Scaling Your Business with AI | The Six Five Summit

Welcome back to the six five Summit 2024. We are broadcasting live at IBM think 2024 in Boston. The summit, as you know, is all about AI in 2023.

It was all about the build out, the infrastructure, the tools, and sure as an industry we're building those tools out as we speak. But 2024 is the year where enterprises and even consumers are starting to get value. Daniel, how you doing my friend?

Yeah, pat, you really hit it on the head. 23. We saw this big acceleration following the advent of chat GPT.

It wasn't new. AI wasn't new, it wasn't like all of a sudden hearing a thing, but it was that killer workload that everybody could really understand. And now it was about how do we extrapolate value from that workload and start to drive to enterprises to drive growth, productivity, and excitement.

And that is really what, not only all the events we've been at this year have been all about Pat, but what the six five summit this year is all about. And Dan, who was the company that went first to GA with an end-to-end enterprise AI platform. It was IBM.

Exactly. And with that, to talk through that, Kareem from IBM. How are you doing my friend?

I'm Great guys. Thanks. Thanks for having me.

It's great to be Here back for a second year at the summit. We are so excited. And by the way, are you just gonna just do a victory lap the whole time here?

Uh, No. Not quite. We got a lot of work to do, but it's good to be making progress.

It's good to be seeing some traction, so Yeah, For sure. Yeah, so we sat down a year ago actually, the, the studio wasn't quite this cool, but we had a great conversation actually for our six five summit. That's right.

And we're bringing you back again because it's been a massive year. In fact in the a MA we spoke to Arvin, I hit him up on the year in review. Yep.

4 trillion of productivity that he keeps talking about. But you've got growth, you've got product, you've got the responsibility to bring this story to life, right. For IBM and its customers.

Talk a little bit about how that's moved throughout the last 12 months. Well, you know, it really anchors in first and foremost on what I would call use cases, right? So the whole journey began, as you recall when we were talking last year, about how was gene AI gonna be brought for business to the enterprise.

And as you looked over the year that went past, I would say three things very clearly emerged customer service as a great starting point for thinking about bringing gene AI to bear was emerged this notion of digital labor tied in with business process automation, right? Integrating gene AI into actually doing work was the next. And then a little unexpected for me only because I thought developers would be like, stay away from me with all this ai, right?

Gen AI for code development really exploded as a real way to unlock productivity benefit. And so that really shaped kind of the portfolio evolution of the last year and the traction we've been seeing. So a question, you know, you talked a little bit about the use cases.

Can we do the double click on that? Yeah. And maybe talk about some of the outcomes that, that, that people like to see.

And if you wanna cite outcomes that your clients have had, that would be great too. Well, I mean, look, if you think about outcomes, there's the reason why everybody talks about productivity. 'cause it's very easy to begin to think in terms of time saved and then based upon time saved things either more done or avoided.

Right? So let's take customer service as an example. A lot of the business cases center on the ability to serve without having to escalate to a human.

Yes. That has got very clear benefits in terms of time computation, right? It also, um, when you think more along that notions of avoidance, think about an OX HR system.

Self-service is very empowering. And the more you can enable people to do stuff themselves, the more you drive that kind of value. We saw it extensively at IBM.

Yeah. We saw it at many of the customers who you saw on stage, uh, talking, uh, over the last three days. Elephants Health as an example, right?

Uh, Don and Bradstreet was just another one, just thinking immediately off the top of my head, when you think about code as another case, it speaks to this notion of productivity, but also expertise and skills enablement. Yeah. We introduced the product, uh, what's next?

Code assistant for Z for example. A lot of our customers love using genai there to understand the COBOL applications, right? Just being able to gain their understanding and, and bring that kind of knowledge to the fore before you take action becomes valuable.

So that's kind of how it's beginning to manifest in real tangible terms. That's great. So, you know, I mentioned in the, in the, in the, you know, preamble here about CHE EBT, but let's face it, we've moved well beyond that being the only model, the only consideration it, it set the stage, it got people going.

But now we're seeing, you know, private AI proliferate, you know, we're seeing hybrid architecture stuff that actually you and your and IBM bet on very early on. Yeah. We're also seeing this kind of range of model sizes, you know?

That's right. We've got challenges with power, we've got challenges with total amounts of data that are available to, to be used. What are you sort of seeing as it as it relates to the model development and data used to create 'em?

