In this podcast, Dr. Beverly Wright, Vice President – Data Science & AI at Wavicle Data Solutions, engages in a captivating discussion with Rachel Stuve, Senior Director of Data Science & AI at Elevance Health, focusing on the topic “The Role of the Chief Artificial Intelligence Officer (CAIO).” They explore the essence of a CAIO, delve into the intersection of technical expertise with business acumen, and uncover the transformative solutions this position can bring to organizations. Tune in to untangle the multifaceted dimensions of the CAIO role and its impact on driving innovation and progress in the dynamic realm of data science and artificial intelligence.
Speaker details:
Watch the full podcast here or keep scrolling to read a transcript of the discussion between Beverly and Rachel:
Beverly: Hello, I’m Dr. Beverly Wright, and welcome to TAG Data Talk. With us today, we have Rachel Stuve from Elevance Health, and we’re talking about the role of the Chief AI Officer.
Thanks for being here, Rachel.
Rachel: Thanks for having me. Beverly.
Beverly: Absolutely. Let’s start off with a little bit of background. Tell us, why you are so cool?
Rachel: Yeah, I am probably one of the few people who went to school for what I’m still doing. I went to school for analytics and data science, which at the time was not called that. But I’ve just naturally grown up through AI and data. I’ve seen how it’s evolved from the very early days of computer programming to now, gen AI. And one of the things that actually makes me really cool is that even though I have a really technical background, I most often sit in with the business. So, I have a lot of strategy background that I bring to the tech and then vice versa, bring the tech to that strategy.
Beverly: Yeah, that’s a great combination and not something everybody can pull off. And I have to say, you have a really good reputation for being able to do that from people I know that you work with.
So, we’re talking about this new sexy title “Chief AI Officer.” What is this thing? Is it the newer version of a Chief Analytics Officer? Is it something different? What is it?
Rachel: Yeah, it’s a good topic. I actually read an article earlier this morning in a technical magazine that about 25% of companies are evaluating adding a Chief AI Officer to their executive team. And I thought that it was really interesting that a lot of businesses are really starting to see the value of data, not just in operational savings, but revenue generating and vice versa. It can be both. And I think it’s really interesting that we have this influx of companies that are saying, “yes, it’s such a critical piece of our business that we’re going to create an executive officer role around data and AI.”
Beverly: How do you see? I mean, it’s still being formed, so who knows what’s going to actually happen? But how do you see this reporting structure? Are they reporting to the CIO, the CTO, the Chief Strategy Officer, or directly to the CEO? Where do you see them reporting?
Rachel: Most often, what I’ve seen and would recommend is right into the CEO. So, a Chief AI Officer needs to have a technical background. That person needs to understand how the technology works, what the nuances are, and needs to have an engineering background, data background, and data quality background. So, all of these things that are typically very technical. But the true role of a Chief AI Officer to be successful is in the adoption of the AI.
Beverly: Adoption. So, you think that should be their job. Not even to necessarily generate because people are generating AI all over the place and you almost can’t harness it but the adoption. So, unpack that a little bit.
Rachel: Yeah. As we were chatting earlier, a good number of AI projects, more than 80%, and we said even upwards of 90-95% of AI projects, fundamentally don’t succeed or are not adopted widely across the organization. Some of that is because there is a lot of research and development in AI, so we would expect that. But a good part of it is where we’re seeing now, especially with the proliferation of gen AI and getting more democratization of data and AI, is: what are the acceptable use cases where AI can truly help? And where may another solution be better? So, a key function of the Chief AI Officer is to evaluate: what are the appropriate use cases? And what are the pros and cons of those use cases? AI is not a panacea for every business problem. And I’m sure you’ve talked to people every day that sometimes, there is that thinking.
Beverly: Yeah, they want to go straight to AI for everything.
Rachel: Absolutely. Sometimes, it’s a fundamental business problem or a process problem that needs to be solved, and AI could potentially even exacerbate that.
Beverly: Is there a kiss of death as far as reporting structure? We talked about how they should go to the CEO, but if they go to the CIO, the CSO, or the CTO. Is there a place where you’re like, “Well, I really think they should go to a CEO, but the worst thing you can do is put a Chief AI Officer in here.”
Rachel: I think the worst possible place is going up to the CFO, which I have seen in some organizations, where it’s considered financial. That tends to be more of a kiss of death in the sense that AI can be cost-saving and revenue-generating, but it also has to be holistic across organization. Sometimes, putting that in a financial organization or under financial oversight limits the ability to see the application across the organization, and it reduces some of the AI into, “are you saving me money? If not, I’m shutting this part right there”.
Beverly: Yeah, how do you ever innovate? I mean, could you even get any research or any sort of trial-and-error innovation if you’re being held accountable for every single move you make? That would be tricky.
What about other c-suite data roles like Chief Data Officer, Chief Analytics Officer, or Chief Data Scientist? Is this even a data science thing or more of an engineering thing? How do they interact with these people?
Rachel: Yeah, the Chief Data Officer is a great role. Data is really the key to AI. You have chatted about that on a lot of your podcasts, where if you have incomplete data or dirty data, that can really affect your models and outcomes. So, that’s a critical role, especially when we start as companies building models and AI that does cross the organization, and it proliferates much more into the organization, and we find AI to be a lot more ubiquitous. Now, some data that may previously have been contained or just for the benefit of one group, fundamentally, may flow upstream or downstream where those particular users may not be invested in that or even have that insight. But a Chief Data Officer working together with the AI officer can truly build a comprehensive data strategy for an organization.
Beverly: Yeah. So, none of these people are going away. This is truly a net new type of thing. Do you see certain things, a Chief Data Scientist going away? Because all of a sudden, if I work at a company, and I’m the Chief Data Scientist, and someone is hired as a Chief AI Officer, I might feel a little queasy. What do you think?
