Making a compelling use case for adopting AI is occupying a great deal of time and brain space for CTOs and CIOs. The pace, scale and speed of the cloud empower users but create its own struggle as technology is moving faster than organizations can adapt their practices. This is where AI comes into its own; development is faster and easier, and AI helps it get there and stay there.

Knowing where to deploy people and which tools and platforms to implement to help them keep up with the pace of cloud transformations can feel like a blur of options with no solid foundation to enable a decision.

Having worked in QA testing with AI at our core since 2017, we’ve observed every permutation of AI integration, and we know what works—and what may result in an expensive mistake. Contrary to popular belief, AI did not suddenly spring into being in the last two years. Machine learning and predictive analytics have evolved over many years, and we were there at the outset.

If you need a starting point, here are some tips and the questions you should be asking your tech partners:

Read the rest of Hugo Farinha's piece on tips for implementing AI on your platform on Forbes.