Yeah. Well look, as you well pointed out the, the, the, the, it our, our thesis was proven out that targeted models would begin to rule the day. And also that price performance of models in terms of influencing costs and all that would become more and more critically important, right?

As people looked at size of models that they needed to deploy. But I think what really became the key on lock from all of that was this focus on what, how do models get better, right? And how can you source more contributions to improve a model?

And that led us to the instruct lab technique, which we then open sourced via Red Hat along with key models that we have our granite models, one for English language based LLM, and then a whole host for, uh, code-based models to really begin to solicit that open source involvement. And I wanna stress a very important point when I talk about that ability to contribute. Before instruct lab was put out there, the state of the art was fork a model and do something with it, right?

And so you take, for example, a llama model and you've got a gazillion variations of llama, but no way to bring all of that together into a single model. And and When you say fork, just for everyone out there, you basically mean like a save as in a document. Thank you.

That's the exact Point. Save as, so you take the one save as make changes. And so you've got all these copies, No version control, no, no version control, nothing, no TTQM, nothing.

But the instruct lab techniques says you can actually bring together various contributions around skills and data, enhance the model. And very importantly, when you think about enterprise customers in the way we've licensed this, enhance it for private use or enhance the contribute back into the public domain, I think this will unlock a lot of innovation as we go through the next coming year. Yes, Analysts will, we're still kind of peeling the onion back on struct lab, but it seems first of all, very provocative and from what I understand, aligns with a lot of the needs of the enterprise that we talk to who, you know, they, they use, uh, a large language model, a big one, and they're not getting the results even through, let's say, utilizing rag, right?

And then they feel like they're in the, in the position where, hey, I need to create my own proprietary model myself. And they look at the cost and the ability that's right to do. That's, so this is a really interesting way to kind of have your, your cake.

You eat it too. Um, so I gave you kudos upfront about Watson X GA first enterprise platform that went ga. Um, can you talk us through, it's been GA since July.

Yeah. How has it evolved since then? So I think if you, you remember when we ga the platform, there were three core components.

data, the data lakehouse and DOT governance, which was for doing governance. All of them have, you know, come through a number of terms of the crank over the course of the year with some key announcements being made. ai side, it's been all about how do we make it easier for these model outputs to be embedded into applications, right?

We already started with this notion of providing multi-choice, right? Access to a lot of different open source models and ours. And that has continued with multi-language models coming in, dedicated models from, um, uh, for Arabic, for example, the alarm model coming in.

And obviously as we were talking about open source models and the like, but the next level is when you look at how people are building applications that leverage these models, there's a lot more that goes into it. Yes. A lot more considerations.

And how do you do that at scale? So looking at all the various frameworks and how do you bring that together in an enterprise context? That's been a key element there.

data, it's all about performance, right? Okay. It already was an open data store, open fabrics, but it was all about, um, open formats rather, how do we up the performance bringing in the new Presto engine and really giving a price performance element that rarely syncs, and then doing some what I call recursive embed gen ai there to allow the data and the metadata to be better understood for those working with that.

And the last, but not the least on governance, we already did had what we're doing with monitoring, right? And all of that. And we had started down the path of processes with taking things like various regulations and creating regulatory pacs, but the next key unlock was opening that up to support models regardless of where they run, right?

So whether they're running on AWS SageMaker like we demonstrated this week and showed or anywhere, we can now use what's next governance to bring that kind of governance framework to it to bear as well. That's Great. And there's gonna be a ton of focus on governance, tons of focus on compliance and safety of AI in the coming months.

Uh, you know, I, I recently did a, you know, I did a segment. We were talking about privacy and someone's voice was being used. We've got licensing issues in use and every enterprise is creating this new sort of unmeasurable, immeasurable risk when they're trying to move this quickly, but not being quite sure how models are trained.

And so this government governance component is incredibly important. Another thing that IBM has been very focused on, Kareem has been around these alliances. You know, you do the AI Alliance, you are working closely with companies like Meta, who's been very focused on open source.

True, of course, IBM owns Red Hat. But Red Hat operates very much in parallel and has a massive developer in open source community. Um, this has been an enabler of so much progress for you, but can you talk a little bit about sort of how ecosystem, how partnerships and these alliances are really shifting and how it's worked over the past year?

