How Voicebots Are Making Businesses More Efficient

Voice control of IT is the future. We’re seeing this everywhere in the consumer world: in mobile and home apps (Siri, Google, Cortana), connected cars, television remotes—you name it, voice is going there. This is because in the last few years voice recognition and cognitive learning have joined forces to make things even easier than they already were for humans to interact with machines. The trend is moving into business, and faster than you might think. This eWEEK slideshow, using industry information from Alois Reitbauer, chief technology strategist at Dynatrace, explains why digital voice assistants are growing along with our population.

According to the Consumer Technology Association, approximately 5 million voice-activated digital assistants have been sold to date, and that number is predicted to double this year. The number of people using virtual assistants in the United States is growing too—by 23.1 percent in 2017 alone. It didn’t take long for people to recognize the value of voice automation, and developers are working hard to incorporate the benefits of artificial intelligence (AI)-powered digital virtual assistants into a variety of consumer and business scenarios.

Among IT teams aiming to keep systems running smoothly, voicebots are quickly becoming a hot commodity, in large part due to the simple concept on which they are based: answering a question. When corporate IT teams use voicebots, it’s like speaking to fellow IT team members, but with faster results. In fact, it’s predicted that by 2018, 30 percent of a person’s interactions with technology will be through “conversations” with voice interfaces. This technology makes businesses more efficient because, realistically, a person could not come close to competing with a bot’s AI-powered engine in terms of speed and precision.

Virtual assistants play an important role in alleviating much of the complexity and time constraint issues placed on IT today. AI helps sort and deliver the answers—not just the data—to provide actionable insights to business stakeholders, operations staff and development teams. This is especially important given that 24 percent of businesses feel they’re understaffed in IT/development.

IT complexity has increased a thousand times over. Did you know that today’s enterprise applications can have about 16 billion runtime dependences, making it impossible for mere humans to identify and fix the root causes of digital performance in real time? The increased complexity also exceeds any human IT team’s ability to monitor performance around the clock using traditional systems. As a consequence, teams are wasting time in a constant war room state, jeopardizing customer interactions and leading to lost revenue. AI has proved to be the best and fastest way to detect any issues in apps, and interactions via voice interfaces make the process more natural and efficient.

Maintaining digital performance is critical to a business’s revenue and reputation. In fact, downtime could cost a business up to $5,600 per minute. However, 50 percent of IT and business professionals claim that digital performance challenges directly inhibit the success of their digital transformation strategies, according to a new global study.

Voicebot technology is built with the ability to infer a user’s intention from a spoken phrase. But during development it is important to avoid combining too many related phrases into one single intent. For example, when developing Davis, Dynatrace’s AI-powered voice assistant, the Dynatrace team originally mapped questions like “Are there any problems at the moment?” or “Have any problems affected travel this week?” to the same problem intent. The company quickly discovered that these questions were too broad, which just overloaded the bot’s logic. A better approach is to keep the scope of each intent as specific as possible.

During the development and installation process, voicebots can be programmed to recognize patterns or preferences so that they not only handle tasks, but also learn from every interaction. For example, when Davis reports on application performance, it can also identify the anomalies in performance that need immediate attention based on preset parameters. The key here is to think broadly and laterally about all of a voicebot’s potential use cases, and for developers not to limit themselves in the development process.