Name: Bruno Aziza, IBM
Uploaded: 2024-10-31T17:25:00.000Z
Duration: 21 min 25 s
Description: John Furrier hosts an interview with Bruno Aziza, VP of Product Management, Data and AI ay IBM to discuss IBM’s updated strategy on Business Intelligence and Business Analytics and upcoming announcements to provide context on how those announcements validate IBM's GTM strategy and commitment to their clients.

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Bruno Aziza

Group VP, Data, BI & AI

IBM

play_circle_outline IBM Analyst Forum 2024: Bruno Aziza discusses Gen AI's impact on data infrastructure evolution

play_circle_outline IBM's credibility with enterprise and culture of invention

play_circle_outline Importance of multi-cloud, multimodal, multi-step approach in Gen AI

play_circle_outline Role of IBM in simplifying complexity and orchestrating for better business processes

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Bruno Aziza, IBM

Bruno Aziza

Group VP, Data, BI & AI IBM

In this special coverage from New York City, IBM Analyst Forum 2024 is discussed, focusing on the importance of setting up infrastructure and data for the upcoming generation AI wave. Bruno Aziza, the newly appointed group vice president of data analytics and AI at IBM, discusses the opportunities and advantages IBM has in the enterprise and the culture of invention within the company. The conversation touches on the evolution of analytics, the concept of assistants versus agents, and the need for simplification and collaboration in the multi-cloud, multi-mod... Read more

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What is the significance of setting up infrastructure and data for companies in preparation for the Gen AI wave? add

What advantages does IBM have when it comes to using Generative AI for business transformation? add

What challenges do CIOs face when it comes to implementing Gen AI and what strategies should they consider in order to successfully navigate this transformation? add

What is the key to succeeding in transforming your business over the next 10 years with Gen AI? add

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Bruno Aziza, IBM

>> Hello and welcome to this CUBE special coverage here in New York City. We are at the IBM Analyst Forum 2024. I'm John Furrier and Dave Vellante. And Dave doing wall-to-wall interviews, talking to the experts at IBM, the leaders as they set the agenda for the next generation. As this gen AI wave comes on, it's going to be super important for companies to figure out how to set up their infrastructure, their data, to bring in those gen AI apps that are going to be coming into the markets. It's a tsunami, it's a Cambrian explosion, and we are here in New York covering it all with a friend of theCUBE, Bruno Aziza, newly appointed group vice president of data analytics and AI at IBM. Great to see you. I think this is like your either 13th or 14th time on theCUBE. You're a CUBE alumni going back to 2012.

Bruno Aziza

>> Yes, thank you.

>> Welcome back to the CUBE. Now as an IBMer executive, how's it feel?

Bruno Aziza

>> Thank you. It feels great. It's always great to talk to you, John. Always have fun. I watch all your shows, so I'm both a participant and a fan of the shows.

>> Well, you are a super fan because you took our last three podcasts and put it in Google Notebook LM and made a podcast about our podcast. I can't wait to listen to that, but I'm super excited for you because we have history. I've followed your career of your journey for over a decade, 12 years now. You've had your finger on the pulse in data for a long, long time. And we go back to the old Hadoop days, which I call kind of the gen one of the big data revolution, which we're now living in now, the Gen AI, which is a total secular shift. It's a game changer in a way we've never seen before since the web, the evolution of the onboarding of analytics to Gen AI is transitioning. And we're just starting to see the adoption of Gen AI. We're expecting massive growth of Gen AI to be pervasive throughout all companies, all businesses, and our lives. So this is a great moment, and you got the domain expertise and the experience. Now you're going to run the ship here at IBM looking at analytics. What's your vision and how do you see this evolving? Because IBM is really well positioned. They have a lot of data, a lot of applications, a lot of big customers.

