Skills CIO Need to Succeed in the AI Era

In this Tech Barometer podcast, Dr. Norman Jacknis explains how CIOs can adapt to rapid change driven by AI and the value it can bring to organizations.

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Transcript:

Dr. Norman Jacknis: If I ask you to travel from New York to Boston, you pretty much know what your options are. You can fly, you can take a train, you can drive, you can get a map from Google, whatever. There’s a whole bunch of things. It’s pretty straightforward, and you might run into a traffic jam or something like that. It’ll sell you down, but basically you know where you’re going and what it’s going to be like when you get there. With ai, particularly generative AI these days, it’s more like the Louisiana purchase in the Lewis and Clark Expedition. You’ve got this whole big territory that you have to explore and you’re not going to know what its value is. I mean, ultimately in the long run, it’s the best $3 million investment the United States government ever made, but you don’t know when it’s going to start delivering. You don’t know exactly why it might deliver. The whole nature of artificial intelligence is it’s an iteration, it’s an exploration, and so that’s one of the reasons why the people who expect to see a return in the next quarter or the next two quarters are really missing. The point about what this is about.

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Jason Lopez: Norm Jacknis is Professor of Practice Innovation and Entrepreneurship at Northeastern University, which is located in Boston, Massachusetts. Before coming to Northeastern to teach technology leadership, he was an executive at Cisco and the CIO of Westchester County in New York. Those are just a couple of his consequential stops throughout his career. And in this podcast, you’re going to hear about the role of the CIO in establishing AI and organizations. What makes this compelling is that as a professor, he’s had to gather together his deep experience in technology and organize it to teach future CIOs, and we gleaned some of his best insights. This is the Tech Barometer podcast. I’m Jason Lopez, so let’s pick up from where we started this podcast with his comment about understanding what AI in the enterprise is.

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Dr. Norman Jacknis: There was some hype clearly, but I think there’s been pushback, particularly from CEOs, they see the value in this stuff these days. What those people are talking about is generative ai, the more traditional machine learning for prediction, for improving interactions with customers so forth. That’s already a given. You can see companies that have been using that and have been making money from that, but even on the generative AI stuff, I think they hyped it up too much, and I think outsiders who haven’t done this successfully don’t understand the nature of working with ai. It’s different from a traditional software project.

Jason Lopez: Jacknis says the hype around generative AI has put so much attention on language processing. Perhaps it’s easy to forget about machine learning, something he reminds students who are mid-career executives.

Dr. Norman Jacknis: There’s still a lot of low hanging fruit, if you will, more traditional machine learning. That stuff can be used to help identify potential customers, help identify problems ahead of time that you can fix all that sort of stuff. So that kind of thing is still very useful and can be done, and the generative AI stuff properly used also can do some things to make it easier for your users and customers to have access to the computing resources they need instead of having to be subject to the constrictions. The restrictions that have come up about over decades of computer science history

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Jason Lopez: Those decades ago before it was AI or generative AI or machine learning, it was called computer automation where for example, you took what was on paper and put it on a screen.

Dr. Norman Jacknis: Yeah, no, I suppose it was an improvement. I’m not sure how much it was annoying, and of course, they’d ask the same questions over and over again the way you did on paper forms. Now with generative ai, one of its advantages is it can be much more conversational and it can be smarter about what to ask. So if I put down than I’m mail, it’s not going to ask me how many months pregnant I am and things like that. It’s a lot smarter and it makes that whole human computer interface a lot easier.

Jason Lopez: He talks about AI as a strategic tool. There are ways to deploy it that aren’t so great and ways that are successful.

Dr. Norman Jacknis: One of the things about the successful uses of AI is that they help break down the silos in companies because you have to, the folks who are expert on the algorithms don’t know the business. For this to work, they need to speak to the business, and the nice thing about it is AI is sexy enough. Everybody’s quite willing to work together on this. So I think that’s actually kind of helpful. So there’s a strategic value in this as well.

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Jason Lopez: Jacknis gave us an insight in what he teaches students in his technology leadership courses, how a business can get started in ai.

Dr. Norman Jacknis: Don’t go out and hire a whole bunch of data scientists and programmers to write your own AI cloud providers, as you know, offer most of the various options for AI tools. There also is open source stuff. One of the things I use with some of my students is a product called nine K-N-I-M-E, which is from Europe, open source and free. Got a lot of these algorithms there. But start off with that. So the focus is on how do I want to get the data and present it to the computer so it can come up with the best predictions and not worry about getting stuck writing all kinds of blower level code.

Jason Lopez: Once a CIO’s team has become successful in AI deployment in the cloud, measuring that by an uptick in revenue, Jacknis says about 5%. Perhaps it might be time to swap out some capabilities for something more proprietary, but he emphasizes use the cloud and operate on laptops before doing anything on-prem.

Dr. Norman Jacknis: When this started out maybe a few years ago, a lot of the people in AI and machine learning wanted to use the internal data centers that their companies had because there was a big concern about data being proprietary. They didn’t want a lot of people know it and all that sort of stuff. And then the CIOs who were not part of this process pushed back and said, you want to run this program, which is going to eat up gobs of computing resources while I’m trying to run a payroll or a CRM. No way. So in some respects, they were told internally, push this outside, put this in the cloud because we’re not going to let you do this internally.

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Jason Lopez: And another piece of advice from Professor Jacknis, create the culture around AI deployment. He’s seen failure around treating AI in the enterprise as a technology project, treat it as a business project

Dr. Norman Jacknis: As opposed to we have a set of tools that will help us learn more about the environment in which we’re operating in, help us identify customers better, all that kind of stuff. Instead of saying, Hey, this is a joint effort of everybody who’s got some experience worthwhile in our company to work together. They treat it as a technology thing and then it fails partly because typically what they’ll do is they’ll think, oh, I have a whole bunch of data. Lemme throw it at the computer and see what happens. Oh yeah, the computers are good, but not that good.

Jason Lopez: He goes on to say that when establishing an AI component to business operations, make sure you bring in some people who truly get the business side of it. When he spoke about this, he referred to research showing just several years ago that new AI teams were made up of mostly data scientists.

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Dr. Norman Jacknis: Ninety percent of the people in their AI projects were all data scientists. A couple of years later, it was maybe a quarter or 20% because what they found was they needed people who understood the business side at the beginning of this process, and they needed people who were better at knowing how to implement systems. Once you figured out what you have and the actual amount of data science work was small, the computer’s pretty good at this. In fact, you’ve got some computer systems now that will sort of basically be automated data scientists. What they can’t automate is the business understanding that you get from human beings, and they obvio