Today on episode #793 of CXO Talk, we're speaking about Data and a I. Our guests are Inderpal Bandari, the Global Chief Data Officer of IBM, and Anthony Scriffiniano, the former chief of Data scientists at Dunn and Bradstreet Inderpal, welcome to CXO Talk. It's great to see you. And please. Tell us about your work at IBM. I'm actually a four time Chief Data Officer. When I first became Chief Data Officer in 2006, there were just four of us globally.
I was the first in healthcare and then the profession and the related professions like Chief Analytics officer, transformation officers, the century that took off and I happened to be fortunate enough to ride with it and I've done this job four times, IBM being the 4th and perhaps the most
complicated. At IBM, my strategy, data, strategy has been to make IBM itself into an AI enterprise and then use that as a showcase for our clients and customers, because our clients look very much like us. So that's what I've been doing for the last seven years, 7 1/2 years or so. And Anthony Scriffiniano, welcome back to CXO Talk. It's your good friend. It's great to see you and tell us about your work these days. Thank you very much, Michael. It's, it's great to see both of
you. So as you mentioned, I was with Dunn and Bradstreet for quite a long time, over 20 years. Right now I'm doing a number of things and probably front foot is as a distinguished fellow with the Stimson Center, which is a think tank, think tank. I'll put in quotes because there's a lot of what I would call applied research or action research where they actually get involved in doing things, not just writing about them I've been involved with.
Things that are called a I. The term's been around probably since the 50s but I've been involved with it in as it's become computational from its from its birth and and I know Interpol has as well lots of things going on in the world right now in terms of regulatory focus on a I as well as new types of a I becoming sort of the greatest new shiny object and everyone pays attention to them. And I I stand for the, the, the size behind it.
What do you have to believe? What has to be true in order for you to do that thing that you think is so cool and and why is it better than what you're doing today and and what is the cost of it? So I try to ask those emperors new close kind of questions and that's the role I'm playing right now. So we're talking about data and AI. And I think where we need to start is when we talk about an AI data strategy, what actually is that Interpol, you wanna maybe take a crack at that to
start? AI is only as good as the data that is used to train that AI, because AI has a training sequence and then an inference sequence. The training sequence has to do with seeing. All kinds of related data, so that it can actually then train itself to figure out what the right output is when it's shown an input that it may not have seen before. So if the data to begin with is flawed or low quality, the AI
will not work effectively. It's the garbage in, garbage out, that kind of phenomenon that you would have. So they go hand in hand. And very often you think of people talking about AI, and if they haven't really looked at the data, but they embark on a data on an AI strategy that is going to be very high risk, it'll most likely fail because they'll have to go back and straighten out the data strategy first, just so that it's fit for purpose.
Now when you say fit for purpose, what that means is. If you know what the business objective is that you're trying to serve, so it could be something quite narrow like a specific objective. It could be something like I want to understand what segments of my business. Should I try to try to expand to increase my top line then, in which case, if it's segments of business, you know, data about your clients, about your products, etc, those things
become very important. You'd want to make sure that they are, that that data is of very high quality. And on the other hand, if it's something at a strategy level, which is what kind of what happened when I joined IBM, I mean, you know, IBM wanted to be a cloud and AI company. And to be a cloud and AI company, eventually we landed at the point that, well let's transform ourselves internally before we actually show this off to our clients and customers.
That became a strategy that was enterprise wide and we realized that now while for instance not only do we have to make sure that our structured data is in order, but also our unstructured data because we are going to go after this. And transform ourselves into an AI company. So there are two, two aspects there that are relevant. One is at a strategy level when you're aligning, aligning to the business strategy or it could be more narrow to a specific
business objective. Anthony, the challenge of aligning the data strategy to the business objectives is something that many organizations struggle with. What thoughts or advice do you have on making that work? You've seen so many different scenarios. You would really have to unpack what Interpol just said quite a bit to really get at the essence of it, and I did when I was listening to him, but he was using some terminology very
carefully there. A lot of times organizations don't have one strategy, I mean make more money, you know grow, grow, you know fill in the blank, right. The the things that we learn in Business School, you can serve your shareholders, you can serve your customers, you can serve your employees. Kind of hard to do all of those things at the same time because very often optimizing for one is is less optimal for one of the others. So the strategy of which we speak.
