Dell Technologies Realize magazine Issue 2 | Page 32

THE INTERVIEW 30 Where do you see these opportunities for machines to step in? The first is the user experience. Some simple examples are voice assistants and digital assistants that already make customer care more productive and our homes more efficient. Those experiences are not possible with an army of human beings behind them. You need machine intelligence. The second is business process improvement. Every business process we have today—financial, industrial, manufacturing, healthcare—involves thinking tasks, and most of them do not have enough human beings with the expertise to do those thinking tasks. Applying machine intelligence to take on just some of the thinking, such as with radiology, achieves a more effective operational outcome, without running out of humans to do the work. Then the third is the creation of entirely new industries. The autonomous vehicle industry is the best example. Candidly, if you want cars to drive themselves, you can’t do it by adding more people. All of these outcomes have different degrees of complexity, but none of them are possible without shifting most of the task— maybe all of the task—to a machine environment that operates with speed and efficiency. The upside is enormous. How do you address concerns about job disruption? All technology, all industrialization changes disrupt jobs, full stop. AI will disrupt jobs. There are jobs that will go away, and there will be jobs created. But there’s also a third category, and that’s improvement of the human condition. What happens when machine intelligence achieves its outcome, like making cars autonomous? Or making healthcare more intuitive, or making customer service easier? What happens to the businesses that use them? It’s very likely those businesses will grow because some impediment to their growth suddenly disappears, which allows them to reach more customers and provide a better service. It’s much more complex than just a one-dimensional consideration of jobs. New technology will always equal some job disruption—hopefully more job creation—plus a change in the human condition that is a net positive and makes our existence happier and better. What is the biggest misconception leaders have about their data? Many leaders are wrestling with the idea that you have to understand your data—what to collect and what to keep—before you can develop a data strategy. That’s not true; flip it around. You have to pivot to the idea that it’s because you don’t understand all of your data that you should gather it and use tools like AI and machine learning to figure out what it’s telling you. If you don’t pivot, two things will happen. One, your data strategy will probably never happen because you’ll never really understand all your data. Or two, you’ll throw away a bunch of data that could’ve been really valuable in getting better insight. The other misconception people have is that the data era equals big data. Big data allowed us to use some new, but rudimentary tools to mine lots of data to try to see patterns, then present