On a recent Sunday afternoon, I felt a severe pain in my forearm. At first, I thought it was a muscle-related pain, but my index finger was also losing sensation.
Thoughts of a possible blood clot crossed my mind, so I called the hospital’s advice line. My call was promptly answered by an automated system asking me a question. Before I could answer, the system said, “I did not get that” and proceeded to lead me into what seemed like an infinite loop of questions.
That’s just one example of the type of experience users have with today’s state-of-the-art voice applications. It wasn’t a very pleasant experience, especially for someone who was in pain!
The good news is that we are in the midst of some of the greatest technological advances in artificial intelligence (AI) and its derivative technologies, including machine learning (ML) and natural language processing (NLP).
These technologies amplify our ability to perform at a higher scale. They solve a broad spectrum of problems and create efficiencies by offering new ways to automate tasks — whether it’s dictating a text message to your phone, looking up instructions on how to cook chili or searching for photos of your dog.
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AI Gets Better Every Day
Research and new developments with NLP continue to explore the finer details of interactions between humans and systems. Human speech is complex, and researchers are trying to improve natural language processing systems to the point where they can understand the intended meaning of language. They are adding natural language understanding (NLU) capabilities to NLP by teaching computer systems the nuances of mispronunciations, colloquialisms, voice quality and the like.
If efforts such as those are successful, calls like the one I made to the hospital could go more smoothly. When I made that call, I was using my cellphone while driving. The background noise, my accent and, perhaps, the poor quality of my phone mic all may have contributed to the system not understanding me.
New research on natural user interfaces that use both voice and gestures could also lead to advances that improve voice interactions. Motion sensors can detect the proximity and direction of a voice. Eventually, NLP technology will be able to gauge the caller’s emotional state based on the caller’s voice inflection or intonation. In my case, a system with that capability could have perceived the pain I was in, or the frustration I felt as I went through the infinite loop of questions.
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Adding Empathy to AI
But technology by itself will not fix all of the problems I had with the hospital’s advice line. In today’s experience economy, it is imperative that we design products with empathy for the end consumer.
The hospital advice line provided an unsatisfactory experience in part because it is an example of today’s primitive workflow-centric system design. Most of today’s call center system designs are based on questions and answers preprogrammed on a rule-based paradigm. That model does not have any empathy for the person consuming the service. Every new question asked increases the end user’s anxiety; the goal should be to ask a minimum amount of questions.
Innovations often start with a cool technology in search for a purpose. Engineers typically build a solution based on a use case, and customers are rarely involved in the process. Only after the developers have a working prototype do they engage designers to build an interface. That needs to change.
Customer empathy should be the driving force of a company. Innovative companies start by empathizing with a customer who is trying to solve a real-world problem and then design solutions that use the best available technology — or they create new technologies to fix the problem. The innovation cycle should be simple: Start with customer empathy, validate the business case, and then have engineers and designers work together from the very start of the discovery phase to design useful and meaningful experiences to solve the customer’s problem.
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New Mindsets for Engineers and Designers
Engineers need a design mindset and designers need a technology mindset.
Engineers need to develop customer empathy and put themselves in the customer’s shoes when they are developing solutions. If they lead with empathy, they will build something that fits a need. Microsoft CEO Satya Nadella captured that sentiment perfectly in his book “Hit Refresh” when he wrote, “Ideas excite me. Empathy grounds and centers me.”
Customer empathy is important, and designers are at the heart of that. That said, designers also need to learn about technology. They do not need to be technology experts, but they need to be familiar with the latest and greatest in technology and work closely with engineers to ensure that their designs are forward-thinking. When designing applications, they need to think beyond the solution design and figure out how to improve experiences everywhere it makes sense.
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A Magical Experience for Enterprises
When people use devices to connect with companies, they should not feel like they are interacting with a device. Rather, their devices should feel like extensions of themselves. (Think about this: When we forget our phones, we get the feeling that we have lost a part of our body.) Experience matters, especially when designing voice interactions.
When it comes to adoption of applications with AI functionality, there is a big gap between the consumer and enterprise worlds. In the consumer world, people are using voice assistants like Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Google’s Assistant to do all kinds of things. In the enterprise world, there aren’t many business applications that use NLP; I mainly see it used in call centers.
Experience is not one thing. Many aspects come together to make an experience. That is how Marvin Minsky, the father of AI, defined artificial intelligence in his book “The Society of Mind.” Enterprise experiences are made up of the various business applications in a heterogenous environment. A truly magical experience in an enterprise happens when an intelligent suite of applications works together in harmony and continuously improves by drawing insights from real-time feedback.
Experiences are the next step in the progression of economic value. Good and bad experiences can make or break a business, and enterprises that close that gap in delivering magical interactions will leapfrog their competitors.
In my case, I was lucky. Although my experience with the voice automated system was lousy and the following process was tedious, my final diagnosis was a pinched nerve. My overall experience would have been so much better if the voice system understood my intent and the pain I was going through and connected me to a doctor immediately. But what if someone with a serious ailment had called?