AI and machine learning are still not well understood by most C-level executives. So the advice for creating and driving AI initiatives in an organization is more about business results than explaining the technology.

A gathering of various corporate AI experts at VB Summit 2018 shared insights and perspectives about what helped move their AI and machine learning efforts. Some of the approaches include showing C-level execs results of AI projects that have to be business-led, which means building the project, rolling it out, and having a return on investment; branding your AI projects with an anthropomorphic name, which helps create a common conversational tool; and finally realizing patience is a big part of what needs to be communicated to C-level execs.

“Trying to explain AI and machine learning to the C-suite is a waste of time,” said Pavan Arora, Chief AI Officer at Aramark. “Instead, show them what you can do. In doing so, you need to figure out your metric for this work. For example, if you’re looking at labor and optimization, the metric is about reducing over time and data optimization.”

Other common challenges faced by businesses centers around creating transparency to get everyone on board and then setting the right expectations to manage the actual outcome. That often involves how you go about getting enough budget to create the intended impact or success.

In terms of setting appropriate expectations and driving results, Jay Allardyce, head of corporate development and partnerships at Uptake, says it’s important to determine what the context is for the project or initiative. And what follows with that context is determining “the financial outcome you want and care about.” Because driving bottom line cost savings and revenue is critical in businesses trying to gain buy-in and budget from executives.

In all types of corporate transformational efforts driven with AI and machine learning, those bringing these new technologies to the business have to realize the importance of allotting time for hand-holding between data scientists and the people in the businesses where the AI and ML is supposed to help.

“Create wins, but you need to create the right wins and strike a balance,” said Peter Chiang, VP of global digital strategy and transformation, MetLife. “It is a fine line and it’s a struggle.” You can’t aim too high for a project and you can’t aim too low where the outcomes are small and just pragmatic.