Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. This season,
Speaker 1: we're continuing our conversation with new creators visionaries who are
Speaker 1: creatively applying technology in business to drive change, but with
Speaker 1: a focus on the transformative power of artificial intelligence and
Speaker 1: what it means to leverage AI as a game changing
Speaker 1: multiplier for your business. Our guest today is Kareem Yousef,
Speaker 1: Senior Vice President of Product Management and Growth for IBM Software.
Speaker 1: Kareem's focus at IBM is on product strategy, thinking about
Speaker 1: the roadmap for IBM Software products and how they can
Speaker 1: deliver effective and compelling customer experiences with the current boom
Speaker 1: and generative AI. Kareem's job is to help businesses figure
Speaker 1: out how they can apply artificial intelligence at scale to
Speaker 1: help solve big problems and boo mose productivity at new
Speaker 1: orders of magnitude. In today's episode, you'll hear Kareem explain
Speaker 1: how AI powered by foundation models can make AI adoption
Speaker 1: by enterprise businesses even easier, how generative AI will change
Speaker 1: the way businesses process data and make decisions, and how
Speaker 1: these considerations influence the design of Watson X, IBM's next
Speaker 1: generation AI and data platform. Kareem spoke with Jacob Goldstein,
Speaker 1: host of the Pushkin podcast What's Your Problem. A veteran
Speaker 1: business journalist, Jacob has reported for The Wall Street Journal,
Speaker 1: the Miami Herald, and was a longtime host of the
Speaker 1: NPR program Planet Money. Okay, let's get to the interview.
Speaker 2: I'm Jacob Goldstein. I'm one of the hosts at Pushkin
Speaker 2: and a correspondent on this show, and I'm delighted to
Speaker 2: have you here. Can you introduce yourself?
Speaker 3: Ah?
Speaker 4: Hi, I'm Kareem Yusuf. I'm the senior vice president of
Speaker 4: Product Management and Growth for IBM Software. You can think
Speaker 4: of me as the chief product officer for IBM Software.
Speaker 2: Okay, sounds like a big job. We're here today to
Speaker 2: talk about AI. We've heard really an extraordinary amount in
Speaker 2: the last few months about chat GPT, and you know,
Speaker 2: particularly in how it's used in the very kind of
Speaker 2: consumer facing way. But I'm curious what is the rise
Speaker 2: of chat GPT and you know, AI more generally, what
Speaker 2: does it mean for business?
Speaker 4: Well, you know, it's if you kind of step back
Speaker 4: and think about what really happens. You know, in a business,
Speaker 4: you're really talking about a set of processes, right, you know,
Speaker 4: activities that represent what a business needs to get done,
Speaker 4: whether it's product they produce and then sell or service
Speaker 4: that they provide, and inherent to operating the business, I
Speaker 4: would say, are two very key factors. Data and then
Speaker 4: the decisions you make around that data, and then actually
Speaker 4: lastly the processes or activities you do in accordance with
Speaker 4: that decision. So if you then think about AI as
Speaker 4: applied to business right in that context, well, the first
Speaker 4: place it often starts is how do you make sense
Speaker 4: of a lot of the data associated with driving the business?
Speaker 4: And so AI has always been, in my mind at
Speaker 4: its foremost about gaining insights then lead in to supporting decisions,
Speaker 4: and ultimately ending at helping to automate the activities that
Speaker 4: then are executed as a result of those decisions. So
Speaker 4: that's kind of my simple way of thinking of AI,
Speaker 4: and we can obviously coloring with examples, but that's my
Speaker 4: simplest way of thinking about AI. When you think about
Speaker 4: in the business context, gain insights from masses of data
Speaker 4: to support decisions and then drive.
Speaker 2: Actions, that's a really helpful framework. And then if we
Speaker 2: think about sort of what's happening in the world now
Speaker 2: with you know, enterprise businesses NAI, what are you seeing
Speaker 2: with enterprise adoption of AI at this moment?
Speaker 4: So we're really talking about almost a tale of two periods.