Bruno Aziza

>> So first of all, you're right, I've had a journey through small, medium and large organizations. You followed me through the four startups that I did when I was at Microsoft and most recently at Google. And I've been able to kind of see the evolution of what's going on in the world of data. IBM has a huge opportunity on multiple fronts. I think the first one is obviously the credibility with the enterprise. I think we now kind of passed the first inning on generative AI. We got really a lot of excitement on the consumer side, but we know that the rules of enterprise Gen AI are very different. And so IBM has already the trust of a lot of important players in industry. So I think that's definitely an advantage when you think about using Gen AI as a business transformation option. And then the second bit that I would say that really excites me is the culture of invention. I don't know if you know this, but IBM invented SQL. The first DBMS was from IBM. And so when you bring these two things together in the moment of history that we're in, there are not many players I think that can partner as a business transformation company for these enterprises.

>> And IBM a historic brand, obviously. They basically invented business computing going back to the roots of Tom Watson in the early days, mainframes, and some are comparing the NVIDIA and the clustered systems that are emerging as the mainframe and distributed computing cloud model, which is, if you look at it, these clustered systems is essentially supercomputing for the masses. It's democratizing basically horsepower or computing. That's going to enable a lot of change with data. And again, data's been around, you've seen it. Predictive analytics is not going away, but it is coming into gen AI. How do you see the portfolio? Because this has become a moment in time where I kind of feel... I was talking to Dave about this, it feels like a Steve Jobs moment where he gave that famous speech to the employees and said, "We're going to go back to our old agency. We're going to simplify things. We're going to go back to our roots. We're going to streamline, make it simple, just iMac. And then the iPod, that famous speech, it's on YouTube." And he really was simplifying. He said, "We're too complex." IBM has a similar portfolio where there's so much going on that you really can't include everything. But now with Gen AI, you can abstract away and still have all those great things, but simplification seems to be the theme because people are moving very fast. What's your view?

Bruno Aziza

>> Absolutely. I think if you look at the stat in the market, you'll find that the-six percent of CIOs are dissatisfied with the progress they've made with Gen AI. Interestingly enough, 60% of also of them do not have a consistent strategy for the transformation through Gen AI. So I think there's a correlation between these two numbers. The reality is that the world of the CIO today when it comes to Gen AI is what I call the multi-world. You have to deal with multi-cloud, you have to deal with multiple models of multiple size. So you need to have a small model strategy and a large model strategy. Multi-modal of course is an important dimension, but you also have multi-cloud that you have to think about. You have multi-step because you're going from assistants to agents, multi interfaces. There's chatbots and embedded. And then finally you think about it as a multi-year effort. And so I think when you're a CIO and you're thinking about that, it's very, very complex. And the opportunity for us is to really simplify across the stack. First, you're going to manage and work with data, then you're probably going to orchestrate and then you're probably going to activate it. And when you think about doing that for the enterprise, I think what matters the most is are you working with a partner that doesn't really have a horse in the race of which cloud you're going to choose or which model you're going to choose, but is really aligned with your goals as a CIO? And how do we succeed over the next 10 years in transforming your business with Gen AI, which is very close to the identity of what IBM has done for decades at this point.

>> We've been talking about super cloud for two years, and the whole idea of super cloud was it's multi cloud, but it was a lot of other multi going on. You mentioned multimodal, which we know we hear that all the time. Computer vision, text, the foundation models and not just language, it's a lot of images. So multi mode modal. Multi model is interesting because that brings up the whole power law, models will work together with each other. Multi cloud, that's the data center in the cloud, if you will. That's the hardware. As I say, I think AJ Patella actually said that once on the queue, but the cloud is just hardware. I'm a middleware guy. So multi cloud is super important. And when you start to integrate things, multi-step is new. You said multi-step. Rob Thomas mentioned that here at the analyst forum. He says that's where the value is. And of course multi-year, it's a horizons journey. 10, 20-year generational shift. Talk about multi-step. How do you see that happening? Because I think that's where the dots connect with agents because there's reasoning involved. There's self-learning, so healing. These are concepts. Take us through, unpack multi-step.

