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Artificial intelligence: A revolution underway in finance and trading

Auto trading (or algorithmic trading) has become so successful that it now accounts for a significant portion of trading in financial markets. As professor Andrew W. Lo explains, "Trading algorithms exploit opportunities that even the most experienced human traders cannot detect in time." However, while auto-trading offers unparalleled execution speed, it also requires constant vigilance.

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MIT Initiative on the Digital Economy
4 new studies about agentic AI from the MIT Initiative on the Digital Economy
New research ranges from how AI agents negotiate to how “personality pairing” can optimize human-AI collaboration.
Special Report
MIT Sloan insights for success in AI-driven organizations
Experts share innovative ideas for using artificial intelligence to solve critical business problems and deliver on strategy.
Teaching & Learning Resources
Generative AI for Teaching & Learning
Explore our resources to unlock AI's potential for enhancing teaching and learning at MIT Sloan.

Centers, Initiatives, and Programs

MIT Center for Collective Intelligence
Center for Information Systems Research
Computer Science and AI Lab
Master of Business Analytics
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AI at MIT Sloan

Machine learning and generative AI: What are they good for in 2025?
While generative AI is widely accessible and useful, businesses need to know when to use other AI tools, like traditional machine learning.


How to find the right business use cases for generative AI
Three steps to finding use cases for large language models: Break down workflows into tasks, consider all costs associated with automation, and launch pilots.

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These human capabilities complement AI’s shortcomings
The work tasks that AI is least likely to replace are those that depend on uniquely human capacities, such as empathy, judgment, ethics, and hope.
How to spot real value in AI — and avoid the snake oil
A new book aims to help business leaders separate real AI value from overhyped claims.
How organizations build a culture of AI ethics
From risk management policies to the five stages of AI ethics, here’s how some organizations approach ethical AI.
Bringing transparency to the data used to train artificial intelligence
Using the wrong datasets to train artificial intelligence models can result in legal risks, bias, or lower-quality models. The Data Provenance Initiative’s tool can help.
Media: Bloomberg
MIT Harnesses AI to Accelerate Startup Ambitions
Paul Cheek, executive director of the Martin Trust Center for MIT Entrepreneurship said: "Our mission at the Trust Center is to advance the field of innovation-driven entrepreneurship everywhere. We can't do it with intuition or by throwing stuff against the wall. We have to practice entrepreneurship in a rigorous, systematic way that increases the odds of success."
Managers and workers need to collectively develop new expectations and work practices to ensure that any work done in collaboration with generative AI meets the values, goals, and standards of their key stakeholders.
Kate Kellogg

David J. McGrath jr (1959) Professor of Management and Innovation
I've gained deep insights into cutting-edge technologies and had the chance to connect and network with industry leaders ... [T]aking courses or working on projects related to deep learning has been a rewarding experience.
Anne Castille Buisson

MBAn ’24
This technology’s deepest impact on the world of work will come as it’s used to reimagine entire organizations. This deep reimagination will be a decentralized and distributed phenomenon, carried out by innovators and entrepreneurs.
Andrew McAfee

Co-Director, MIT IDE & Principal Research Scientist, MIT Sloan
You need to be talking about AI along with specific initiatives and the outcomes you want to achieve using it. And those outcomes need to be compelling for your organization.
Barbara Wixom

Principal research scientist at the MIT Center for Information Systems Research
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