Speaker 4: So let me, first of all kind of take you
Speaker 4: back before the advent of what I would call generative
Speaker 4: AI and the whole chat GPT. So what has been
Speaker 4: going on in what I would term the realm of
Speaker 4: more standardized machine learning models. A lot of what has
Speaker 4: been going on has been very much in the realms
Speaker 4: of certain things like anomaly detection or optimization, right, using
Speaker 4: machine learning models to do that kind of work, and
Speaker 4: where might it apply well, think of anomaly detection in
Speaker 4: security software right detecting threats based upon different events flowing
Speaker 4: through or in enterprise asset management software monitoring equipment and
Speaker 4: detecting anomalies within their behavior, or even in IT automation
Speaker 4: software once again detecting anomalies based upon what's going on
Speaker 4: with various IT events, and then tasks that should occur.
Speaker 4: Optimizations often play around in the realm, as you might imagine,
Speaker 4: to solve problems of resource optimization. Whether you think of
Speaker 4: that in the context of application resource management for it,
Speaker 4: or in the context of supply chain. These have been
Speaker 4: very classical applications of machine learning AI to really make
Speaker 4: sense of the data and provide a basis to drive decisions. Now,
Speaker 4: what is characterized by all those examples have given and
Speaker 4: the state of the art of that kind of technology
Speaker 4: has always been it's very task specific. So there was
Speaker 4: a air quotes, if I may, kind of limitation in
Speaker 4: the sense that the tak it had to be very
Speaker 4: task specific. And so we've seen a lot of broad
Speaker 4: based adoption within the enterprise, right, but it's very very
Speaker 4: task specific, as you might imagine.
Speaker 3: Now, what has happened recently.
Speaker 4: And has been brought to the four has been this
Speaker 4: discussion of generative AI, which is powered by a very
Speaker 4: specific innovation, this notion of foundation models. And in the
Speaker 4: simplest way to think about it, it's about training this
Speaker 4: large model that can then be refined to various tasks.
Speaker 4: And the easiest one that everybody recognized at the moment
Speaker 4: is the notion of a large language model, a model
Speaker 4: that has an understanding of a lot of the elements
Speaker 4: of a language such that it can be refined to
Speaker 4: a variety of tasks. Write an essay, answer a question,
Speaker 4: singer songs, so on, answers so forth. I like to
Speaker 4: liken the power, if you like, and this will speak
Speaker 4: to the why everybody is so excited about it, why
Speaker 4: I would argue at an inflection point, I like to
Speaker 4: liken it to teaching a child the alphabet. When you
Speaker 4: teach a child an alphabet, it's a set of letters. Right,
Speaker 4: Let's call that our foundation model. But over time that
Speaker 4: knowledge of the alphabet is tuned to read a book,
Speaker 4: write an essay, do a composition, create a song, write
Speaker 4: a poem, write an invoice. You understand what I mean, right,
Speaker 4: And so from one foundation model you can support multiple
Speaker 4: targeted tasks as opposed sticking with the analogy to having
Speaker 4: a model for reading, writing, thinking of doing a poem,
Speaker 4: doing an essay, so on and so forth, and so
Speaker 4: in the enterprise context, that means that we're now talking
Speaker 4: about being able to unlock even additional value at scale
Speaker 4: because of the nation of nature foundation models and their
Speaker 4: appeal to generative use cases.
Speaker 3: Generative in this case meaning creation of new content.
Speaker 2: So let's talk about Watson x. IBM recently announced what's
Speaker 2: an x just first of all, what is that? What
Speaker 2: is what's an X?
Speaker 4: Well, what's an X refers to our is our brand
Speaker 4: for our platform, the WhatsApp platform for really taking advantage
Speaker 4: of generative AI within the enterprise, within business. And so
Speaker 4: when you begin to think about what does that mean while,
Speaker 4: it leads you to the components of what's an X
Speaker 4: and to a set of use cases. So let me
Speaker 4: paint a few quick pictures for you here. What's an
Speaker 4: X first of all, is about enabling our customers to
Speaker 4: manipulate models against their task, manipulate these foundation models against
Speaker 4: their task. Our belief is that the world is a
Speaker 4: multi model world, right, and especially when you think about
Speaker 4: it in the context of business.
Speaker 3: Models are going to.