Well, Look, it's, as you know, ecosystem has always been important to us and this kind of open mentality. And it's really about bringing together sets of folks who want to collaborate right? Together around ideas and moving forward the state of the art.

You've heard me say before, technology is a means to an end, right? There's an end our clients are actually trying to achieve in terms of their businesses and what they're trying to do. So yes, you mentioned correctly strong relationships with meta, strong relationships with Mistral as well as many others, uh, opening up to, uh, various regional, uh, concerns as well.

But take for example, your point around Red Hat, right? That's also about how do you lower the bar of entry and hit those hardcore developers who are operating at that level. That's why we introduced rail ai, which really is on the back of Red Hat Linux bringing in the in instruct lab and, um, whatchamacallit open models so that you can get very small developer laptop footprints, right?

That folks can start working on. That allows us to create this value chain from the very beginning there all the way up to enterprise level, you know, tools with more, more in them with what's next. So it's really critical.

The last element, I'll say on ecosystem not to be forgotten, is don't also remember that we've been working with so many ecosystem partners to embed what's next in their solutions. We talked about, uh, you know, Adobe, uh, obviously we talked about SAP that's right before Airtable was on the on stage, um, earlier this week, right? We've been talking about all these folks who are helping us to get better, right?

And really generate this innovation, you know, this ecosystem of innovation to ensure we're delivering real value that matters. Yeah. On the ecosystem side, I, I have to admit, so I've been tracking IBM for almost 35 years, and I feel like this is a great example, the ecosystem play of what I consider the new, the new IBM.

And it's beneficial because addressing clients' needs really take, takes a village, not one single company Exactly. Uh, can do it either horizontally, vertically, uh, at a, in a certain country. And it's just great to see you do this.

So second year in the six five summit, really appreciate, uh, your participation. We've talked a little bit about, uh, what you've done since May. Uh, we've talked about the updates that you've had since then.

Talked a little bit about what you announced at IBM, think. Let's talk about the future a little bit. Where does this go next year when hopefully graciously come on the show again and represent the summit Yeah.

In 25, what will, what do you want to have accomplished? So I look, I think of three lanes of activity in the context of the core platform is continuing to do the work that helps to accelerate leveraging large language models within the applications, right? Within agents, bringing that more and more to the enterprise, right?

And I think that's a, a theme that we've got, you know, quite a bit of way to run with, right? Right. To strengthen the middle lane for me is all about the notion of AI assistance and making it easier to build AI assistance, to integrate AI assistance that are tailored, automate critical process and integrate into the enterprise.

Our lead offering for that is the what's next orchestrate offering, right? In terms of that build, but also then building and delivering high value assistance, right? To target very specific domains.

You'll still see more on that theme. You saw, as I said, the what's next assistant for Z, um, what's next? Code assistant and that family expanding, right?

Right. Becomes critical. We just brought in yet another member of that family around enterprise Java applications.

So you'll see a lot more activity on the assistance. And then the last lane, not the least, but not to be missed, the ongoing embedding of these AI features into what I would call products that people just use from us in various domains, right? Think about what we showed, um, you know, what's next Assistance embedded into planning analytics or aptio or Maximo or Guardium, right?

Continue to strengthen and broaden that out. And that's really important because for many customers that will also be their first taste Sure. Of gen AI at scale Sure.

As their interacting with these products and getting differentiated value from us. And that's why we have to be really targeted there to make sure it's not just, Hey, here's another chat bot for help. Exactly.

It is embedding assistance that brings real value to the work they're doing, those applications. It's Exciting. A lot of work cut out for you this year in your team.

A lot of work cut out for us and we're, we're really looking forward to it. And you know, um, on a final footnote on that, this all leads to when you think about that last year of, uh, of applications, keep paying attention to what we're doing in AI enabled automation, especially around IT operations. Okay.

We launched IBM concert, which is purely a Gen I native application that is able to synthesize data from the entire IT environment, give a real view on what's going on with applications, and drive meaningful value on how they get managed. More of that too, That's, you want it to be scalable, you want it to be turnkey, you want it to be available, you want it to be responsible. Next year, you'll bring a star to the show.

Kareem, thank you so much for joining us here on the six five. We appreciate you joining the summit this year. Thank You for having me.

Cheers. Thank you. Alright everybody, we are here at the six five Summit 2024, brought to you here at IBM think 2024.

We appreciate you tuning in. Stay with us for all our coverage. We'll see you soon.