Rachel: Yeah, the Chief AI Officer, in my experience, in my opinion, is a very strategic business role.
Beverly: That’s very interesting. So, you see it as a strategy-business-type role, even though these people need to have technical chops, at least to some extent.
Rachel: Yeah, I think, with that being a Chief Data Officer or Chief Data Science Officer, I would love to have a Chief AI Officer. Because that’s someone who is going to advocate for me to my users and say, “Hey, here’s the advantage of this.” And it’s going to take AI and data science, which historically has been a cost center, and put it into a strategic and revenue-generating center.
Beverly: Very interesting. So, there’s 25% right now that are really considering a CAIO. What do you think that number will look like in five years?
Rachel: Well, I’m a very positive person. In five years, that will be very common, greater than three quarters of the companies. Especially with a lot of the work that I do in the industry, as well as in the investment community here in Atlanta, a lot of organizations that historically you wouldn’t think necessarily were very data-based organizations are leaning very much on data now and seeing that as a competitive advantage. Even industries that historically were very service based – new auto industry, certain types of retail or retail services, or very small businesses – are now leaning on data for competitive advantage.
Beverly: Is this reserved only for big companies? Because you hinted about small companies. Is this only a big company thing? Or do you think everybody needs something?
Rachel: Yeah, everybody needs something; I advise startups that it’s much easier to do it at the ground level and start tracking than it is to go back and retroactively try and do that. For small organizations, it doesn’t have to be something like gen AI or very cutting edge. It could be something simple, like an online database or a spreadsheet, where you’re tracking sales, costs, different types of customers, demographics, what do they usually buy, or when do they usually buy. That type of information, if you can start gathering it, can actually, in many aspects, help a small company much bigger than a larger company. Because when you’re at a point when you’re small, you can grow multiples every year. That type of data can be very valuable to that.
Beverly: Right. That’s a good point. So, two more questions. This is really intriguing because not just in data, this is the big job right now. So, one question is: what about companies that are slow to adopt? There was talk, and I would say, 25 or 30 years ago, when I was early in my career, about if you don’t see data as an asset, you’re going to be in trouble. Your shares are going to go down, you’re going to lose revenue, you’re not going to be a great employer because people aren’t going to want to go to you if you don’t see data as an asset.
Well, now it’s given, of course, you have to see data as an asset. Is AI going to go like that, too? If people don’t start bringing on the CAIO role, are they going to fall behind? If so, in what ways will they fall behind?
Rachel: Yeah, the key is being very intentional about the application. I’ve had conversations with business leaders, and they hear all of the news about AI, especially now, gen AI, and they want to do it. “I can sell for more if I say that it’s AI-based.” And there will be almost AI exhaustion that may come in, where if you’re a consumer or client and you’re trying to buy something, and everything says it’s AI…
Beverly: Yeah, you get tired of hearing about it.
Rachel: Exactly. But for the companies that can sit and be very intentional about how they are implementing, I do think the Chief AI Officer role, if it’s implemented properly as a business function and a strategy function, I absolutely think those companies will have an advantage, Because they will better understand their customer, they will better understand their competitive ecosystem. In private companies or startups, it will be vastly easier to raise money and differentiate yourself from other organizations. So, I do think that companies need to look at adopting but be very intentional from a strategy perspective.
Beverly: Don’t just do it because you have to or you want that stamp of an AI. That’s a great point. And then the last thing before I get your final piece of advice, “Rachel, I’m 17, I’m in high school. What do I do to get a Chief Data or Chief AI Officer role? I want that job in 10 years; what do I do to get there?”
Rachel: Yeah. I tell the people I mentor and on my team that it’s very good and also easy to focus on the technical aspect; I want to be a great programmer, I want to understand this, I want to run my code efficiently, and that has a lot of value. But to really grow in leadership requires that business acumen where you can simplify AI to have a conversation with another business leader to truly understand their problem, and then look in your technical toolkit and pick the best tool that’s going to solve that problem.
So, what I tell my students is, yes, focus on your programming and your math; that’s really important. But go and take public speaking classes and a writing class because a lot of the things it takes to be successful in that Chief AI role are communication and persuasion.
Beverly: Isn’t that something, we’re down to talking about influence again? Business influence, and you see it as a business and a strategy type of function. So, it’s a way of matchmaking, use cases, and having the toolkit, but also, you mentioned things like communication, influence, and those sorts of things are not part of the technical toolkit. So that’s a good set of information to know for people who are trying to get there.
And then, lastly, what final piece of advice would you give to people who are trying to better grasp this Chief AI Officer role, especially if they’re interested in becoming one?
Rachel: Yeah. I have a lot of advice. But the advice I have is to really dig in and understand the business problems. Because a lot of times, what a user may say is their biggest problem isn’t really the problem. So, sitting and doing observation, talking, or doing the research to say what is the actual problem. Because that’s where you can truly design a solution that will be adopted. Because you’re solving the problem, not necessarily a symptom or what someone told you.
Beverly: Right. And I’ve seen that so many times. That’s a big part of why much of what we produce doesn’t make it past the shelf. I have actually written an article about business problem framing because I’m trying to help stimulate that. So, I’m glad you highlighted it and confirmed me.
Thank you again to Rachel Stuve from Elevance Health for talking to us about the role of the Chief AI Officer.
Rachel: Thank you. Thanks for having me.
Harness your organization’s data potential and unlock AI opportunities with strategic AI leadership as highlighted by Beverly and Rachel. Their insightful dialogue sheds light on the importance of effectively engaging with the C-suite, navigating complex business environments, and capitalizing on AI to power data-driven success. Explore the full catalogue of TAG Data Talk conversations here: TAG Data Talk with Dr. Beverly Wright – TAG Online.