debating automation vs. augmentation | MIT Sloan

As organizations begin deploying agentic systems that can execute entire workflows, a consequential question is emerging: How can leaders turn agentic AI productivity gains into business growth?

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Artificial intelligence can help with traditional market research problems like long wait times and low quality data. MIT Sloan’s @johnjhorton says that LLMs can deliver informal market insights that can later be refined through investigation and more formal research practices. Learn more: bit.ly/3W2420m
Photo of MIT Sloan associate professor John Horton with quote on using generative AI as a tool for market research: "Companies might build and test three or four variants of something, but they're not going to test 700,000. Being able to pre-process and surface problems is really powerful."

ALT Photo of MIT Sloan associate professor John Horton with quote on using generative AI as a tool for market research: "Companies might build and test three or four variants of something, but they're not going to test 700,000. Being able to pre-process and surface problems is really powerful."

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In his new book, “Boundaries of Tolerance,” MIT Sloan’s Jeffrey Saviano says that AI’s novel risks and “ethical infirmities” have made governance and strategy a leadership necessity. mitsloan.mit.edu/ideas-made-…
The 5 forces creating urgency around ethical AI adoption | MIT Sloan

The 5 forces creating urgency around ethical AI adoption | MIT Sloan

In his book “Boundaries of Tolerance,” MIT Sloan School of Management senior lecturer Jeffrey Saviano argues that ethical AI strategy and governance are the defining leadership challenges of our time.

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From the classroom to the factory floor, @MIT alumni are helping build the next era of American manufacturing. At @ReBuildMFG, MIT Leaders for Global Operations (LGO) alumni — including co-founders @jeffawilke, LGO ’93, and Miles Arnone, LGO ’93, along with Amanda Taplett Johnson, LGO ’07, and Lauren Heintz, LGO ’23 — bring deep expertise in manufacturing, operations, engineering, and management to complex industry challenges. Their work to ensure the next generation of important products are made, at scale, in America aligns with the mission of the MIT Initiative for New Manufacturing (INM), an MIT-wide effort driving research, education, and collaborations to transform the future of manufacturing in the United States and beyond. @MITSloanAlumni @MIT_alumni
Jeff Wilke (@jeffawilke) profile pictureJeff Wilke
@jeffawilkeOct 27
Five years ago, Miles Arnone, Will Barker and I started Re:Build because we believed deindustrialization was an economic failure and a national security risk. Today we announced $2.1B in new funding, led by General Catalyst and JPMorganChase’s Strategic Investment Group. Our mission is to ensure the next generation of important products are made, at scale, in America.
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The conventional fear about artificial intelligence and jobs is that disruption will arrive suddenly — a wave that knocks workers over before they have time to adapt. But new research from MIT FutureTech suggests a different future. mitsloan.mit.edu/ideas-made-…
How will AI automation hit — like a crashing wave or a rising tide? | MIT Sloan

How will AI automation hit — like a crashing wave or a rising tide? | MIT Sloan

AI performance is improving across many workplace tasks broadly, not in sudden shocks, new MIT research finds. That cadence gives companies and workers time to prepare for task-level displacements.

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“The defining leadership challenge of our time is turning agentic AI-enabled productivity gains into new value for the business and new possibilities for work.” — Kate Kellogg and Rama Ramakrishnan, MIT Sloan mitsloan.mit.edu/ideas-made-…
Stop debating automation vs. augmentation | MIT Sloan

Stop debating automation vs. augmentation | MIT Sloan

As organizations begin deploying agentic systems that can execute entire workflows, a consequential question is emerging: How can leaders turn agentic AI productivity gains into business growth?

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Organizations are eager to learn whether artificial intelligence can boost worker productivity. Studies involving software developers, accountants, and manufacturing firms have shown that it can. But that boost doesn’t necessarily translate to final outcomes. Learn more: bit.ly/4rvlR3P
A chart titled "AI Boosts Coders' Output, But Not Their Releases" shows data on three AI tools: Autocomplete Tools, Sync Agents, and Async Agents. It lists their respective percentages for lines of code, commits, and releases. Autocomplete Tools: 228% lines of code, 36% commits, 10% releases. Sync Agents: 741% lines of code, 109% commits, 20% releases. Async Agents: 658% lines of code, 34% commits, with releases marked as "n/a". A note explains that AI tools boost code-writing but less so workflow tasks. The source is MIT Sloan School of Management, 2026.

ALT A chart titled "AI Boosts Coders' Output, But Not Their Releases" shows data on three AI tools: Autocomplete Tools, Sync Agents, and Async Agents. It lists their respective percentages for lines of code, commits, and releases. Autocomplete Tools: 228% lines of code, 36% commits, 10% releases. Sync Agents: 741% lines of code, 109% commits, 20% releases. Async Agents: 658% lines of code, 34% commits, with releases marked as "n/a". A note explains that AI tools boost code-writing but less so workflow tasks. The source is MIT Sloan School of Management, 2026.

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Now that agentic systems can execute entire workflows rather than just assist with tasks, leaders need to examine how people, agents, workflows, and business strategy can work together to create new value. mitsloan.mit.edu/ideas-made-…
Stop debating automation vs. augmentation | MIT Sloan

Stop debating automation vs. augmentation | MIT Sloan

As organizations begin deploying agentic systems that can execute entire workflows, a consequential question is emerging: How can leaders turn agentic AI productivity gains into business growth?

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Whether artificial intelligence makes jobs better, not just faster, depends on the choices employers make in deploying the technology. mitsloan.mit.edu/ideas-made-…
10 levers for shaping generative AI that truly improves worker performance | MIT Sloan

10 levers for shaping generative AI that truly improves worker performance | MIT Sloan

A report from the MIT Working Group on Generative AI & the Work of the Future identifies three principles and seven outcomes that ensure that jobs are better, not just faster, with AI.

