Summary.
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity.
Sadly, beneath the aspirational headlines and tantalizing potential lies a sobering reality: Most AI projects fail.
Some estimates place the failure rate as high as 80%—almost double the rate of corporate IT project failures from a decade ago. Approaches exist, however, to increase the odds of success.
Sadly, beneath the aspirational headlines and tantalizing potential lies a sobering reality: Most AI projects fail.
Some estimates place the failure rate as high as 80%—almost double the rate of corporate IT project failures from a decade ago. Approaches exist, however, to increase the odds of success.
Companies can greatly reduce their risk of failure by carefully navigating five critical steps that every AI project traverses on its way to becoming a product: selection, development, evaluation, adoption, and management.
When I worked as a data scientist at LinkedIn in 2018 and 2019, AI was of interest only to a small team of people in the data science organization with advanced degrees in statistics or computer science. AI—and especially its newest star, generative AI—is now a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. The topic is so fundamental that “Data Science for Managers,” a course I helped create to teach MBA students how to develop, leverage, and manage AI, is now a first-year requirement at Harvard Business School.