AI for Business: Multiplying the Impact of AI
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Hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio, and IBM. I'm Malcolm Claubo. This season, we're continuing our conversation with new creators, visionaries who are creatively applying technology in business to drive change, but with a focus on the transformative power of artificial intelligence and what it means to leverage AI as a game-changing multiplier for your business.

Our guest today is Karim Youssef, Senior Vice President of Product Management and Growth for IBM Software. Karim's focus at IBM is on product strategy, thinking about the roadmap for IBM software products and how they can deliver effective and compelling customer experiences.

With the current boom in generative AI, Karim's job is to help businesses figure out how they can apply artificial intelligence at scale to help solve big problems and boost productivity at new orders of magnitude. In today's episode, you'll hear Karim explain how AI powered by foundation models can make AI adoption by enterprise businesses even easier.

how generative AI will change the way businesses process data and make decisions, and how these considerations influence the design of Watson X, IBM's next-generation AI and data platform.

Karim spoke with Jacob Goldstein, host of the Pushkin podcast, What's Your Problem? A veteran business journalist, Jacob has reported for The Wall Street Journal, The Miami Herald, and was a longtime host of the NPR program, Planet Money. Okay, let's get to the interview.

I'm Jacob Goldstein. I'm one of the hosts at Pushkin and a correspondent on this show. And I'm delighted to have you here. Can you introduce yourself? Hi, I'm Karim Youssef. I'm the Senior Vice President of Product Management and Growth for IBM Software. You can think of me as the Chief Product Officer for IBM Software. Okay.

Sounds like a big job. We're here today to talk about AI. We've heard really an extraordinary amount in the last few months about ChatGPT, and, you know, particularly in how it's used in the very kind of consumer-facing way. But I'm curious, what is the rise of ChatGPT and, you know, AI more generally? What does it mean for business?

Well, you know, if you kind of step back and think about what really happens, you know, in a business, you're really talking about a set of processes, right? You know, activities that represent what a business needs to get done, whether it's product they produce and then sell or service that they provide.

And inherent to operating the business, I would say, are two very key factors, data, and then the decisions you make around that data. And then actually, lastly, the processes or activities you do in accordance with that decision.

So if you then think about AI as applied to business, right, in that context, well, the first place it often starts is how do you make sense of a lot of the data associated with driving a business? And so AI has always been in my mind at its foremost about gaining insights, then leading to supporting decisions and ultimately ending at

helping to automate the activities that then are executed as a result of those decisions. So that's kind of my simple way of thinking of AI, and we can obviously color in with examples, but that's my simplest way of thinking about AI when you think about it in the business context. Gain insights from masses of data to support decisions and then drive actions. That's a really helpful framework.

And then if we think about sort of what's happening in the world now with enterprise businesses and AI, what are you seeing with enterprise adoption of AI at this moment? So we're really talking about almost a tale of two periods. So let me first of all kind of take you back before the advent of what I would call generative AI and the whole chat GPT to what has been going on in what I would term the realm of more standardized machine learning models.

A lot of what has been going on has been very much in the realms of certain things like anomaly detection or optimization, using machine learning models to do that kind of work. And where might it apply? Well, think of anomaly detection in security software, detecting threats based upon different events flowing through.

Or in enterprise asset management software, monitoring equipment and detecting anomalies within their behavior. Or even in IT automation software, once again, detecting anomalies based upon what's going on with various IT events and then tasks that should occur.

Optimizations often play around in the realm, as you might imagine, to solve problems of resource optimization, whether you think of that in the context of application resource management for IT or in the context of supply chain. These have been very classical applications of machine learning AI to really make sense of the data and provide the basis to drive decisions.