And we start to talk about AI has some very serious implications, these methods that we're talking about. And I should say that these days it's rare that only one method gets applied very often. There are many methods being applied simultaneously. There are some commonalities. So one of the commonalities is that the quality of the data has many dimensions, so truth. If if your a I is going to ingest data, it's going to probably presume it's all true.
Well, all data is not necessarily simultaneously true. It may have been true at the time that it was created, but maybe not so much anymore at the time that it's curated. So how old is the data? How is it still true? How would you know that it's still true it before you consume it into an algorithm or an approach that presumes that? I love to say that. When we go to court, we swear to tell the truth, the whole truth, and nothing but the truth.
That's because those are three different things, and those are three different ways to manipulate veracity or understanding. So when you get back to this concept of strategy, well, whose strategy? What part of the organization? Specifically, what objective? How would we know when we were successful? And asking those questions is very often a source of intention because the people in the room that all think they want the same thing. Realize they don't.
And when you unpack it a little further, they realize that to get what the guy on the left wants, you have to get less of what the person on the right wants. And it's not pretty. So it's not really a technical problem as much as it is an alignment problem. And, you know, sort of getting everybody to agree on what they want so that we would know that this strategy of which we speak is actually what this a I of which we speak, is delivering
really difficult. These roles like the Chief Data Officer, the Chief Transformation Officer, the reason these are CXO roles is because of what Anthony just said. You have to be part of that discussion. It's not so much like there is the concrete business objective. Sometimes you get into those situations where it's very clear cut, but more often than not it's a strategic discussion in terms of a understanding, clarifying, perhaps even adding to the business strategy.
And then relating it back to what you're trying to do with data and AI, and unless you're in a position to have that kind of conversation and you also have the wherewithal to pull that off, you know that you won't be able, you won't really be successful. So that's why these are CXO roles, because it's really part of the negotiation that goes on to align the business strategy to the data or the AI strategy. Maybe I can just add a little bit to that, that the whole
concept of being in the room. It's so important back in the day that the the goals and the objectives would come down from on high and that the folks with the the keyboards will just make it so you know, it doesn't work that way anymore and it can't really work that way anymore. And so it's so critical that the folks in the roles that Inderpal and I have had have a seat at the table understand what went into the ask and not just the ask very often what. Folks want and what they need
are two very different things. And so without being arrogant about it, you you asked a lot of questions and you get at what they really needed in the 1st place, which is probably not what they started out asking for. You're talking about organizational alignment with business strategy, and at a high enough level, this is true for every business decision that needs to be made.
And yet, when you hear people talking about a I. And data strategy, the conversation turns and very quickly to what kind of data do we need? Where do we get that data? What's the technology that we're going to use to aggregate and to manipulate that data? What kind of models are we using? And so now I'm confused because you're talking about one thing and I hear the entire world talking about something
different. The world tends to focus on the hammers and the nails, and it tends to focus on. The the tools that are going to be used for the the, the purpose. If I come to your house and I say and if then you want to put an addition on your house and I've met with the architect and I understand your objectives, let's talk and someone else comes to your house and says I'm going to build you a beautiful addition and I'm going to use
the hammer, right. You don't really care about the hammer, so of course the hammer is important. It's very important that we have the right data, the right tools, the right technology, the right people. So it's people, process, tools and mindset. All of those have to be aligned in order to get this right, but it starts with making sure you're focused on the right mission. Yes, that number changes that piece of, you know, aligning back to the business it's.
So the way I would put it is, yes, no matter how promising the technology, no matter how dramatic the advance, etcetera, it doesn't let the organization off the hook for coming up with a sound business strategy and then of aligning these elements to that business strategy. That's still going to be very much needed, in fact maybe even more so than before as you try to go after these new approaches and methods.
Be sure to subscribe to our YouTube channel and hit the subscribe button at the bottom of our web page and you can subscribe to our newsletter and we'll tell you and notify you about our excellent upcoming shows and guests. We have lots of them. So would you say that the business side is more difficult or harder to achieve than the data and technology foundations in your experience?