Speaker 4: Come from various sources, the ones we supply, the ones
Speaker 4: out there in open source, and so of you. But
Speaker 4: there are activities you need to do around these models
Speaker 4: to as I said, apply them to your use case.
Speaker 4: And we'll talk about use cases in a bit. So
Speaker 4: what's next dot AI is that environment that build a
Speaker 4: tool if you like, for being able to do those
Speaker 4: manipulation of models to meet your specific use case. Thinks
Speaker 4: that people will recognize in the field prompt engineering prompt tuning,
Speaker 4: fine tuning, those kinds of activities which are all the manipulation.
Speaker 3: Of models to meet your use case.
Speaker 4: Yeah, the second component is dot data, So what's the
Speaker 4: next Dot data is essentially a next generation data store
Speaker 4: is based upon something referred to as an open data
Speaker 4: lakehouse architecture that helps to bring together the data that's
Speaker 4: needed to actually do the AI. In this case, when
Speaker 4: you think about manipulating a model, a foundation model, you're
Speaker 4: generally using some data to prompt it, tune it, to
Speaker 4: train it to your use cases. And so we provide
Speaker 4: a very open data store that allows all manner of
Speaker 4: data and formats to be brought through to do that.
Speaker 4: And the third component is what's next up governance And
Speaker 4: this is all about the framework and the toolkit required
Speaker 4: to apply the right governance principles across doing this kind
Speaker 4: of work, because when you're deploying AI within the enterprise,
Speaker 4: governance is actually important, right, It's critical to understand why
Speaker 4: is your data coming from? What data did you add in?
Speaker 4: How is your model performing? Are you able to keep
Speaker 4: an appropriate audit trail of your activities for your own
Speaker 4: internal policy and compliance needs or for regulatory needs as well?
Speaker 2: So this platform, the system that you're describing, I'm curious,
Speaker 2: how is it different from the you know, the generitive
Speaker 2: AI options that you know we've all been hearing about
Speaker 2: sort of in the press.
Speaker 4: Well, I think it really comes down to the ethos
Speaker 4: or the principles that first of all drive the work
Speaker 4: that we're doing. The first I would fixate on is
Speaker 4: being open.
Speaker 3: Right.
Speaker 4: We fundamentally believe that do this kind of work within
Speaker 4: the enterprise, you need an open platform that, as I said,
Speaker 4: is open to all manner of models from all sources.
Speaker 4: It's one of the reasons why we announced our partnership
Speaker 4: with hugging Face to make sure that our clients can
Speaker 4: gain access to open source innovation within the platform to
Speaker 4: do their work.
Speaker 2: And hugging Face, to be clear, is sort of the
Speaker 2: open source AI kind of hub.
Speaker 3: That's right, that's correct.
Speaker 4: Yes, it's a marketplace hub for all kind of ecosystem
Speaker 4: coordinator for open source models, and I believe there's a
Speaker 4: lot of innovation going on out there. So first of all,
Speaker 4: open becomes important. The second targeted, So our focus is
Speaker 4: very much on enabling these business use cases, right, And
Speaker 4: you might say, what kind of use cases are we
Speaker 4: talking about I give you three very quick ones that
Speaker 4: with our customers are focused on a lot of focus
Speaker 4: around enhancing customer service use cases. Think of this as
Speaker 4: chatbots or digital assistance that are further trained in more
Speaker 4: and more information about what the company has to offer
Speaker 4: or could be internal policies, external policies, so on and
Speaker 4: so forth. This means a platform that makes it really
Speaker 4: easy to bring your own data to train and tune
Speaker 4: the model while protecting your own data. That's extremely important
Speaker 4: for the enterprise right. Another important use case seeing a
Speaker 4: lot of focused on what I'd call AI based orchestration
Speaker 4: or automation of task, whereby think about like an HR
Speaker 4: professional as an example, going through a job requisition is
Speaker 4: able to interact with multiple systems via a very simple
Speaker 4: chat interface and have work dynamically sequenced to support them
Speaker 4: in doing their task. That once again requires a notion
Speaker 4: of working with models and technology in a way that
Speaker 4: in many ways can be un unique to how a
Speaker 4: business wishes to work and indeed, in various cases can
Speaker 4: embody what they consider their their secret source or their
Speaker 4: differentiated advantage. So once again, a platform that recognizes that
Speaker 4: and designed for business that's not the same scope or
Speaker 4: frame of reference for a consumer platform. And then you know,
Speaker 4: we're also seeing a lot of work around cod generation,
Speaker 4: application modernization, you know, and people enhancing their skills. So
Speaker 4: targeted becomes really important. Mentioned open, and I mentioned targeted,
Speaker 4: targeted to the business to the use cases that they
Speaker 4: need to do underpinning that. Then it's trusted. So everything
Speaker 4: I gave you in those targeted use cases talk about
Speaker 4: handling enterprise proprietary and specific data. We are trusted in
Speaker 4: this regard.