Now, what is characterized by all those examples I've given and the state of the art of that kind of technology has always been it's very task specific. So there was a

air quotes, if I may, kind of limitation in the sense that it had to be very task specific. And so we've seen a lot of broad based adoption within the enterprise. Right. But it's very, very task specific, as you might imagine. Now, what has happened recently and has been brought to the fore?

has been this discussion of generative AI, which is powered by a very specific innovation, this notion of foundation models. And in the simplest way to think about it, it is about training this large model that can then be refined

to various tasks. And the easiest one that everybody recognizes at the moment is the notion of a large language model, a model that has an understanding of a lot of the elements of a language such that it can be refined to a variety of tasks, write an essay, answer a question, sing a song, so on and so forth. I like to liken

the power, if you like, and this will speak to the why everybody's so excited about it, why I would argue at an inflection point. I like to liken it to teaching a child the alphabet. When you teach a child an alphabet, it's a set of letters, right? Let's call that our foundation model. But over time,

That knowledge of the alphabet is tuned to read a book, write an essay, do a composition, create a song, write a poem, write an invoice. You understand what I mean, right? And so from one foundation model, you can support multiple targeted tasks as opposed, sticking with the analogy, to having a model for reading, writing, doing a poem, doing an essay, so on and so forth.

And so in the enterprise context, that means that we're now talking about being able to unlock even additional value at scale because of the nature of foundation models and their appeal to generative use cases. Generative in this case meaning creation of new content. So let's talk about Watson X. IBM recently announced Watson X. Just first of all, what is that? What is Watson X?

Well, WatsonX refers to our, is our brand for our platform, the WatsonX platform, for really taking advantage of generative AI within the enterprise, within business. And so when you begin to think about what does that mean, well, it leads you to the components of WatsonX and to a set of use cases. So let me paint a few quick pictures for you here.

What's Next, first of all, is about enabling our customers to manipulate models against their tasks, manipulate these foundation models against their tasks. Our belief is that the world is a multi-model world.

Right? And especially when you think about it in the context of business, models are going to come from various sources. The ones we supply, the ones out there in open source and so on. But there are activities you need to do around these models to, as I said, apply them to your use case. And we'll talk about use cases in a bit. So, What's Next.ai is that environment, that builder tool, if you like, for being able to do those manipulation of models.

to meet your specific use case. Things that people will recognize in the field, prompt engineering, prompt tuning, fine tuning, those kind of activities, which are all the manipulation of models to meet your use case. The second component is .data. So what's the next .data is essentially a next generation data store is based upon something referred to as an open data lake house architecture that helps to bring together the data that's needed

to actually do the AI. In this case, when you think about manipulating a model, a foundation model, you're generally using some data to prompt it, tune it, train it to your use cases. And so we provide a very open data store that allows all manner of data and formats to be brought through to do that.

And the third component is what's next dot governance. And this is all about the framework and the toolkit required to apply the right governance principles across doing this kind of work. Because when you're deploying

AI within the enterprise, governance is actually important, right? It's critical to understand where is your data coming from? What data did you add in? How is your model performing? Are you able to keep an appropriate audit trail of your activities for your own internal policy and compliance needs or for regulatory needs as well?

So this platform, this system that you're describing, I'm curious, how is it different from the, you know, the generative AI options that, you know, we've all been hearing about sort of in the press? Well, I think it really comes down to the ethos or the principles that, first of all, drive the work that we're doing. The first I would fixate on is being open.

We fundamentally believe that to do this kind of work within the enterprise, you need an open platform that, as I said, is open to all manner of models from all sources. It's one of the reasons why we announced our partnership with Hugging Face, to make sure that our clients can gain access to open source innovation within the platform to do their work. And Hugging Face, to be clear, is sort of the open source AI kind of hub, right?

That's right. That's correct. Yes, it's a marketplace hub for all kind of ecosystem coordinator for open source models. And I believe there's a lot of innovation going on out there. So first of all, open becomes important. The second, targeted.

So our focus is very much on enabling these business use cases, right? And you might say, what kind of use cases are we talking about? I'll give you three very quick ones that our customers are focused on.

A lot of focus around enhancing customer service use cases. Think of this as chatbots or digital assistants that are further trained in more and more information about what the company has to offer or could be internal policies, external policies, so on and so forth. This means a platform that makes it really easy to bring your own data to train and tune the model while protecting your own data.

