@MITSloan

Insights for your work from MIT experts

 Cambridge, MA
mitsloan.mit.edu
Joined September 2007
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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. mitsloan.mit.edu/ideas-made-…

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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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 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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In today’s AI-driven environment, robust data monetization is a precondition for success. Here are three principles that help determine whether data monetization efforts will drive financial performance. mitsloan.mit.edu/ideas-made-…

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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Kaiser Permanente combined top-down governance with bottom-up idea generation: A small, senior task force set enterprise-wide AI strategy, while thousands of front-line unit-based teams identified problems AI could actually solve. mitsloan.mit.edu/ideas-made-…

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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AI can greatly expand access to negotiation practice and feedback, but its most promising uses complement rather than replace human judgment. mitsloan.mit.edu/ideas-made-…

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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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," from MIT Sloan School of Management. It is a 2x2 grid illustrating a framework for decision-making based on ambiguity and risk. The grid categories are: 1. Exploratory - High ambiguity, high risk, with an approach to enable experimentation. 2. Strategic - High ambiguity, low risk, with an approach to partner. 3. Routine - Low ambiguity, low risk, with an approach to automate and monitor. 4. Consequential - Low ambiguity, high risk, with an approach to manage and supervise. The bottom section includes an explanation of decision components, participants, and approaches. The matrix helps determine how humans and AI should participate in decision-making.

ALT This image is a diagram titled "The AI Decision Matrix," from MIT Sloan School of Management. It is a 2x2 grid illustrating a framework for decision-making based on ambiguity and risk. The grid categories are: 1. Exploratory - High ambiguity, high risk, with an approach to enable experimentation. 2. Strategic - High ambiguity, low risk, with an approach to partner. 3. Routine - Low ambiguity, low risk, with an approach to automate and monitor. 4. Consequential - Low ambiguity, high risk, with an approach to manage and supervise. The bottom section includes an explanation of decision components, participants, and approaches. The matrix helps determine how humans and AI should participate in decision-making.

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“AI is changing not just what we can do; it’s changing who we are as thinkers. Our capacity to engage in sustained focus and struggle is being disrupted by AI. It’s not just a matter of our capabilities but our tolerance for friction.” — MIT Sloan professor Eric So mitsloan.mit.edu/ideas-made-…

‘The Collision: What AI Does to Us’ | MIT Sloan

In his new book, MIT Sloan School of Management professor Eric So examines the ways AI is reshaping human cognition and offers advice on protecting your uniquely human skills while using AI.

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A new certificate program is designed to help PhD students in science and engineering at MIT translate their research into practice in deep-tech fields such as biomedicine, nuclear energy, climate sciences, quantum computing, and space exploration. mitsloan.mit.edu/ideas-made-…

These new MIT Sloan courses track emerging technologies and business trends | MIT Sloan

MIT Sloan School of Management course offerings like AI-Augmented Work, Defense and Dual-Use Ventures, and Leading Agentic AI Transformation in Organizations anticipate businesses’ future needs.

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How many jobs will artificial intelligence eliminate? It’s a question that many people have been asking. But Google’s chief economist suggests that a more relevant question is how the technology will change the jobs that already exist. mitsloan.mit.edu/ideas-made-…

AI and jobs: 4 takeaways from Google’s chief economist | MIT Sloan

Google’s Fabien Curto Millet examined how AI is reshaping job roles, workflows, and employee training in an address at the MIT Stone Center on Inequality and Shaping the Future of Work.

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Modern enterprises face the challenge of determining what tasks artificial intelligence can safely perform without human oversight. The AI Decision Matrix can help business leaders assess ambiguity and risk. mitsloan.mit.edu/ideas-made-…

A framework for determining when AI can make decisions | MIT Sloan

The AI Decision Matrix uses ambiguity and risk to determine the optimal decision-making structure between humans and AI automation.

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The fear about AI and jobs is that disruption will arrive suddenly. But researchers from MIT FutureTech estimated that AI could reach success rates of 88% to 97% on most text-based workplace tasks by 2030 at a minimally sufficient quality level, though near-perfect performance remains several years away. That gives governments, organizations, and workers a window in which to plan for the prospect of AI automation at the task level. Learn more: bit.ly/4hC5Zcq
Numbers That Matter: 97%. Artificial intelligence could reach success rates of 88% to 97% on most text-based workplace tasks by 2030 at a minimally sufficient quality level, according to MIT FutureTech researchers.

