What is BYOAI?
A working definition from MIT Sloan
BYOAI (noun)
Employees’ use of unapproved, publicly accessible generative artificial intelligence tools for work-related tasks.
The rapid increase in generative AI use poses a variety of new challenges for organizational leaders, among them the advent of “bring your own AI.”
While the use of generative AI offers workplace value, companies need a plan for employees’ use of unapproved tools on the job, according to Barbara H. Wixom and Nick van der Meulen of the MIT Center for Information Systems Research.
BYOAI introduces risks that organizations are not yet equipped to manage, including data loss, intellectual property leaks, copyright violations, and security breaches.
The answer isn’t to ban these tools outright, van der Meulen said during a webinar hosted by MIT Sloan Management Review. “If we restrict access to these tools, employees won’t just stop using generative AI. They’ll start looking for workarounds — turning to personal devices and using unsanctioned accounts and hidden tools,” he said.
Instead, organizations should aim to turn BYOAI from a liability into an asset by developing clear guardrails and guidelines, training employees, and sanctioning specific generative AI tools.
Working Definitions: Artificial Intelligence
MIT Sloan's Working Definitions explore the words and phrases behind emerging management ideas.
Leading the AI-Driven Organization
In person at MIT Sloan
Register Now
Data liquidity leads to AI success
Three levers — data architecture, data preparation, and data permissions — determine whether data becomes a reusable strategic asset or stays trapped in silos.
Pro-worker AI, explained
Artificial intelligence can make workers more capable and productive, but only if leaders design and deploy it to augment human judgment.
5 things to consider when working with AI
Researchers at the MIT Initiative on the Digital Economy share the latest insights about getting the most from working with AI, such as personality pairing and reorganizing job tasks.
Balance AI innovation and risk with ‘minimum viable governance’
As organizations scale generative AI, traditional governance models prove to be too rigid or too loose. Minimum viable governance calibrates oversight to risk, enabling responsible innovation.
The surprising power of warmth in AI negotiations
In MIT’s international AI Negotiation Competition, “warmer” agents achieved better outcomes in negotiations with other AI agents.
Seeing real value from AI depends on being able to verify its outputs
A new paper explores how seeing economic value from artificial intelligence hinges on closing the gap between what AI can do and how humans can verify its outputs.
‘AI gravity’ is pulling you toward dependency. Here’s how to push back
AI systems hold the promise of competitive advantage, but they can usher in cognitive decline among workers, says MIT Sloan School of Management’s Eric So. Learn how to protect cognitive capital.
Heeding the pope’s call to ensure AI protects human dignity
Following Pope Leo XIV’s encyclical on AI, MIT Sloan professor emeritus Thomas A. Kochan argues that firms should partner with workers to ensure AI augments human skills and knowledge.
What senior leaders want to know about AI
Leaders are turning to MIT Sloan Executive Education to learn more about AI, including managing humans amid technological change and rethinking their relationships with IT departments.
What leaders still get wrong about AI
Organizations are struggling to succeed with AI. Research from the MIT Center for Information Systems Research shows common mistakes and how to overcome them.