That's extremely important for the enterprise. Another important use case, seeing a lot of focus on what I would call AI-based orchestration or automation of tasks, whereby I think about an HR professional, as an example, going through a job requisition is able to interact with multiple systems via a very simple chat interface and have work dynamically sequenced to support them in doing their tasks.

That, once again, requires a notion of working with models and technology in a way that in many ways can be unique to how a business wishes to work. And indeed, in various cases, can embody what they consider their secret source or their differentiated advantage. So once again, a platform that recognizes that and is designed for business, that's not the same scope or frame of reference for a consumer platform.

And then we're also seeing a lot of work around code generation, application modernization, and people enhancing their skills. So targeted becomes really important. I mentioned open, and I mentioned targeted. Targeted to the business, to the use cases that they need to do. Underpinning that then is trusted. So everything I gave you in those targeted use cases talk about handling enterprise, proprietary, and specific data.

We are trusted in this regard, right? We have been serving the business for many, many a year. And we are designing our platform and even our principles and way of operating to recognize and enable that, both in terms of the work we do around the governance framework and transparency that you're able to gain and apply, but

even in the way we allow our platform to be deployed in multiple locations or footprints, consumed as a service on a hyperscaler, running your own private footprint on-prem or your cloud footprint, all of these need to be brought together to meet the needs of an actual enterprise business. My last comment is where I think we're fundamentally differentiated is we're really about empowering our customers to

to take advantage of AI to unleash the intelligence, capabilities, productivity of their own business. This isn't about, "Oh, we've established a bunch of APIs that you can ask questions."

This is about how do you craft what you need for your business to deliver differentiated value to your customers, shareholders, employees with all the appropriate protections as well. And so there's a lot of focus on what we've done with the platform and the tool set to enable that, to enable what we like to call AI value creators, not just consumers of AI value. When you were talking about

basically enterprise adoption of AI. You use the word trust. And I'm curious, you know, what does trust mean in the context of AI and the enterprise? I would kind of deconstruct trust along these key avenues. If AI is about giving you insights to help you support decisions, how do you trust what insights it's providing? What data is

did it use? What did it consider based upon that data that therefore led to the insight provided? Why is this important? Why this notion of trust? Well, one, you're about to make a decision. So you want to understand

the basis for a decision, it's no different than me asking you something and then saying, okay, can you explain your working, right? That would be a notion of trust that we establish and a very natural interaction as humans, right? We do it all the time, right? So there is that element. The other reason why it becomes important, if you're applying AI into business processes and therefore how your business works, you want to make sure that you know what

biases are built in to any decision or not, or if the AI, the model in effect, is drifting away from kind of the parameters that you would want it to remain within, right? Ergo, trust. And so,

In many ways, that's one big aspect of trusting the technology because you're applying it into decisions you need to make every day and you need to know in very simple terms how it works and how it is working. The other element of trust that I think is important in this discussion, who are you getting your AI from? That's very important to us as a company here at IBM, right? Given we serve business,

that trust becomes extremely important. And what are the elements of that trust? What are the customers trying to understand? Well, first and foremost, what's your ethos around AI? We're very clear on the customer's data is their data. When they tune or refine those models to meet their use cases, that is all theirs. And we actually provide the ability for them to do that in very isolated and protected ways as they choose.

And we never use their data without explicit opt-in and permissions, right? Customers might say, oh yeah, use this so that you can make a generally overall better model, but it's full awareness, full transparency. That is important. That's a trust of who you're doing business with. So that's how I think about trust. How do you build systems you trust? And are you working with people you trust?

I find Karim's point about trust when it comes to data to be so important. Because as powerful as AI tools can be, their helpfulness is dependent on how trustworthy the data is. Humans will have to decide if our data, our decision-making, and our AI insights live up to the vision we hope to achieve in business.

As Karim and Jacob continue the conversation, Jacob asks some more practical questions about how businesses can adopt AI into their own processes. Let's listen. How can businesses move toward integrating AI as part of their core business model instead of, you know, sort of as an add-on on the periphery?