I would say that if you take something new, so you probably want to draw the distinction between a mature technology and a technology that's more recent or Macent or emerging. And if you take something new like that like the latter then it's you know there. There is a tremendous amount of complexity on the technology side as well.
So early on when getting into this game, you know when you we were working on on. AI for instance, at IBM, it became very clear as we went forward that there were four elements that had to move in lockstep, data, technology, workflow and culture. And those four kind of had to move at the same time. Otherwise the adoption was not going to be not going to be effective and.
The technology piece for an emerging technology, so at that time, you know the cloud was emerging, there was a lot of AI techniques that were emerging, the deep learning stuff with, you know, GPUs and things like that. You have to make all that stuff work together. So there is a significant complexity in the technology piece, but there is also a significant complexity in the data piece and the workflow workflow piece and then eventually in the culture piece of the organization.
The the stuff that we were talking about in terms of the negotiation, working with the C-Suite, you know, there's a lot of the cultural aspect that goes into, there are many organizations one could go into and you would essentially not, Anthony said. They would still want to give you a set of objectives and say here, go off implement this. We really don't want to hear from you about anything else. These are your marching orders. Go off and implement this.
Well, that's the wrong approach when you're trying to bring in an emerging technology and use it to impact the business. Anthony, we have a question exactly on this topic from Twitter from Arsalan Khan and maybe you can share your thoughts on this. He says we when we talk about alignment, there's business strategy, enterprise business architecture, change management culture and now data strategy.
All right, Anthony, so what's your prescription than to make all these layers work together and align sounds almost impossible? Almost impossible is a synonym for possible. So if you said it was impossible, you know, now we have to, we have to talk, right? I think that first of all, thank you for the question. From someone who knows that I ask a good question, I would say it's really important. That you start with the question with the objective. Everybody wants to jump to the
technology. They want to jump to the the date of the deal. The the thing that we're going to the the, the, you know that there's there's two factions in the room. The one faction is focused on the the revenue, the growth, you know what's going to happen to the organization and the other faction is focused on, all right, let's get going, Let's start, you know, doing stuff, let's start cooking in the kitchen.
I'm usually the 1:00 somewhere in the middle of those two saying let's make sure we're answering the right question here. And I, you know, I'm not slowing you down. I'm actually making sure we get done in a way that we don't fall over the finish line. So it is very difficult to get all those factions in the same
place. Probably the most important thing you have to do is be able to listen to each other and not start immediately talking about hammers and nails or immediately talking start talking about what color we're going to paint the finished product, right. But you know, somewhere in the middle is, you know, why are we doing this? What are we not doing while we're doing this? Do we know there's a big difference between can we do it and should we do it. So what are we giving up while
we do it? What about compliance, What about regulatory, what about making? How do we know that the data that we have is the right data to make the decision you want Just because you believe it and you have your confirmation bias and you found one or two pieces of data that support your hypothesis doesn't make you right. So we have to ask these difficult questions and there's a very fine line between being
right and being dead. So you have to be able to ask them in a way that doesn't annoy, it can annoy them a little bit, but you have to annoy them just to the point where they don't kick you out of the room and and keep asking those, you know, help me understand kind of questions until we get to a shared understanding of what it is we're trying to achieve and the opportunity cost of all the other things that we're not doing. Interpol. But you're a technologist?
So if this is strictly then a business issue of organizational alignment, why do technologists play such an important role in this discussion of such a foundational fundamental? Role. I think the best way to think of my role of people in similar situations is that of a changing. The catalyst for the change is the technology. The change has to be affected in the organization and in the business. So you have to be able to bridge those two to be able to do this
successfully. So you know it's a transformation and the transformation typically has those elements that I talked about for what we do, what I do data, technology, workflow and culture and I'll give you one other thing, I mean it's. There is there is a lot to be done in terms of changing the culture of an organization when you try to bring bring about this change.