Bruno Aziza

>> So the idea for me, what I've observed with customers for many years is that what we've been working on has really been assistance locked in an application context. So it might be a copilot for your business intelligence application or copilot for your dev environment. But the reality is that what you're trying to do is you're trying to build a world where agents are going to be, one, collaborative. So imagine a world where now as an agent above a layer of a cloud or above a layer of an application, I'm able to have awareness of other agents that can pull in. And then the second aspect is I'm going to be action oriented or outcome oriented. The great philosophy I like is Clay Christianson's Jobs to be Done is nobody wakes up with a database problem. We all wake up with a business growth issue. And so if I can now interface with an agentic framework that says how do I solve for business growth? Now you can imagine a team of agents that are assembling around your issue. Analytics is a great examples because we have a huge issue today with business analytics or business analyst talent. Now you can use agents to collaborate with other agents, agents to collaborate with other humans across domains and effectively solve beyond just creating a dashboard, solve to create a business outcome.

>> You and I were talking last night when we saw each other for the first time, now that you're at IBM, and we talked about the analytics market. You mentioned analytics engine as a way to look at how analytics has life of its own, if you will. It's my word not yours, but take me through that vision. I think this is relevant around these action. And when you bring in multi-step and multimodal, it's implied that now you have models talking to each other. So some sort of trust delegation relationship there, that's going to provide some value. That's got to be fed to a process that has certain steps and domain expertise that's going to be automated and entrusted.

Bruno Aziza

>> Yeah.

>> What is that connect? What does that enable? How do I figure out all these dashboards? How do have this analytics engine? Explain what you meant by that?

Bruno Aziza

>> The currency and the wealth of data is trust. I think we all know that. I think that some of the adoption issues we've had in the industry is not because we haven't had great cloud technology or great database technology, is because ultimately we're unable to trust the data. So I think when I think about AI and big data coming together, we now have a huge opportunity to use AI on data so you can get better data and then use data for better AI. You need to be able to orchestrate that. You need to be able to govern that. And there aren't a lot of companies that can do that across multiple clouds. I mean, a simple example might be today, if I want to build a workflow, who's going to tell you which is the best infrastructure to run that on? What is your left to right view of the various agents you could be using? And by the way, it could be agents we build. It could be your agents, it could be agents from other companies. And so this idea of having a framework for the hybrid world, which is this multi dimension that we talked about, and a model for openness is going to bring the most value to the CIOs who today have not standardized on one cloud or one application. They have the multiplicity of that environment and they need someone to come on top to help them orchestrate all of that. If you think about music, you got the guitar player and the drummer, they all have great value by themselves, but someone needs to be orchestrating that. And I think we have a great role to play here.

>> And any given time, some data will get the guitar solo.

Bruno Aziza

>> That's right.

>> Some will get the drum solo. So harmony is really critical in music and it feeds off each other. They reinforce and share.

Bruno Aziza

>> Yeah, I think that's exactly the idea. I mean, the CIO today needs more calm to be brought to their world because over the last 10 years, we've certainly have a lot of innovation around networking and compute and of course databases. And now every morning that you wake up as a CIO, you're getting hit with another model and another benchmark. So how do you build this strategy that really we know is going to be a ten-year strategy? The internet is a great example for us. When we got the internet, everybody got excited. Well, it took us 10 years for 70% of companies to get a website that actually is functioning. I think we got to approach it this way because if you don't, you're going to miss the opportunity to truly become competitive in the market. And so that's what I'm excited to do and that's why I'm here because when I was kind of looking at where's the future taking us, there aren't a lot of organizations that have the credibility and the ability to execute against that. There's a lot of organizations that have great technology and I love every single one of them I've worked at. And now we're in the stage where, okay, let's take that to the next level. Let's orchestrate for a better business process, better automation, better governance, so we can drive the industry into most trusted and reliable environment.