It's funny, you know, my simple answer to that is it's actually the simplest thing in the world to do by thinking about your business, thinking about your elements of differentiation, and then thinking about how AI can help you

extend, expand those, right? What do you want to be known for? I picked a very simple use case of customer service interaction. Almost every business needs to do that and wants to do it better. And so it becomes a way to start. But then as you begin to work your way through, you think about various automation of business processes. You think about decisions that need to be made, right? Or how can individuals be helped? How can they be made more productive?

I think always becomes a very important one, right? So, and you can apply this in many contexts. A financial analyst looking at reams of data and trying to derive insights. How do you leverage AI to make that financial analyst even more powerful? And so that's how I advise, you know, people to always look at it. Think about your tasks. Think about your business processes. Think about where help is needed or where new value could be unlocked.

And then you're applying AI as a tool to achieve that end. One of the themes we return to on this show a lot is creativity and the relationship between technology and creativity. And I'm curious how you think that AI can help people be more creative at work.

I think AI can help people be more creative at work by automating the mundane to unlock your mind to be able to focus on higher value. You know, I've used a couple of times, I've talked about deriving insights from data, right, to drive informed decisions.

If you can use AI to gather a lot more insights into one place than you could typically do yourself or more manually, you'd have to like write it down, look at six spreadsheets, copy from here to there, then you actually have more time

to look at that data, digest those insights and think about what do I need to do with these as a business? Which direction do I want to go? I think of it as freeing us up to do more of what we actually as humans do extremely well, which is actually that creative thinking. In very simple terms, why do we use a calculator to do arithmetic?

It's not that we cannot necessarily knock it out ourselves, but if you're trying to balance your checkbook, to use an old phrase, or dare I say... What's a checkbook? What's a checkbook? I felt about that. So let us modernize that. If you're trying to check your expenses for the month and your performance against budget, yes, you could print out all your statements, circle everything, hand add it all up,

Or you could begin to use technology to improve that experience so you can get more time to think about what really am I learning from my spending patterns and what do I want to do about it? It's a very simple personal example, but I think it's fundamentally what we're talking about here. And that's always been, in my mind, the promise of technology, freeing us up to actually apply ourselves to higher value thoughts and higher value problems.

So we've been talking basically about the present so far. And I'm curious, if you think about the future and you think, you know, medium to long term, how do you think AI is going to transform business? And, you know, how can people now, business leaders now, prepare for what's coming? So to an earlier comment I made, I do really think that we are at an inflection point with the

advancement of the technologies of AI, I talked about foundation models, we definitely are at the cusp of being able to address use cases at scale that were more challenging before. And so I do think the future

looks like a lot more generative AI surfacing within the enterprise and within business processes and manifesting in interesting ways. I think it's almost a given that any piece of software, right? Whether you think of it in terms of an application or you think about it in terms of, you know, the interact with the website will have conversational enabled interfaces.

from the analyst saying, "Give me the latest reports for the last three months," you know, typing that or saying it, versus the right-click, file, blah, blah. I think you're going to see that change in interaction to more conversational interaction, I think particularly chat-based. We forget that the graphical user interface is just a metaphor, right? It's not like the way computers work. It's just an interface. And if chat is a better interface, people will use chat.

And I think we're going to see that really explode. And that's powered by a lot of this generative AI work because it becomes for you to feel natural, for it to be as informed, to readily, as I said, link things together and orchestrate. That's a big part. So I think I see that happening and the appropriate or associated productivity unlocks. You begin to see with that will just change what kind of decisions, the ease with which we can make more and more informed business decisions.

