To avoid redundant investments, applications that don’t scale, and unexpected costs and risks associated with AI, organizations should determine where oversight is likely to have the greatest impact.
Whether a task is worth automating depends not only on whether machines are cheaper than human labor but also on whether the task is performed at scale to spread fixed costs.
To avoid redundant investments, applications that don’t scale, and unexpected costs and risks associated with AI, organizations should determine where oversight is likely to have the greatest impact.
To gain maximum productivity from AI, MIT Sloan’s Robert Pozen advises senior leaders to use AI as a co-worker, focus AI on the final product, and delegate work to AI without abandoning judgment.
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.
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?
Artificial intelligence can boost the systematic innovation of products, processes, and services. MIT Sloan professor Steven Eppinger offers four steps for making that happen.
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.
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.
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.
