Succeeding in AI Adoption

Learn about the challenges of AI adoption—tangible and intangible obstacles—and how to overcome them by setting realistic expectations and establishing best practices.

By now, we’ve seen how involved applied ML is for an organization to embrace fully, and we’ve just spent most of this lesson looking through the various growth areas in AI for organizations that are already in business and are looking to capitalize on these growth areas. We’re discussing the transition of incorporating AI into a traditional software product.

This means we can now move on to set the stage for what this adoption looks like, but before we get started, we want to include a caveat. We really can’t talk about AI transformation unless we also set ourselves up for success to be able to begin the long and arduous process that is AI adoption. We have to make sure the conditions are right. Whether we’re working in an organization that’s ready to build an AI native product or we’re in a traditional software environment that’s ready to adopt AI at the feature level, there’s a level of expectation setting that’s required for the endeavor to be successful. But setting expectations properly in an already established software company that isn’t used to the demands AI will place on an organization is especially difficult.

Navigating AI challenges

Get hands-on with 1200+ tech skills courses.