>> Let's talk about your job here in New York. Rob Thomas, who we've known for many as decade as well, been with IBM for 25 years. He said on stage something that kind of is in consistent with what I've been hearing here at IBM and coming from Rob's, it's interesting, although he's an executive, a little bit biased, but I think he was totally sincere. He said, "In my 25 years at IBM, I've never seen more innovative time for IBM." He's never said, he's never clearly rolled out that cliche in the past, we're more innovative. He actually meant it. And I hear that a lot from other IBM-ers because the Gen AI is a renaissance opportunity, especially for historic brands like IBM and even companies like Salesforce who are not pivoting, but they're abstracting away all the... They have the data. IBM's got data, they got plenty of technology, they've got a lot of applications, they got a lot of customers. So you see IBM's got a nice spring to their step right now, so you can see it in how their strategy is. So what attracted you to IBM? Because you're at Google Ventures before that, Google Cloud, BigQuery, you had your hands on some pretty cool things. What was it about IBM and what are you going to be working on?

Bruno Aziza

>> So first of all, I had a very fun time at Google. Just like you said, within three years, we became a top five software organization with Google Cloud. So it was really incredible acceleration, and I had a ton of fun, lots of friends that I have there. In fact, one of my friends is Ritika, right? So one of the opportunity to come and work with Ritika, who she and I worked together at Google was a huge factor. The other factor was the moment in time in our history, I really believe that we're now a pivotal point where you need, I don't want to say mature thinking, but that's really I think where we can help CIOs and I want to put myself in a position where I can be the most helpful. And then third, I think just like Rob explained, we are at a unique point where this pace of innovation is getting really interesting, I think because there's been a lot of noise in the consumer gen AI world, but there hasn't been a lot of structure for enterprise gen.ai, and I think IBM is a great opportunity. Let me give an example. You asked me what am I going to work on? So I'm working on outbound product management across BI, AI, and data. So really across the portfolio wise, Google, I was really focused on business analytics. And if I take the business intelligence world, for instance, what have we seen? We're in the third generation. The first generation was how do I understand the data itself and how do I make it accessible to people through SQL? It was really, really painful. I worked at business subjects back then and it was a lot of fun, but it was really hard. Then we moved to self-service BI. I was at Microsoft and we had an incredible ride. And now Power BI is one of the top BI tools out there, but if you look at why is it that people are not adopting dashboards as much as they should is this idea of trust. Gen AI or Gen BI enables us now to actually solve for the job. Nobody woke up with a need to have a dashboard. They had a question to drive their business. And the best way we had found to solve that problem was with the dashboard. Well, with Gen AI, now you might not need the dashboards. You might just need a really good agent, if you will, that you're going to trust, where you're going to be able to explain how the decision was made. And it can be kind of your business analytics assistant, so you can get to the resolution, which is ultimately, where do I need to invest? Why is this store not generating this much revenue and so forth, which dashboards are very inefficient at. So the opportunity is really standing on that technology to take it to the next level.

>> Yeah, it's interesting. IBM's got a lot of customers that have a lot of data. So the aperture of their opportunities increase to enable that. You mentioned assistants and agents. What's the difference in your mind between an assistant and an agent?

Bruno Aziza

>> An assistant is something that is helping you within a particular application or maybe within a particular workflow. The best examples that I think of, again, if I take business intelligence, it's something that's in on the side and is explaining maybe what the dashboard is saying or maybe answering a question that you can't really answer. It's really application centric. It's in its silo. It's an island essentially. That's the best way I can explain it. An agent is that plus the ability to collaborate. And so I think about it as maybe it's across the main view. And I think about it as it's a collaborative framework that allows us to get more. So the example of music that we talked about earlier is a great example. You have the guitar player, the drummer, they each have great value in their domain, but when you bring them together, you create incredible music. And so that's I think the difference for the agent framework. Now, a big part of agents, of course, they got to be open. They got to be governed. Because they're cross application, cross cloud, cross domain, they got to be really good at working together. And so that is really hard when you're a vendor that's specialized in building an assistant and application. How are you going to make them collaborate with others? And we know that the best work comes through collaboration. So if we can get agents to collaborate, agents to collaborate with agents and agents to collaborate with humans effectively in a governed manner, you're going to get a ton of value.