Speaker 3: Right.
Speaker 4: We have been serving the business for many, many a year,
Speaker 4: and we are designing our platform and even our principles
Speaker 4: and way of operating to recognize and enable that. Both
Speaker 4: in terms of the work we do around the governance
Speaker 4: framework and transparency that you're able to gain and apply,
Speaker 4: but even in the way we allow our platform to
Speaker 4: be deployed in multiple kind of locations, of footprints, consumed
Speaker 4: as a service on a hyperscaler, running your own private
Speaker 4: footprint on prem or.
Speaker 3: Your cloud footprint.
Speaker 4: All of these need to be brought together to meet
Speaker 4: the needs of an actual enterprise business. My last comment
Speaker 4: is where I think we're fundamentally differentiated is we're really
Speaker 4: about empowering our customers to take advantage of AI to
Speaker 4: unleash the intelligence capabilities productivity of their own business. This
Speaker 4: isn't about, oh, we've established a bunch of APIs that
Speaker 4: you can ask questions. This is about how do you
Speaker 4: craft what you need for your business to deliver differentiated
Speaker 4: value to your customers, shareholders, employees with all the appropriate
Speaker 4: protections as well. And so there's a lot of focus
Speaker 4: on what we've done with the platform and the tool
Speaker 4: set to enable that, to enable what we like to
Speaker 4: call AI value creators, not just consumers of AI value.
Speaker 2: When you were talking about basically enterprise adoption of AI,
Speaker 2: you use the word trust, and I'm curious, you know,
Speaker 2: what does trust mean in the context of AI and
Speaker 2: the enterprise.
Speaker 4: I would kind of deconstruct trust along these k avenues.
Speaker 4: If AI is about giving you insights to help you
Speaker 4: support decisions, how do you trust what insights it's provided?
Speaker 4: What data did it use? What did it consider based
Speaker 4: upon that data that therefore led to the insight provided?
Speaker 4: Why is this important? Why this notion of trust? Well, one,
Speaker 4: you're about to make a decision, so you want to
Speaker 4: understand the basis for a decision. It's no different than
Speaker 4: me asking you something and then saying, okay, can you
Speaker 4: explain you're working? Right, That would be a notion of
Speaker 4: trust that we establish and a very natural interaction as humans, right,
Speaker 4: we do it all the time, right, So there is
Speaker 4: that element. The other reason why it becomes important if
Speaker 4: you're applying AI into business processes and therefore how your
Speaker 4: business works, you want to make sure that you know
Speaker 4: what biases are built in to any decision or not,
Speaker 4: or if the AI the model in effect is drifting
Speaker 4: away from kind of the parameters that you would want
Speaker 4: it to remain within, right or go trust And so
Speaker 4: in many ways that's one big aspect of trusting the
Speaker 4: technology because you're applying it into decisions you need to
Speaker 4: make every day, and you need to know in very
Speaker 4: simple terms how it works and how it is working.
Speaker 4: The element of trust that I think is important in
Speaker 4: this discussion. Who are you getting your AI from? That's
Speaker 4: very important to us as a company here at IBM. Right,
Speaker 4: given we serve business that trust becomes extremely important and
Speaker 4: what are the elements of that trust?
Speaker 3: What are the customers trying to understand? Well?