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MIT Sloan associate professor @johnjhorton makes the case that large language models can effectively deliver informal market insights that can later be refined through further investigation and more formal research practices. mitsloan.mit.edu/ideas-made-…
Generative AI as a tool for market research | MIT Sloan

Generative AI as a tool for market research | MIT Sloan

Market research is essentially a prediction problem. MIT Sloan’s John Horton says organizations should consider using generative AI to simulate the effects of their decisions before they make them.

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It’s possible for privacy-preserving technologies to coexist with data analytics. Researchers can protect individual privacy and still get reliable results from data. mitsloan.mit.edu/ideas-made-…
Data or privacy? Businesses can have both, MIT Sloan research finds | MIT Sloan

Data or privacy? Businesses can have both, MIT Sloan research finds | MIT Sloan

Data regulation doesn’t necessarily cause economic harm. Markets — and consumers — can still derive economic benefits from data collection when it’s tempered by reasonable privacy guardrails.

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We asked three new MIT Sloan faculty members what their research or experience has revealed about the intersection between AI and work. Here’s what they had to say. mitsloan.mit.edu/ideas-made-…
What 3 new MIT Sloan professors have learned about AI — and what they want to know | MIT Sloan

What 3 new MIT Sloan professors have learned about AI — and what they want to know | MIT Sloan

New faculty members are thinking about how AI can help decision-making, the value of adversarial AI agents, and the skills workers need in the AI era.

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Innovations from the delta V showcase include a trust layer for AI agents, an autonomous underwater vehicle for overboard rescues, a hands-free pump for mobile blood transfusions, and AI diagnostics for property owners. mitsloan.mit.edu/ideas-made-…
13 MIT startups to watch in 2026 | MIT Sloan

13 MIT startups to watch in 2026 | MIT Sloan

The 2026 MIT delta v Demo Day showcased startup innovations in biotechnology, healthcare, AI, cybersecurity, robotics, finance, and manufacturing.

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AI can help organizations create more collaborative, repeatable innovation processes, but humans should stay in charge. mitsloan.mit.edu/ideas-made-…
How to level up systematic innovation with AI | MIT Sloan

How to level up systematic innovation with AI | MIT Sloan

Artificial intelligence can boost the systematic innovation of products, processes, and services. MIT Sloan professor Steven Eppinger offers four steps for making that happen.

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What tasks should AI be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges of the AI era. Researchers at the MIT Center for Information Systems Research have developed a framework to help. Learn more: bit.ly/4zed20M
This image is a diagram titled "The AI Decision Matrix: A framework for determining how humans and autonomous AI agents should participate in decision-making based on two dimensions: ambiguity and risk," from MIT Sloan School of Management. It features a 2x2 grid with "Ambiguity" on the y-axis (ranging from low to high) and "Risk" on the x-axis (ranging from low to high). The quadrants are labeled as follows: top left is "Exploratory," top right is "Strategic," bottom left is "Routine," and bottom right is "Consequential." Each quadrant includes descriptions of decision types, approach, and ownership. A key at the bottom explains decision components and participants, with colors indicating human or AI ownership. The footer cites the source and publication.

ALT This image is a diagram titled "The AI Decision Matrix: A framework for determining how humans and autonomous AI agents should participate in decision-making based on two dimensions: ambiguity and risk," from MIT Sloan School of Management. It features a 2x2 grid with "Ambiguity" on the y-axis (ranging from low to high) and "Risk" on the x-axis (ranging from low to high). The quadrants are labeled as follows: top left is "Exploratory," top right is "Strategic," bottom left is "Routine," and bottom right is "Consequential." Each quadrant includes descriptions of decision types, approach, and ownership. A key at the bottom explains decision components and participants, with colors indicating human or AI ownership. The footer cites the source and publication.

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Researchers from the MIT Center for Information Systems Research explain why AI has made robust data monetization capabilities not just a competitive advantage but a precondition for success. mitsloan.mit.edu/ideas-made-…
3 principles to bring your data monetization initiatives to life | MIT Sloan

3 principles to bring your data monetization initiatives to life | MIT Sloan

In today’s AI-driven environment, robust data monetization capabilities are both a competitive advantage and a precondition for success, research from the MIT Sloan School of Management finds.

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Researchers found that front-line workers were more receptive to AI when reassurance came from union reps who had personal relationships with them. mitsloan.mit.edu/ideas-made-…
What employers can learn from Kaiser Permanente’s AI labor deal | MIT Sloan

What employers can learn from Kaiser Permanente’s AI labor deal | MIT Sloan

An MIT report details how Kaiser Permanente and the Alliance of Health Care Unions reached a landmark labor agreement to incorporate worker input into AI decision-making.

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Negotiation was once considered the unique domain of humans, but that’s no longer the case. Today, you may encounter AI at your side or across the table, or serving as a mediator in every step of the negotiation process. mitsloan.mit.edu/ideas-made-…
4 seats AI can occupy at the negotiating table | MIT Sloan

4 seats AI can occupy at the negotiating table | MIT Sloan

AI can negotiate for you, coach you, support your team, or mediate between parties. An MIT Sloan School of Management professor explains those four roles — and why human judgment still matters.

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Data regulation does not necessarily cause lasting economic harm. Online publishers adapted to Europe’s GDPR privacy rules and kept producing quality content. mitsloan.mit.edu/ideas-made-…
Data or privacy? Businesses can have both, MIT Sloan research finds | MIT Sloan

Data or privacy? Businesses can have both, MIT Sloan research finds | MIT Sloan

Data regulation doesn’t necessarily cause economic harm. Markets — and consumers — can still derive economic benefits from data collection when it’s tempered by reasonable privacy guardrails.

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