>> Ritika was on stage earlier. I saw her conversations. I asked her a question around the shared data layer, which is a big part of the software portfolio. I want to get your thoughts on this because as the world evolves, just seeing all the work being done at the infrastructure level, better chips are coming out, custom silicon, Kubernetes is getting solid. That whole cloud-native world is locking in their piece. And then when that's going to finish soon, now your team and I call the data industry is preparing that next wave, which is coming right after the infrastructure, natural progression, of course, agentic right around the corner and all those agentic systems, as Rob Thomas calls it, think of it like middleware, which is a good way to think about it. So you got right around the corner is the world's going to spin in your direction, so you got to prepare. What are you preparing? What is Ritika and the team looking at because data warehouses were siloed and they were connected and then just moved to the cloud with snowflake and data bricks. Now they're data lakes, but that's not the end game. New things are going to emerge like observability, data resilience, the things around security. So data's going to move into things with networking construct. We heard Dario Gill say we're networking the connections and encoding the data. I mean, that's like a networking concept meets data. So you have this whole layer change. What's your reaction to that? How would you explain that to a normal person as you prepare for that next sequence of events coming, which is the data action?

Bruno Aziza

>> So you've known that I've been a product guy from day one, and the role in product management is start from the customer out. And so I'm very excited because I get to meet with a lot of customers and it's the identification of the jobs they're trying to get done that's going to guide us. I know Steve Jobs used to say that customers don't know what they want. I think customers are really good at defining the problems that they have. And it's up to me as the product person to design a system around the solution for that. And so that's the blueprint that I'm using. I use that at Oracle, I use that at Microsoft, I use that at Google as well is build a system that is very customer-centric, where we truly understand what customers are trying to solve, build incredible empathy with them. The incredible opportunity at IBM is we can have a wide aperture on that, because like I said, we're open and hybrid, so I can have a huge span, if you will, of what I can offer for the clients. And then we have incredible opportunities through IBM Consulting. There's incredible research innovation in the research organization. And so I'm really excited to kind of co-innovate with the customers, but it will start with what are the jobs that they're trying to execute on? And like you said, we have some of the largest brands, the most important brands using our technology already. So we have a lot of data points to be able to design the future with them.

>> Yeah, it is funny you mentioned the Steve Jobs quote. I think he was referring to Blackberry when he built the iPhone, but I think the job's point is about breakthroughs. And Rob Thomas addressed that. There are breakthroughs coming, we just don't know where they are yet. I think that's important because there's a lot of pragmatic housekeeping you could do right now to prepare, but also there's real use cases for Gen AI. I mean, JIRA tickets are being eliminated. Heard ServiceNow tickets almost going to zero. There's a lot of enterprise data out there.

Bruno Aziza

>> Yeah. Well, so interesting you say about the use case because there's a lot of research around that. I think any CIO listening to us will easily go to their business counterparts and say, what are the top use cases you have? And they're going to come up with hundreds of use cases. But I think what research has shown is this 80% of the value actually comes from three or four key use cases. And where are they? Well, the first one is going to be employee productivity. I mean, we have a great internal example with using Gen AI for HR, for ourselves. Content creation. So I think about content creation for marketers, of course, but development is the type of content creation when you're creating code. And so that's another one. Customer interfacing. So creating compelling customer experiences where you kind of change the relationship and you delight your customers. It's very true in financial services and retail. And finally, it's all the embed scenarios. I think Rob talked about this idea of we used to do plus AI and now we're in this era where we're used to build from AI up, AI plus. And so that's how I would parse through those. I'd probably parse across not the hundreds, but maybe the three or four organization and think about which are the ones where I'm adding AI to or some that I'm building from AI up. I think this second area, we're going to be surprised with the amount of innovation that customers are going to come up with.

>> Bruno, thank you for coming on this special CUBE.

Bruno Aziza

>> Thank you.

>> Here at the IBM headquarters, I'm John Furrier, Dave Vellante and Dave Booker here in New York City for IBM's Analyst Relations Forum. Thanks for watching.

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