ALT Numbers That Matter: 97%. Artificial intelligence could reach success rates of 88% to 97% on most text-based workplace tasks by 2030 at a minimally sufficient quality level, according to MIT FutureTech researchers.

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As artificial intelligence use becomes more widespread, scholars are working to understand its effects on the people who use it. MIT Sloan professor Eric So offers one of the most complete syntheses yet, in his new book, “The Collision: What AI Does to Us.” news.mit.edu/2026/collision-…

Q&A: The “collision” between AI, your brain, and society

Heavy use of artificial intelligence can lead to a kind of cognitive atrophy, in which people fail to develop and retain essential thinking skills, MIT Professor Eric So contends in his book, “The...

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MIT Sloan professor Eric So believes that people who use artificial intelligence fall into one of two groups: those who use the technology to think better, and those who use AI instead of thinking. Without intervention, the latter group could pay a heavy price. mitsloan.mit.edu/ideas-made-…

‘The Collision: What AI Does to Us’ | MIT Sloan

In his new book, MIT Sloan School of Management professor Eric So examines the ways AI is reshaping human cognition and offers advice on protecting your uniquely human skills while using AI.

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Artificial intelligence will transform more jobs than it eliminates outright. The biggest productivity gains will come not from incorporating AI into existing workflows but from redesigning those workflows entirely. mitsloan.mit.edu/ideas-made-…

AI and jobs: 4 takeaways from Google’s chief economist | MIT Sloan

Google’s Fabien Curto Millet examined how AI is reshaping job roles, workflows, and employee training in an address at the MIT Stone Center on Inequality and Shaping the Future of Work.

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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

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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How many jobs will artificial intelligence eliminate? It’s a question that many people have been asking. But @Google's chief economist suggests that a more relevant question is how the technology will change the jobs that already exist. @MITshapingwork mitsloan.mit.edu/ideas-made-…

AI and jobs: 4 takeaways from Google’s chief economist | MIT Sloan

Google’s Fabien Curto Millet examined how AI is reshaping job roles, workflows, and employee training in an address at the MIT Stone Center on Inequality and Shaping the Future of Work.

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MIT Sloan School of Management retweeted
Sinan Aral
@sinanaralOct 14
🚨Out now in PNAS! The first ever (we believe) large scale RCT + field experiment on the performance effects of AI Personalization! 🚩 In a preregistered randomized experiment, 🚩 We paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. 🚩 These human–AI teams produced 7,266 display ads, evaluated by 1,168 human raters, and a field experiment on X with ~5M impressions.
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Artificial intelligence is becoming easier for companies to access — but when competitors can also use many of the same foundation models and AI agents, simply adopting AI won’t necessarily set an organization apart. mitsloan.mit.edu/ideas-made-…

5 things to know about how companies can create value from AI | MIT Sloan

As AI tools become more widely available, competitive advantage will depend on the trust, data, expertise, operating capabilities, and work redesign companies build around them.

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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

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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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 an informational chart titled "The AI Decision Matrix" from MIT Sloan School of Management. It is a grid with axes labeled "Ambiguity" (vertical) and "Risk" (horizontal). The four quadrants are labeled: Exploratory (high ambiguity, low risk), Strategic (high ambiguity, high risk), Routine (low ambiguity, low risk), and Consequential (low ambiguity, high risk). Each quadrant describes an approach and decision type and includes recommended levels of human and AI involvement in decision-making. A legend at the bottom specifies decision components and participants, with icons for human and AI. Adaptation credit is given to authors and publication details at the bottom right.

ALT This image is an informational chart titled "The AI Decision Matrix" from MIT Sloan School of Management. It is a grid with axes labeled "Ambiguity" (vertical) and "Risk" (horizontal). The four quadrants are labeled: Exploratory (high ambiguity, low risk), Strategic (high ambiguity, high risk), Routine (low ambiguity, low risk), and Consequential (low ambiguity, high risk). Each quadrant describes an approach and decision type and includes recommended levels of human and AI involvement in decision-making. A legend at the bottom specifies decision components and participants, with icons for human and AI. Adaptation credit is given to authors and publication details at the bottom right.

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