And so for me, it's that rolling out at scale, touching everything, procurement, HR. Think about the advent of the spreadsheet and how many different roles it just ended up touching. And everybody can use or does use a spreadsheet in business in some shape, size or form. So I think of this as AI at scale. And so what it therefore means from, as you said, getting prepared,

Well, it's all about gaining, first of all, the right understanding of the technologies and part of what a lot we'll be talking about. Necessary ingredients begin to be, well, where do I want to apply it first? What data do I need to bring together to readily support that? What unlocks what new value? And I think it's going to be like this rollout, right? You're going to start with this project and then there's another project. And very soon it will be ubiquitous

in the way it supports the work we need to do, that it will just speak to a new way of us working. That is, when you now look back, we'll be pretty different from how we work today. You see the seeds today, but I would argue, think of that now like fully bloomed. It's a forest, not a flower bed, you know? Yeah, yeah, yeah. Great.

One other sort of loose thread I want to return to, and that's governance, right? You talked about governance. And maybe just to help sort of set the table, like you mentioned it in a broad way, but narrowly, what does governance mean in the context of IBM's work on enterprise AI? I think as the word tries to suggest, it is about having the way to

govern one's activities in this realm, which really speaks to policies, rules, and frameworks within which to understand all of that. Now, before we dive in the direction of regulation, which is where people often go, policies can be all internal. So,

Think about it this way. If I say to you, when I build AI, I do not use my customers' data is their customers' data, then from a governance perspective, I need processes that ensure I know what data I'm using and I can prove to myself, just first of all internally, forget about anybody else, that I'm actually adhering to the policies I've laid out.

That, in my mind, is a lot of what governance is about. And in the context of AI, it always tends to, I think, structure around three key areas. Data, where did it come from? And what did I do with it? And how did I apply it? And where did I use it? And then usage. What do I expect this model to do? Is this model still performing the way I think it should be performing? What are my processes to address whether the answer to that question is yes or no?

and manage that through. And then importantly, so this is then to bridge to regulation, if you take a look at what's going on in the world of AI regulation, and our point of view on this, by the way, is that you actually regulate the use cases, not the technology. Then from a governance perspective, how are you able to clearly understand, track, and account

for what use cases you are leveraging AI for, and then back to my earlier comments, how that AI is performing. And when you talk about governance, how do you make sure that you have the governance you need without inhibiting innovation? I think what is key, and this is a key design point for what we're doing with Watson X, is how you make governance accessible.

seamless institute versus another activity that you do, right? And so our goal is to try and drive that kind of seamless interactions of a value add in terms of governance so that when, oh, let's pull through the history, right, of everything we've done here, what prompts we've created or what data we've used, it's kind of already there, right?

Right. And so you can feel free to be innovating and testing out your different prompts and all that stuff or bring it in your data sets without saying, oh, before I do that, I need to make sure I run this checker. And now you can kind of bring it in systems kind of automatically categorize it. And then you can go in and later verify, validate or explore, say, I'm no longer going to take this path based upon these facts. I think the more we can make it more of a natural extension.

of the activities that need to be done, the more we can make it then just a part of what needs to be done. And as to your point, gain our governance needs or support the governance needs of our customers without stifling the innovation of the individuals at the glass trying to think through, iteratively think through new value ways to do work. Excellent.

Let me ask you, are there things I didn't ask you that I should? Are there things you want to talk about that we didn't talk about? I think we covered quite a lot, truth be told. No, I think we covered the basis there. Earlier, Karim mentioned that we are at an inflection point in AI technology. Implementing AI in business will get easier, and AI platforms like WatsonX can empower even the largest enterprise businesses to reinvent the way they run.

As Karim said, in the same way the spreadsheet took over business operations, the adoption of AI at enterprise scale could be just as ubiquitous. It's not an overstatement to say that a new era of work may be upon us. I'm Malcolm Gladwell. This is a paid advertisement from IBM. Smart Talks with IBM is produced by Matt Romano, David Jha, Nisha Venkat, and Royston Berserve with Jacob Goldstein.

We're edited by Lydia Jean Cott. Our engineers are Jason Gambrell, Sarah Bruguere, and Ben Talladay. Theme song by Gramascope. Special thanks to Carly Migliore, Andy Kelly, Kathy Callahan, and 8Bar and the 8Bar and IBM teams, as well as the Pushkin Marketing Team. Smart Talks with IBM is a production of Pushkin Industries and Ruby Studio at iHeartMedia.

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