Speaker 4: First and foremost, what's your ethos around AI? We're very
Speaker 4: clear on the customer's data is their data. When they
Speaker 4: tune or refine those models to meet their use cases,
Speaker 4: that is all theirs, and we actually provide the ability
Speaker 4: for them to do that in very isolated and protected
Speaker 4: ways as they choose, and we never use that data
Speaker 4: without explicit opting and permissions. Right Customers might say, oh, yeah,
Speaker 4: use this so that you can make a generally overall
Speaker 4: better model. But it's full awareness, full transparency that is important.
Speaker 4: That's a trust of who you're doing business with. So
Speaker 4: that's how I think about trust. How do you build
Speaker 4: systems you trust? And are you working with people you trust?
Speaker 1: I find Kareem's point about trust when it comes to
Speaker 1: data to be so important because as powerful as AI
Speaker 1: tools can be, their helpfulness is dependent on how trustworthy
Speaker 1: the data is. Humans will have to decide if our data,
Speaker 1: our decision making, and our AI insights live up to
Speaker 1: the vision we hope to achieve in business. As Green
Speaker 1: and Jacob continue the conversation, Jacob asks some more practical
Speaker 1: questions about how businesses can adopt A into their own processes.
Speaker 1: Let's listen.
Speaker 2: How can businesses move toward integrating AI as part of
Speaker 2: their core business model? Instead of, you know, sort of
Speaker 2: as an add on on the periphery.
Speaker 3: It's funny, you know.
Speaker 4: My simple answer to that is it's actually the simplest
Speaker 4: thing in the world to do. By thinking about your business,
Speaker 4: thinking about your elements of differentiation, and then thinking about
Speaker 4: how AI can help you extend expand those Right, what
Speaker 4: do you want to be known for? I picked a
Speaker 4: very simple use case of customer service interaction. Almost every
Speaker 4: business needs to do that and wants to do it better,
Speaker 4: and so it becomes.
Speaker 3: A way to stop.
Speaker 4: But then as you begin to work your way through,
Speaker 4: you think about various automation of business processes. You think
Speaker 4: about decisions that need to be made right, or how
Speaker 4: can individuals be helped, how can they be made more productive?
Speaker 4: I think always be comes a very important one, right, So,
Speaker 4: and you can apply this in many context a financial
Speaker 4: analyst looking at reams of data and trying to derive insights.
Speaker 4: How do you leverage AI to make that financial analyst
Speaker 4: even more powerful? And so that's how I advise you know,
Speaker 4: people to always look at it. Think about your task,
Speaker 4: think about your business processes, think about where help is
Speaker 4: needed or where new value could be unlocked. And then
Speaker 4: you're applying AI as a tool to achieve that end.
Speaker 2: One of the themes we return to on this show
Speaker 2: a lot is creativity and the relationship between technology and creativity.
Speaker 2: And I'm curious how you think that AI can help
Speaker 2: people be more creative at work.
Speaker 4: I think AI can help people be more creative at
Speaker 4: work by automating the mundane to unlock your mind to
Speaker 4: be able to focus on higher value. You know, I've
Speaker 4: used a couple of times I've talked about deriving insights
Speaker 4: from data right to drive informed decisions.
Speaker 3: If you can.
Speaker 4: Use AI to gather a lot more insights into one place,
Speaker 4: then you could typically do yourself or more manually you'd
Speaker 4: have to like write it down, look at six spreadsheets,
Speaker 4: copy from here to there. Then you actually have more
Speaker 4: time to look at that data, digest those insights, and
Speaker 4: think about what do I need to do with these
Speaker 4: as a business, which direction do I want to go?
Speaker 4: I think of it freeing us up to do more
Speaker 4: of what we actually as humans do extremely well, which
Speaker 4: is actually that creative thinking. Exactly simple terms, why do
Speaker 4: we use a calculator to do arithmetic? It's not that
Speaker 4: we cannot necessarily knock it out ourselves. But if you're
Speaker 4: trying to balance your checkbook, to use an old phrase
Speaker 4: or dare I say, what's a check But so let
Speaker 4: us modernize that. If you're trying to check your expenses
Speaker 4: for the month and your performance against budget, yes, you
Speaker 4: could print out all your statements, circle everything and add
Speaker 4: it all up. Or you could begin to use technology
Speaker 4: to improve that experience so you can get more time
Speaker 4: to think about what really am I learning from my
Speaker 4: spending patterns and what do I want to do about it.
