THE INTERVIEW
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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