When it’s Smart to Play Dumb: Managing AI Recommendations
May 12, 2017 | Craig Higdon
As machine learning and artificial intelligence evolve and begin to show interesting results, our clients are exploring how to apply the technology to their products and services. Their goal is simple: improve the customer experience while decreasing customer service costs.
But what does this even look like? With enough information about you, the customer, AI can lead to accurate recommendations. Or it can go a step further and take action on that information and clue you in later. So when should an AI-powered digital product check in before it does something, and when should it take matters into its own hands?
With Big Data Comes Big Opportunities
Our clients know more about their customers than ever before. With the rise of AI, it’s natural that they want to take advantage of this information to create more effective services for their customers. Digital experiences are ideal for delivering personalized services through dynamically adjusting content and personal information based on customer needs.
If a company knows what you want, should they provide you only with the options you care about and nothing else? Going a step further—if the company is so certain, why not simply act on your behalf, rather than provide options?
An AI-powered digital experience could have the data necessary—with an incredible level of confidence—to know what you’ll actually prefer. So why not be where your customer wants you to be and provide an experience that seems magical?
While working on an appointment scheduling feature for a client, we took a step back to think about this question, considering:
Based on this thinking, we developed two approaches: “Ask, Then Act” and “Act, Then Explain.”
“Ask, Then Act”
When the back end of the experience compares a customer’s situation against the data it has available, it will generate numerous options—and then ask the user which option they prefer. This is the default mode for many experiences. You see it in the “People who bought this also bought” on Amazon and the suggestions to add other people to an email in Gmail. It’s so innocuous at this point that we hardly notice it, but the experience is trying to make your decisions easier, unobtrusively.
Every time you act on the suggestion, the AI says “Aha! We were right. Make a note to improve future suggestions!” In the experience we were designing, the “Ask, Then Act” approach had us making suggestions for a new appointment date, time, or location based on previous appointments the user had created.
One potential problem with the “Ask, Then Act” approach is that AI suggestions can trigger “alert fatigue”—such as when your smartphone tells you how long it will take you to get home when you’ve just gotten to work, or when you dismissed a dozen notifications irrelevant to the work at hand.
This noise and clutter take effort and time to dismiss. Even with the best intentions, there can be an exhaustive level of interaction needed to simply get back to whatever you were doing before you were so rudely interrupted.
“Ask, Then Act” Pros and Cons
“Act, Then Explain”
When the data in a scenario points to a clear solution, the experience simply acts. For example, when you use Google Maps, you’re given the best route, with explanations of why other routes are not optimal. Lyft assigns you a driver based on your location and your previous ratings. In our client project, our experience could have scheduled the appointment for the user based on a number of factors, then explained what it did and why.
When “Act, Then Explain” works, it’s great. But what if it goes wrong? We’ve all experienced a route from Google Maps that might be technically shorter, but is overly complicated and feels longer. Or when Lyft’s driver-matching algorithm assigns you a curt driver based on your previous activity and you find yourself wondering, “What did I do to deserve this?” The whole thing starts to feel arbitrary and ill-informed. Before you know it, you’re alienated from an experience that was great only yesterday.
“Act, Then Explain” Pros and Cons
How to Choose: Value vs. Consequences
For our client project, we were challenged with figuring out which approach might work best for a given situation: “Do you want me to schedule your appointment? What day? What time?” or “Here’s your follow-up appointment. Click here to make changes.”
Which approach is better? When should a product or service check in before taking action, and when should it just act outright? As you may have guessed, it depends. The key is understanding value versus consequences.
If there’s not much at stake, but a lot of value for streamlining the experience, we say “Let ‘er rip!” Act first and explain later. This provides a streamlined and satisfying experience for users. They enjoy the added bonus of getting to their goals quicker because the experience just made a series of decisions, intelligently, on their behalf.
But if the consequences of an incorrect action are higher, it’s best to ask first before taking action. If the outcome could be something like poorly invested money or damage to personal or professional relationships, then there’s not much value in streamlining the experience. Better to play it safe and ask a couple of questions before getting to work.