Speaker 4: It's a very simple personal example, but I think it's
Speaker 4: fundamentally what we're talking about here, and that's always been
Speaker 4: in my mind, the promise of technology freeing us up
Speaker 4: to actually apply ourselves to higher value thought and higher
Speaker 4: value problems.
Speaker 2: So we've been talking basically about the present so far,
Speaker 2: and I'm curious if you think about the future and
Speaker 2: you think, you know medium to long term, how do
Speaker 2: you think AI is going to transform business? And you know,
Speaker 2: how can people now, business leaders now prepare for what's coming.
Speaker 4: So to an earlier comment I made, I do really
Speaker 4: think that we are at an inflection point with the
Speaker 4: advancement of the technologies of AI. I talked about foundation models.
Speaker 4: We definitely at the cusp of being able to address
Speaker 4: use cases at scale that were more challenging before, and
Speaker 4: so I do think the future looks like a lot
Speaker 4: more generative AI surfacing within the enterprise and within business
Speaker 4: processes and manifesting in interesting ways. I think it's almost
Speaker 4: a given that any piece of software, right, whether you
Speaker 4: think of it in terms of an application or you
Speaker 4: think about it in terms of you know, the interact
Speaker 4: with the website will have conversational enabled interfaces from the
Speaker 4: analyst saying give me the latest reports for the last
Speaker 4: three months, you know, typing that or saying it versus
Speaker 4: the right click file blah blah. I think you're going
Speaker 4: to see that change in interaction to more conversational interaction.
Speaker 3: I think, particularly chat based.
Speaker 2: We forget that the graphical user interface is just a metaphor, right,
Speaker 2: It's not like the way computers work. It's just an interface.
Speaker 2: And if chat is a better interface, people will use chat.
Speaker 4: And I think we're going to see that rarely explode,
Speaker 4: and that's powered by a lot of this generative AI
Speaker 4: work because it becomes for it to feel natural for
Speaker 4: it to be as informed to readily. As I said,
Speaker 4: link things together and orchestrate. That's a big part. So
Speaker 4: I think I see that happening and the appropriate or
Speaker 4: associated productivity on locks you begin to see with that
Speaker 4: will just change what kind of decisions, the ease with
Speaker 4: which we can make more and more informed business decisions.
Speaker 4: And so for me, it's that rolling out at scale,
Speaker 4: touching everything procurement. HR think about the advent of the
Speaker 4: spreadsheet and how many different roles it just ended up
Speaker 4: touching and everybody can use or does user spreadsheeting business
Speaker 4: in some shape, size or form. So I think of
Speaker 4: this as AI at scale. And so what it therefore
Speaker 4: means from as you said, getting prepared, Well, it's all
Speaker 4: about gaining first of all, the right understanding of the
Speaker 4: technologies and part of what LOT will be talking about
Speaker 4: necessary ingredients begin to be well, where do I want
Speaker 4: to apply it first? What data do I need to
Speaker 4: bring together to readily support that, What unlocks what new value?
Speaker 4: And I think it's going to be like this rollout, Right,
Speaker 4: you got to start with this project and then there's
Speaker 4: another project, and very soon it will be so much
Speaker 4: it will be ubiquitous in the way it's supports. It's
Speaker 4: the work we need to do that. It will just
Speaker 4: speak to a new way of us working that is,
Speaker 4: when you now look back, will be pretty different from
Speaker 4: how we work today. You see the seeds today, But
Speaker 4: I would argue, think of that now like fully bloomed.
Speaker 4: It's a forest, not a not a flowerbed.
Speaker 1: You know.
Speaker 2: Yeah, yeah, yeah, great, one other one other sort of
Speaker 2: loose thread. I wanna I want to return to uh
Speaker 2: And that's that's governance. Right, you talked about governance and
Speaker 2: maybe just just to help sort of set the table,
Speaker 2: like you mentioned it in a broadway but narrowly, What
Speaker 2: does governance mean in the context of IBM's work on
Speaker 2: enterprise A high.
