Technology Implementation

Two frameworks can help organizations identify potential harms posed by algorithms, AI tools, or large language models.

Prompting users to spot errors when using generative AI to complete reports improves the accuracy of the final product.

Learn why humans miss generative AI mistakes and how AI speed bumps help, in this short video.

Experts debate how effectively organizations are adjusting risk management practices to govern AI.

Business leaders can identify and avoid flawed AI models by employing statistical methods and statistics experts.

Learn three principles for reorganizing work around AI.

Watch this short video to learn more about how AI changes the rules of workforce management.

To succeed with machine learning, manage projects as business initiatives, not technology projects.

Two automation initiatives succeeded by scoring potential processes and combining technical and process knowledge.

Journalist and podcast producer Jennifer Strong shares insight into her experiences producing audio journalism.

Age-related cognitive changes can hinder workers’ technology use, but these strategies can help managers support them.

On the Me, Myself, and AI podcast, Tom Davenport discusses how organizations might use generative AI.

Taking a three-stage approach can increase the likelihood of a successful digital transformation in manufacturing companies.

Generative AI tools suit some decision-making situations, but not all. Here’s how to pick the best approach.

Three new surveys of data executives have identified five trends they’ll be paying attention to in 2024.

This webinar from MIT SMR offers guidance to help business leaders thoughtfully plan a generative AI implementation.

This webinar from MIT SMR explores key areas in which generative AI could transform existing ways of doing business.

This webinar from MIT SMR covers the basics of generative AI and explores business use cases for the emerging technology.

On the Me, Myself, and AI podcast, Chevron’s Ellen Nielsen explains how AI supports workers and operational efficiency.

Understanding large language models’ limitations can help users discern which tasks they are and are not well suited for.