What we what we saw at IBM when we pushed forward with our data and AI strategy was that the adoption of the platform was triggered far more by the bottom up measures that we put in place. So we actually had a team that was empowered to engage with other teams. That were working in the business, you know doing workflows, go to cash procurement, you know things
like that supply chain. And so we have an empowered team on the technology side which was didn't really need to come back for direction or instruction but if they found a like minded team they could go ahead and and move forward with the transformation. We found that 85% of the adoption actually came from that part. As opposed to the top down path and so forth. So it really is all about how
you effect the change. But obviously if the catalyst is the technology then you've got to be able to walk that walk as well. So but you you can't discount the other side of it, you have to really be the bridge. Like I smiled when you called into call a technologist and he very diplomatically didn't didn't respond. I think you can tell by that answer that you have to be much
more than. Just an expert in the technology to get what he just said right in large organizations there, what's happening right now is a massive federation of data and a I capability. It's not like you go to the room where the people that know how to do that live and ask them to do it for you. Almost anybody can get these capabilities on their desktop. It doesn't mean that's the right place to do it, but they can start doing it there and everyone feels like they're an expert.
Just like when we all first got, you know, I'm trying not to name a product, but I think I can say Harvard graphics or you know, like in the days even before PowerPoint where all of a sudden we could all, you know, lay things out on the screen, we all thought we were experts in design and layout and font selection and and all of that. And of course we weren't. And and there's an old joke where the punchline is death by PowerPoint. We we all know versions of that
joke. And I'm not picking up PowerPoint. Federating A capability like that across an organization or across the world comes with some risk that those who really honor practitioners who know. What? The differences between what you can do and what you should do, who understand the implications of going down a certain path and the difficulty of changing course once you get too far down the path. They have to be able to hear what's going on so for.
When Interpol is describing to work, well 85% of the time does require an organization that actually talks to each other or or at least talks up to people who talk to each other up and down. But you know that's not always the case. So you can't just throw everything out in the middle of the floor and say here you go, everybody play with this and do whatever you want. That will not work. That will end in tears. So you have governance, you have focus on these foundational
pieces. What about the interface between the technology and what you're describing the whole world and and organizations by and large tend to focus on that technology piece. And so can you now maybe talk a little about technology management as it relates to what you're just describing and also selecting the right kinds of technologies and especially? Selecting the right kinds of data to match with the problems that you're trying to ultimately address. And I would add time that it's
still relevant. I think I have a good example for you. When when the pandemic broke out, I don't think anybody was really expecting that. All of a sudden organizations shifted to almost exclusively working from home. There's laws about what data you can access from home and what data you can access at your desk. You have a different firewall when you're working in the office than you do when you're
working at home. You've got developers that used to be Co located that are not Co located anymore. Organizations had to absorb all of that change while still trying to serve their customers, and in some cases failure to do so could have been life and death. So you know there's an urgency about this as well. You can't take forever to do it and you have to have good
discipline in place. So that when the unexpected happens in the middle of the other unexpected that was already happening, you have the resiliency survive that and come out of that stronger. I'm not going to suggest, although I could that I BM is one of those organizations, but you know mature organizations that get it and do that. We saw a lot of organizations that weren't so mature not getting it in the middle of all that disruption.
So it's it's just a very big question you're asking. The example of the pandemic actually was particularly instructed, I think it goes to your data AI questions of the earlier in the in the segment as well. So when the pandemic hit in terms of being able to run your business, for instance, make financial forecasts, make forecasts about your supply chain, about your procurement abilities etcetera, all the models that were in play were
essentially useless. Because we have now embarked on a situation that was completely new. And so no matter what technology we had in there from an AI standpoint or a model standpoint, it had been trained in a completely different world. And that's Anthony, was Anthony's point, right? I mean, it may not be true now. In fact, it wasn't true. What was true though was if we were able to get the data, accurate data, pristine data.
