Navigating the Realities of AI Implementation for Business Success

The hype around AI is causing confusion and unrealistic expectations for machine learning (ML) projects, which are designed to improve business operations through predictive analytics. The term "AI" is often used to describe practical ML initiatives, but it alludes to human-level capabilities and oversells what most ML business deployments actually do. Defining "AI" as something other than artificial general intelligence (AGI) has become a research challenge, and the lack of a clear definition contributes to the high failure rate of ML projects. To properly insulate ML as an industry from the next AI Winter, we need to differentiate ML from AI and resist the temptation to ride hype waves.