Speaker 4: I think, as the word tries to suggest, it is
Speaker 4: about having the way to govern one's activities in this realm,
Speaker 4: which really speaks to policies, rules and frameworks within which
Speaker 4: to understand all of that. Now, before we dive in
Speaker 4: the direction of regulation, which is where people often go,
Speaker 4: policies can be all internal.
Speaker 3: So think about it this way.
Speaker 4: If I say to you, when I build AI, I
Speaker 4: do not use my customer's data. Is their customer's data?
Speaker 4: Then from a governance perspective, I need processes that ensure
Speaker 4: I know what data I'm using, and I can prove
Speaker 4: to myself just first of all internally, forget about anybody else,
Speaker 4: that I'm actually adhering to the policies I've laid out. That,
Speaker 4: in my mind is a lot of what governance is about,
Speaker 4: and in the context of AI, it always tends to
Speaker 4: I think structure around three key areas. Data Where did
Speaker 4: it come from? And what did I do with it?
Speaker 4: And how did I apply it? And where did I
Speaker 4: use it? And then usage? What do I expect this
Speaker 4: model to do? Is this model still performing the way
Speaker 4: I think it should be performing. What are my processes
Speaker 4: to address whether they answered that question is yes or no?
Speaker 4: And manage that through? And then importantly so this is
Speaker 4: then to bridge to regulation. If you take a look
Speaker 4: at what's going on in the world of AI regulation
Speaker 4: and our point of view on this, by the way,
Speaker 4: is that you actually regulate the use cases, not the technology.
Speaker 4: Then from a governance perspective, how are you able to
Speaker 4: clearly understand, track and account for what use cases you
Speaker 4: are leveraging AI for? And then back to my earlier comments,
Speaker 4: how that AI is performing?
Speaker 2: And when you talk about governance, how do you make
Speaker 2: sure that you have the governance you need without inhibiting innovation.
Speaker 4: I think what is key, and this is key, A
Speaker 4: key design point for what we're doing with What's the
Speaker 4: next is how you make governance seamless institute versus another
Speaker 4: activity that you do right. And so our goal is
Speaker 4: to try and drive that kind of seamless interactions or
Speaker 4: value add in terms of governance, so that when oh,
Speaker 4: let's pull through the history right of everything we've done here,
Speaker 4: or what prompts we've created, or what data we've used,
Speaker 4: it's kind of already there, right, and so you can
Speaker 4: feel free to be innovating and testing out your different
Speaker 4: prompts and all that stuff, or bringing in your data
Speaker 4: sets without saying, oh, before I do that, I need
Speaker 4: to make sure I run this checker. And now you
Speaker 4: can kind of bring it in systems kind of automatically
Speaker 4: categorizing it, and then you can go in a litter
Speaker 4: very five, validate or explore, say I'm no longer going
Speaker 4: to take this path based upon these facts. I think
Speaker 4: the more we can make it more of a natural
Speaker 4: extension of the activities that need to be done, the
Speaker 4: more we can make it then just a part of
Speaker 4: what needs to be done, and as you're to your point,
Speaker 4: gain our governance needs or support the governance needs of
Speaker 4: our customers without stifling the innovation of the individuals at
Speaker 4: the glass trying to think through, I iteratively think through
Speaker 4: new value ways to do work.
Speaker 2: Excellent. Let me ask you. Are there things I didn't
Speaker 2: ask you that I should. Are there things you want
Speaker 2: to talk about that we didn't talk about.
Speaker 3: I think we covered quite a lot of truth it.
Speaker 4: Oh, no, I think we we covered the bases there.
Speaker 1: Earlier, Green mentioned that we are at an inflection point
Speaker 1: in AI technology. Implementing AI in business will get easier,
Speaker 1: and AI platforms like Watson x can empower even the
Speaker 1: largest enterprise businesses to reinvent the way they run. As
Speaker 1: Greem said, in the same way the spreadsheet took over
Speaker 1: business operations, the adoption of AI at enterprise scale could
Speaker 1: be just as ubiquitous. It's not an overstatement to say
Speaker 1: that a new era of work may be upon us.
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