During a recent and fascinating interview with Sol Rashidi, author of "Your AI Survival Guide" and a seasoned technology executive who draws on her experience leading data and analytics initiatives at companies like Sony Music and Estée Lauder, I was able to pose a question directly about 'static data' vs. 'flowing data'. Illuminating. Michael Krigsman: We have an interesting question from LinkedIn. And this is from Greg Walters,... Vertical Divider
Michael Krigsman: We have an interesting question from LinkedIn. And this is from Greg Walters, who says he finds the static versus live wire discussion very interesting. Static, static data versus active, ongoing, flowing data. And he says, "Do you think this is the biggest obstacle when looking to move AI into a process?" He has a follow-on question, but let's address this one first. Sol Rashidi: And I always refer to it as static code versus dynamic code. And the easiest analogy is software development versus AI development, because in AI, you're constantly having to retune because you're going to get new data sets. There's going to be more anomalies. So, there's an element of babysitting, for lack of a better term, that needs to go into AI applications that you're scaling that I don't think most people are talking about yet, because in 2023, it was all about kicking the tires in ideation. 2024, it's about POCs or finishing POCs. I call it a live wire because it's not one and done. There's never a finish to AI projects. That pivot from 2024 to 2025 is going to be pushing things into production. And how do we build an organizational model that can continuously babysit the output, the results from our AI capabilities, to make sure that context is right, the situation is right, the circumstance is right, because you're feeding it new information consistently with anomalies that haven't been detected before. I call it a live wire because it's not one and done. There's never a finish to AI projects. You know, sometimes you can build a mobile app and you're like, "Oh my God, that was intense. Woo! Let's have a celebration. We're done." You can't do that. Not with the AI applications. There's always going to be an element of babysitting. It's kind of like having children. You think they graduated high school? "Oh I'm done. Let's have a celebration. We're done." You can't do that. Not with the AI applications. There's always going to be an element of babysitting. It's kind of like having children. You think they graduated high school? "Oh, I'm done. They're off the paycheck. They're not. They come back to you in college. Oh, and guess what? They graduate college." It's just, the older they get, the more complex the problems are. They're your children. They're always going to be around, and their issues are always going to be your issues. Michael Krigsman: So, you're really going back to the human in the loop that you were describing, but overlaying this need for a culture change, a mindset change in in the way that an organization relates to AI apps as opposed to traditional static software applications. Sol Rashidi: And for all middle managers, practitioners, executives, whether you work in enterprise or a large company, we all kind of know it's never the technology that's been a hindrance for us. It is the change management, the thing we always talk about. But, the thing we always short-circuit or shortcut is when we're actually doing these large transformational programs. That's the reality of it. It's, "How do you upskill an existing talent base to understand this new application that was thrown out? Then, how do you increase the literacy to let folks know that jobs aren't going away? They're just going to shift and look a little bit differently? How do you embed it within existing processes that are now being changed and creating a new org model to be able to support it? How do you now change the incentive programs of both the executive and the team to make sure that there's adoption? Because fundamentally, there's business value, we determined at the end of it, versus it being another tool in the tech stack that doesn't get used. These things need to be discussed because this isn't standing up a data warehouse. It's a high cost. It's a high capital investment. Michael Krigsman: How many, folks, business leaders and technology leaders, relate to AI projects as if they were, or as if they are, traditional software projects. They leave out some of these elements that you're describing, and as a result, have what they then would call a failure because it's because the project is not meeting its expectations. Their expectations. it seems that you don't see a big difference between AI and old-fashioned IT projects. Is that correct? Is that a way of looking at it or not." Sol Rashidi: That's what I'm trying to get folks to avoid. Because there's a stat right now, right? That 73% of AI projects are leaving companies underwhelmed. And there could be a series of reasons, right? They never finished it to the intent and the vision that they had originally planned. So, they cut it short, they ran out of funding, or whatever it may be. Or, the expectations were never set up. Right. And so, everyone thought that it was going to solve world hunger, only to find out that, "No, we're just automated a few steps, but we're not getting what we deserve to get." But, they never measured the human error rate so they can understand. Or, at least, you know, what I call time in transition, how to manage that workflow. There weren't enough statistics to do the before and after, so they can see the overall halo lift that it created. There could be a variety of reasons for that stat, that over 70% of companies are underwhelmed. There are exceptions. There are some amazing companies that have done some really amazing work. I don't want to discredit that. But, for the majority, folks are still struggling, and I think a lot of it comes down to that. So, as a shameless plug, like, some of the things that the book, I don't know everything. It's impossible to know everything. And I automatically raise an eyebrow when someone says they're an AI expert. I'm like, "Really?" Like, I've been in the field for a really long time. I feel dumber than ever right now because the pace of change is so fast, and I can't keep my finger on the ball. So, you're an expert? Then automatically raise an eyebrow with the intention of the book is, at least, "Don't go away not knowing what you don't know. Be informed." And I don't mean the high-level articles in the white papers that you're reading on, like the management consulting firms and LinkedIn. There's grittiness in the book. There's a series of failures of and why the failures were there. At least, become aware of the assumptions that were made that didn't hold true, in the mistakes that were made, that didn't pan out, so that if you're going to make 19 mistakes in that project, you shrink it down to like three, and you still have a high likelihood of succeeding. Michael Krigsman: So, Greg Walters had a second part to his question, and I asked you this in a different way earlier. He says, "That it seems that you don't see a big difference between AI and old-fashioned IT projects. Is that correct? Is that a way of looking at it or not." Sol Rashidi: No, no. I think we make, we establish three clear distinctions. You're going to face higher resistance from the team because there's an element of fear. So, it kind of has to be top down. Two, because you're only as good as your last data set. It's a live wire. So, it's not something that's one and done, and that you can finish. And then three, you do have to change the organizational model to account for when something goes into production, because you always need a human in the loop in that feedback loop to make sure that the output is aligning with the expected intention. Other aspects of it is, yes, measuring human error versus machinery. So, you can set up expectations upfront. So, there are definitely some distinguished components.
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