With a career journey spanning digital native companies such as Yahoo and eBay as well as big established brands like American Express, Express Scripts, and\u00a0most recently\u00a0Northwestern Mutual,\u00a0Neal\u00a0Sample\u2019s unique combination of strengths and interests stands out. He received a PhD in Computer Science from Stanford, so he\u2019s comfortable diving into the deep end as a technologist. But he\u2019s just as tuned in to the human side of the IT equation. In fact, he says one of his greatest accomplishments in his current role is the work he\u2019s done to foster a rewarding and inclusive workplace.\n\nWhen we sat down for the Tech Whisperers podcast, Sample opened up about his new playbook, his leadership philosophies, and how he is winning with talent. Afterwards, we spent some more time discussing his views on the technology landscape and how his career and academic experiences have informed his approach to leadership. What follows is that conversation, lightly edited and condensed for clarity.\n\nDan Roberts: With so much coming at you, how do you stay abreast of current trends and technologies, and how do you translate that into business impact?\n\nNeal Sample: I do a little bit of self-study. I\u2019ll read blogs and follow along with industry trends. I like a conference that\u2019s got a diverse purview\u2014that\u2019s not just, for example, focused on single technology. And what I really like to do is listen to my experts. Sometimes those are the folks that work for me, running security or application development or infrastructure. They\u2019re ultimately going to be deeper in their specialty than I am, and I find that it\u2019s a great way to learn. Occasionally you bring in an outside expert, especially when you\u2019re going to do something new. You can learn from the path that others have trailblazed for you.\n\nOverall, curiosity is incredibly important. If you\u2019re not curious\u2014if you think you know all the answers and you don\u2019t have any questions\u2014then you\u2019re not going to learn and grow. Maybe you do have all the answers. But I find that there are more things in this world that I don\u2019t know than I do. And some of the things that I knew ten or 20 years ago aren\u2019t true anymore today, and I think that\u2019s something that\u2019s kind of unique to technology.\n\nBut even if the business doesn\u2019t change around us, the tools that we use change, and you have to stay current on those tools if you\u2019re going to be effective in your seat. As a CIO, if you\u2019re staying in place, you\u2019re falling behind.\n\nYou\u2019ve learned a lot from working with a diverse set of companies and industries. What would you say is the one call most people would change when it comes to their architecture?\n\nI think if we could go far enough back, we would change just about every decision. All architecture is wrong, because everything we\u2019ve done has changed and grown over time. I think back to the first big architecture I worked on, and boy, you would not do that today. And then you look at some of the things we\u2019re doing now where even the notion of having a server is a little bit archaic. So I\u2019ll be bold enough to say it: All of our architectures are wrong. We just don\u2019t know it yet.\n\nKnowing that\u2019s the case, what can CIOs do to defend against this?\n\nThere are a lot of principles out there. One I like is the open-closed principle: open to extension but closed for modification. If you\u2019re closed to modification, that means you won\u2019t have breaking changes that will impact people in a negative way as you vary the implementation underneath. But open to extension means they can build on it, they can incorporate it, they can actually add to it. And that\u2019s really important.\n\nThere\u2019s one term that I\u2019ve coined, and that\u2019s the notion of \u201cChernobylizing,\u201d which is when you take something that\u2019s legacy and you encase it in concrete. You leave it for ten thousand years until it becomes a little less radioactive. And that\u2019s sort of a forced version of the open-closed principle. You\u2019re shutting down development in this area that is no longer strategic. And maybe time has passed you by, but you\u2019re still able to use it on top.\n\nWhat do you mean when you talk about \u201cforce of data vs. the cult of personality\u201d?\n\nEspecially sitting in the \u201cbig chair\u201d like I do, you speak with a loud voice, one that sometimes is unintentionally loud. The number of times I\u2019ve heard somebody say \u201cNeal wants this\u201d after a request has come back to me, and I had no idea I wanted that, but somebody could point at something in a conversation off to the side at one point.\n\nI find that organizations that are run that way perform very differently than organizations that deliberately focus on the data. Having a data science background makes it easy for me to focus on the data, but it\u2019s something I ask my teams to do as well, to interrogate the data, to learn about the data, to figure out what is the data telling you to make a decision. Or if you don\u2019t think you have the data, what do you expect is missing, or what is a consequence that you don\u2019t see if an alternative hypothesis was true. The idea is that you step back, you set your experiences aside, and you go where the data takes you. I think it\u2019s a really powerful way to operate an organization.\n\nDoes it surprise your people when you can go deep on technology?\n\nI will say that some CIOs come from different places, especially at legacy companies where they were more of a cost center, maybe lived in finance, for example. They tend to be more program administrators or budget focused. Portfolio managers are amazing, but they\u2019re less likely to go deep on something, like a query planner for a database. So I\u2019ve been fortunate enough to have the best of both of those experiences. I grew up at early tech companies where a lot of the tools that exist in the world today, we had to build. So you had to be close to the work. And then I had an academic career for a long time. I taught advanced database design at Stanford. And I didn\u2019t forget all of it! And sometimes that\u2019s a surprising fact for people in the organization.\n\nYou were a college debater and US national champion. What are some not-so-obvious skills you learned that you apply to your role as a C-suite executive?\n\nOne thing I learned is to develop a hypothesis and test that hypothesis incredibly quickly. In parliamentary debate, you would get the topic 15 minutes before you had to debate it, and in that time, you had to develop your case or counter positions, then be ready to argue your positions in front of a critic. You learn how to very quickly spot issues, figure out what\u2019s important and what\u2019s not, and start to filter and address those issues.\n\nThe second thing is, and I think this is really important, every round you\u2019d flip between affirmative and negative, for a topic or against a topic. You would always take both sides. That teaches you to let those positions go and be sort of egoless about it. If you\u2019re wrong, you\u2019re wrong and you move on. I find that a lot of folks come up with an idea and instead of getting invested in testing the idea, they get invested in the idea. That constant flip-flop of taking different sides and testing ideas meant you couldn\u2019t afford to become too invested in them.\n\nWhat technology holds the most promise for delivering game-changing results for your business in the next 12-24 months?\n\nFor us and certainly for a lot of other companies, that\u2019s machine learning and artificial intelligence. When we look at our back-office processes, we know we\u2019ve got the best mortality outcomes and the best persistency in the world. A lot of that comes from our ability to operate algorithms and leverage data that we\u2019ve developed over the last 165 years, but most of that learning has happened at the speed of humans. So when I think about what\u2019s going to change the game for us, it\u2019s the ability to process way more data than you could possibly have imagined one, ten or a hundred years ago, in our case, and that the machines can come up with and test hypotheses a million times faster than people possibly could.\n\nThere are companies that don\u2019t even think that this is an alternative or hypothesis. They\u2019ve started with machine learning, with the cloud, with big data, and they just think this is the natural way to do it. But I think it\u2019s going to be a real transformation for a lot of legacy companies.