Into the hands of the people who were running those different departments along with an overlay of what was actually happening in the pandemic, you know, where COVID-19 was breaking, what were the incident reports in different areas. So if you could like geographically, then overlay that on what these guys were working on, whether it be financial forecasts or sales with, you know, they expected to close or procurement sites that were endangered, things like
that. They could make something out of it and move forward with it. And then so that that's I think also an instructive example of the relationship between data and AI and how that plays out as it as things really unfold that you know that are truly unexpected. Let me draw first blood on saying something super nerdy. There's a concept. I call it decision elasticity. I kind of stole it from economics. But how wrong can you be and still make the same decision
effectively? So you don't have to be perfect to make a decision. And Interpol is talking about training. There's an implication there that you have longitudinal data data from the past that you can project into a near term future that looks reasonably similar and you can measure the elasticity of your decisions. How wrong are they? And then if they start getting wronger and wronger to coin a term, then you can stop and and
reexamine those methods. The problem is when you have something completely disruptive, there is no data and the most dangerous situation you can find yourself in. Is when the world is changing faster than the data that describes it. That's exactly where we were at
that moment. You can't just throw your hands up and say, well wait, when you have five years worth of data, come back and I'll, I'll retrain everything and and we'll be good to go. You have to have methods in place that are effective in a situation where and that's what this environment taught us, that you can't just rely on one type of learning, one type of projection into the future. At that time, I was very involved with watching bad guys
do bad things well. When there's disruption, the best bad guys, especially if they think they're being watched, they change what they're doing. If you model based on what they were doing, you're modeling how the best ones are no longer behaving out of a dangerous thing to do, right? But we know this. And so the the flip side of that coin is if you know that the environment changed such that the bad guys are going to probably try to take advantage of it.
That many of them are probably going to do that unarthfully. And so you may be more easily able to see them as they run. You know, you turn on the light and the the, you know, the little creatures run away, you can see that. And so there might be an opportunity there along with that risk. So it's it's sometimes these these situations are, I would say almost never are they all bad or all good. There's always something in it
that can teach you. There's always something in it that can make what you're doing better if you have enough time to breathe and observe what's going on and use the energy in the best possible way. It doesn't mean the bad thing will stop happening, but it may mean that you emerge from it in a better way because you you took that time to be more thoughtful about it. So we have a question from Twitter. Elizabeth Shaw says the issues you're describing are true of any business or technology
transformation. Are there particular points issues that are more problematic for AI enabled initiatives? Can you kind of drill down into that? If you look at the advent of AI, the progression of AI, it's moved very, very quickly in the consumer space, but not so fast on the business space. And that's because in the business context, people don't trust AI. And they don't trust AI for multiple reasons. I mean, there's the, we talked about some of the issues about
the data. So the robustness of the data, the quality of the data, the currency of the data. Then you also get into issues that have to do with the fairness of the algorithms. You know that the results they produce are going to treat people fairly if they pertain to, if the, you know, the data pertains to people, you have the
issue of privacy being invaded. In terms of the algorithms discovering something new, you know there's this famous example of or infamous example of retail retailer, large retailer actually looking at the shopping shopping data, shopping patterns and shopping data and then inferring that. That this person is is pregnant and actually mailing their home and it turns out to be a young lady and you know, it was, it was it was really a complete invasion of her privacy.
So those aspects come in. Then there are the issues around the job displacement and things of that nature. You know, if you're applying AI in the enterprise, there are two flavors of it. There's the automation flavor which has to do with when things are kind of straightforward. And you go from one step to the other and you know what those steps are and you can automate all that. So there's job displacement associated with that.
But even on the decision making side, where the AI is actually helping make a decision, there's a decision maker in play and they have to trust it. They have to say, well, this is. Going to, you know, this won't displace me.
So and extending that further, the executives as you put AI, we kind of know by now that AI has to be infused into the major workflows of the business, things like procurement, supply chain, etc. That's the kind of IP that doesn't get published in papers or patented or anything. Those are the trade secrets of a company. So they have to be able to trust whoever the vendor is of this software that this is not something that's going to
disintermediate. Furthermore, the decision maker that's working with the system has to understand it. You know, years ago we I did this computer program called Advanced Scout that ended up being used by every coach in the NBA. And I remember the first time it had a counterintuitive finding. It basically asked the coach to play 2 backup players in a playoff game that they were on
the verge of elimination. And he, you know, he was very concerned about that because he felt if I make, if I do this and I lose, I'm gonna lose my job and reputation as well in addition to the series. And we kind of solved that problem by letting him see the video clips of when those two players were on court. But that's the explanation piece, right? So if you tell a doctor, amputate the left leg. They're going to have all kinds of questions, OK, why amputate? What other options were
considered? Why is amputation the right one for this patient? Etc. So explanation is another big part of it, and the AI systems today don't do a good job of all that. So those are the special aspects of AI and trust that come into play. I think that was a fantastic list and I won't vain to add to it, but I will suggest another dimension to it. So great question. Like how how are the A, I issues? What's special about the A I issues?
