How to Prove AI Value to Skeptics

Make a prediction that comes true.

AI has not taken its seat at the decision making table for many organizations that would reap big benefits from it. That’s easy to understand, since the terminology—Artificial Intelligence, machine learning, robotic assistants, and the like—are conflated in stories and ads to the point of being meaningless. Nearly every system claims to be AI-driven, without explaining what that means.

So what do you do when you are an executive who understands the concept and the promise of AI, but can’t get past the barriers? Building a data science team is an expensive leap of faith before you quantify potential gains. Automated machine learning is the best way to show AI’s value swiftly—by making accurate, actionable predictions that deliver better results than your current processes.

Pick a practical problem. Think of areas where classification or regression predictions could add revenue or save cost.

  • Will this happen or not?
  • Which of several things will happen?
  • How much will happen?

Making predictions with AutoML is as simple as pointing to training data, selecting which values you want to predict, and setting it to work on your data. What returns is record-by-record likelihood of future outcomes. Those are easy to monetize through programs that amplify high-probability actions and avoid low-probability ones. The results can be startling. One of Squark’s customers applied AutoML to a marketing cross-sell problem and had this to say:

“Double-digit uplift in sales overnight, with no coding. Remarkable.”

The takeaway: AutoML proves the value of AI quickly. Pick a prediction use case and demonstrate how a view into the future transforms decision making with instant ROI.