Gaining Real Business Benefits From GenAI: An MIT SMR Executive Guide

There’s no question that deciding how to reap real value from generative AI is among the biggest challenges facing business and technology leaders today.

Our new Executive Guide series, “How to Maximize the Business Value of Generative AI,” provides expert insights designed to help those leaders identify the best approaches to using generative AI, establish effective governance, and accurately measure the results.

The series, which begins on Jan. 13, also includes examples from organizations in many industries that are already seeing significant business value from their AI investments, along with practical takeaways and steps for turning insights into action.

Sign up to be notified when new Executive Guide articles are published. Meanwhile, the following summaries provide a preview of what’s coming and when.

Turbocharging Organizational Learning With GenAI

Paul Baier and John J. Sviokla

Generative AI can radically enhance organizational learning by using human language to facilitate interaction and deftly handling unstructured words, images, numbers, and sounds. The authors describe how managers and employees can work in tandem with GenAI to develop and share expertise. Available Jan. 13.

What Leaders Should Know About Measuring AI Project Value

Eric Siegel

Most AI/machine learning projects report only on technical metrics that don’t tell leaders how much business value could be delivered. To prevent project failures, press for business metrics instead. Available Jan. 15

Reinventing the Organization for GenAI and LLMs

Ethan Mollick

Previous waves of technology have ushered in innovations that have strengthened traditional organizational structures. That has not been the case for generative AI and large language models. Learn three key principles for reorganizing work around AI. Available Jan. 21.

Generate Value From Gen AI With ‘Small t’ Transformations

Melissa Webster and George Westerman

GenAI may not yet be capable of large-scale transformation, but companies are getting real business value today by targeting three categories of tasks that involve varying degrees of risk. Research conducted with 21 large companies shows where and how to generate this value while building for the future. Available Jan. 22.

Mayo Clinic’s Healthy Model for AI Success

Thomas H. Davenport and Randy Bean

This case study offers a close look at how one organization’s approach to data and infrastructure is helping its people build AI applications efficiently and safely. A key to its success: Staff members see the data and AI team as an enabler rather than a gatekeeper. Available Jan. 27.

Do You Really Need a Chief AI Officer?

Michael Wade, Anja Lagodny, Ann-Christin Andersen, Corinne Avelines, and Achim Plueckebaum

The CAIO role, which is intended to drive a cohesive approach to implementing AI across an organization, comes with compelling arguments both for and against its creation. The right choice depends on the strategic importance and maturity of AI in your company. Available Jan. 29.