I would say another one is that you have the opportunity to fail faster and at larger scale. There's a tendency once these sorts of systems are implemented, someone says, well, it's 99% accurate, it's 92% accurate, it's 87% accurate and you assume that means that 87% of the time the prediction will be right. Well, no, that's based on the past. In the future, right? Very rarely do we measure fast enough to stop every conceivable
bad thing from happening. Interpol hinted at something which is an observer effect that people, when told what to do by a quote UN quote machine, will sometimes. Think they know better or not want to be told what to do by a machine and do something different just because a machine told them to do it? To prove that they can do something and not necessarily thinking it out loud.
Like that Question I get asked a lot is, you know, what about someday when will people be reporting to robots or robotic bosses of some sort And you say, oh, of course not. I would never do that. And then the GPS tells you to turn left or right and you do. And Outlook tells you to go to a meeting and you go. We're already taking a lot of
direction from automation. I won't call it a I necessarily, but from automation and the the the human fact of what we do as human beings to accept or reject that device is essential to get at trustworthy a I to get at making sure that we don't marginalize others that are already marginalized more because they don't have access to these technologies. This concept of good and and not good is is a very, it depends on where you're sitting sometimes whether it's good or not good.
There are certainly lots of volumes, books, committees focused on trustworthy, A, I and explain ability. There's legislation as we speak being considered that will hold the feet to the fire of anyone who is implementing anything called a I. So you know to say that it's not being adopted by business. The adoption is lower, I think in some ways because of some of these human factors. It's not a lack of technology, it's a reticence to just push
that button so quickly. And you know, technology will always outpace regulation. So you have to be careful where you could find yourself in a in a world of hurt where now they're coming after you because you use that technology that made a better decision. Good luck trying to prove that sometimes. This is a question from who Wong and he says we can sometimes measure the cost of implementing
data solutions. But how can we measure the operational costs when a business decides not to implement certain solutions, such as governance or data quality? The opportunity cost, the cost of not doing something. And thank you way for that question that would. That's that's a big one and I
think it's an important one. If we're going to decide not to do something, we should decide not to do it on purpose, not just because we got tired of arguing about it or because we didn't want to take the effort to get all the data that will be necessary to make that decision. So one of those annoying questions that I usually bring into the conversation.
Is if we're going to decide not to do this because there's some other thing that we want to do and that other thing has been deemed more important, great let then let's make that decision. But let's understand the opportunity cost, the cost of not doing in. Many cases it does become clear cut because you might have regulations that then levy huge fines, for instance in the European Union GDPR for instance, if you don't have the right set up for governance and
privacy and so forth. You'll be hit by a major fine.
In other cases though, when you know they're making these decisions, they might choose not to do the governance of the data, but it'll end up reflecting in the actual output that's being produced, and then somebody has to go back and fix it. So keeping a keeping tabs of that, you know, I'm assuming here that you've lost the argument and they've gone ahead with it without actually, you know, then keeping tabs on that and raising that every time it
happens. I think very quickly you'll be able to make a difference in the in the way people are viewing it because nobody wants, you know wants a disaster. And if the if the if what's if they've skipped that step which is which has major magnitude, you know, as sometimes that'll happen and the collaboration is the name of the game. So you just want to then keep an eye on it, warn people that this
is going to happen. And every time it happens or even before it happens, you raise your hand and say look, I told you about this, now let's do it. This is from Jav Boshinov, who is a professor at the Harvard Business School, and he's also been a guest on CXO Talk, and he says this and Interpol. Maybe I'll ask you first, is there anything different between generative A I and more traditional A I? And how should organizations
approach? This I think the best way to think about generative A I. The promise is that you can do things conversation. So just as you and I can have a conversation and we can discuss something and try to get get to some resolution. That's the hope. So now if you apply that in a large organization and say I've got some intelligence that can now conversationally help me do client support, employee support, my IT operations, etc. That's you know, hugely, hugely promising.
