Expectations are a funny thing. In this space, we talk a lot about customer experiences because expectations often play a big role in that area.
Some industries are known for atrocious customer service. These negative experiences have created an expectation of poor service from providers in that industry. These views can be so entrenched that when a customer has a positive experience with one of these companies it’s treated as an anomaly.
Or, think about a TV show like CSI. There are plenty of anecdotes from lawyers that the type of crime lab work seen on that show influences the way some jurors evaluate evidence in real-life court cases. These people have an expectation of ‘CSI-like’ forensic evidence. That’s an unrealistic expectation.
The point here is that expectations influence how we perceive things.
Now, artificial intelligence (AI) is a technology group that is ripe for having unrealistic expectations. One of the reasons for this is similar to the CSI example. We’ve seen dozens of depictions of AI in popular culture so we have an idea of what could be possible.
However, that doesn’t mean that type of AI technology is right around the corner. A recent Gartner Report on AI reinforced this point, noting that “AI for now is limited. There is no ‘artificial general intelligence’ that can ‘think and act like a human,’ and there’s no reason to expect it soon.”*
Therefore, Gartner also advises “as an IT leader, you’ll have to balance AI’s practical utility today in the near term with the outsized expectations for its future.”*
Getting the Most Out of AI Today
Just because AI today isn’t approaching the level of SkyNet, HAL9000, or Ava from Ex Machina, doesn’t mean that IT leaders should overlook its usefulness either. Current AI technology works best when applied to a specific problem.
Gartner research suggests that “AI potential lies in intelligent automation of key processes — such as improving service and personalization with virtual assistants and chatbots and giving intelligent advice and recommendations — in areas that were formerly the domain of human experts.”*
Narrowing the scope of the problem AI needs to solve helps to manage expectations for AI. This also makes it easier to get started with AI, because businesses don’t need to sink a mountain of cash into an all-in AI solution. Starting small lets companies get their sea legs when working with AI and to establish a solid foundation before expanding project scope.
The benefit of this approach, Gartner points out, is that “as organizations grapple with these early steps to create the AI foundation, they create experience and competence that will accelerate AI adoption in the future.”*
Ending Customer Frustration with AI
Here at Plum Voice, we offer AI-powered voice applications that reduce customer frustration with your voice channel’s self-service offerings. Our technology uses natural language processing to determine caller intent and get them to the right solution faster than current IVR technology.
To learn more about our AI offering, request a demo today.
*Gartner, Applying Artificial Intelligence to Drive Business Transformation: A Gartner Trend Insight Report, 29 August 2017