On the on the other hand, I mean the way these systems work today. You know, the best way to understand generative AI that I've been able to get my mind around it is in a sense, each word is predicted, and then the word, essentially that word is fed back into the input and then the next word is predicted. It's almost like when you and I are talking, I'll sometimes do this. I'll go out on a limb, I'll start saying something and the
thought hasn't fully formed. Usually I'll manage to come out of it. And but many times, you know, I'll end up with my foot and my mouth. So the generative AI techniques are essentially going out on a limb every time because it's, which is also why they're not always consistent with the response. You know, you might have the same problem to give you a different response because it's actually working off a
probability distribution. So I think there's a tremendous amount of promise, but also a tremendous amount of work that needs to be done to address some of the issues that we've raised earlier. Anthony, Differences between Generative AI. And traditional AI and implications for the enterprise and pretty quickly please generative. AI is is making stuff that didn't exist before based on stuff that it observed.
And that stuff can be taxed, it can be images, it can be anything that we as humans consume. So the the the challenge to it is that you look at all the stuff in the past and you you kind of compute on it and do a lot of math and then you generate something that looks like a human said it and a human didn't say it.
And so when the world changes and the corpus of data that it's looking at didn't change fast enough, that nuance gets lost and we lose the ability to understand something nuanced. So if the if the purpose is to provide customer support based on frequently asked questions, or if the purpose is to summarize a whole bunch of things that you should have read and didn't have time, it's a fantastic idea. If the purpose is to to write some new thought leadership on
something. Maybe it's a starting point, but it would be very careful when we consider that to be an ending point. Share final thoughts on advice that you would give to business and technology leaders who want to be more effective using data using AI. Interpol. You want to jump in with that one first. I've been doing this for the last 2025 years, starting from the days when I did that program
for the NBA to now. And I've always felt that whenever I was doing it, I thought, oh, it can't get better than this, but it always seems to get better than that. And I think we're now in one of those moments where there is the potential and the opportunity to have a tremendous impact not just on business but also on society. And I think because of that implication that there are these major societal considerations as well, we absolutely have to get involved. And that would be my biggest.
You know, advice to people either on the business side or on the technology side, you need to really get involved with what's happening here. And there's just tremendous, tremendous potential and it's there's never been a better time to be involved in data in there. Anthony, it looks like you're going to get the last word here. Number one, I would say ask why a lot? Why are we doing this? What do we have to believe? Why this data? Make sure that you understand before you jump into.
That method with that data. Make sure that method and that data are in some way justifiable, like not only against what you intend to do, but against what you're not doing by by doing that instead. And then the second thing is make sure that you pay very close attention that how the environment is changing so that you don't get caught by the change that makes what made sense no longer sensible. And then the last thing is something I always advise, which
is to be humble. It is extremely rare. When you know everything you need to know and have all the information you need without widening that circle and bringing in others that have some sort of expertise or some sort of perspective that you don't have. So inviting that expertise and that perspective is not a sign of weakness, It's a sign of great strength. With that, unfortunately, we are out of time.
I just want to say a huge thank you to Anthony Scriffiniano and into Paul Bandari. Anthony, thank you. It's. Wonderful that you're you've been here again, and I hope you'll come back another time. Absolutely. Thank you so much, Michael. And Interpol, so honored that you joined us. And again, I hope you as well will come back and be a guest on CXO Talk again and on another date. Delighted to do that, Michael. Thank you for having me.
And for those with unanswered questions, please, you know Lincoln and we can continue the conversation. Everybody, thank you for watching, especially those folks who just ask such great questions. You are such a smart and bright audience and we love your questions and keep watching. CXO talk, go to cxotalk.com, be sure to subscribe to our YouTube channel and hit the subscribe button at the bottom of our web page and you can subscribe to our newsletter and we'll tell you and notify you.
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