Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Today, we
Speaker 1: are witnessed to one of those rare moments in history,
Speaker 1: the rise of an innovative technology with the potential to
Speaker 1: radically transform business in society forever. That technology, of course,
Speaker 1: is artificial intelligence, and it's the central focus for this
Speaker 1: new season of Smart Talks with IBM. Join hosts from
Speaker 1: your favorite Pushkin podcasts as they talk with industry experts
Speaker 1: and leaders to explore how businesses can integrate AI into
Speaker 1: their workflows and help drive real change in this new
Speaker 1: era of AI, and of course, host Malcolm Gladwell will
Speaker 1: be there to guide you through the season and throw
Speaker 1: in his two cents as well. Look out for new
Speaker 1: episodes of Smart Talks with IBM every other week on
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com slash smart Talks.
Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 2: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. This season,
Speaker 2: we're continuing our conversation with new creators visionaries who are
Speaker 2: creatively applying technology in business to drive change, but with
Speaker 2: a focus on the transformative power of artificial intelligence and
Speaker 2: what it means to leverage AI as a game changing
Speaker 2: multiplier for your business. Our guest today is Kareem Yousef,
Speaker 2: Senior Vice President of Product Management and Growth for IBM Software.
Speaker 2: Kareem's focus at IBM is on product strategy, thinking about
Speaker 2: the roadmap for IBM Software products and how they can
Speaker 2: deliver effective and compelling customer experiences with the current boom
Speaker 2: and generative AI. Kareem's job is to help businesses figure
Speaker 2: out how they can apply artificial intelligence at scale to
Speaker 2: help solve big problems and boost productivity at new orders
Speaker 2: of magnitude. In today's episode, you'll hear Kareem explain how
Speaker 2: AI powered by foundation models can make AI adoption by
Speaker 2: enterprise businesses even easier, how generative AI will change the
Speaker 2: way businesses process data and make decisions, and how these
Speaker 2: considerations influence the design of Watson x, IBM's next generation
Speaker 2: AI and data platform. Kareem spoke with Jacob Goldstein, host
Speaker 2: of the Pushkin podcast What's Your Problem. A veteran business journalist,
Speaker 2: Jacob has reported for The Wall Street Journal, the Miami Herald,
Speaker 2: and was a longtime host of the NPR program Planet Money. Okay,
Speaker 2: let's get to the interview.
Speaker 3: I'm Jacob Goldstein. I'm one of the hosts at Pushkin
Speaker 3: and a correspondent on this show, and I'm delighted to
Speaker 3: have you here. Can you introduce yourself?
Speaker 4: Ah? Hi, I'm Kareem Yusuf. I'm this and your vice
Speaker 4: president of Product management and Growth for IBM Software. You
Speaker 4: can think of me as the chief product officer for
Speaker 4: IBM Software.
Speaker 3: Okay, sounds like a big job. We're here today to
Speaker 3: talk about AI. We've heard really an extraordinary amount in
Speaker 3: the last few months about chat GPT and you know,
Speaker 3: particularly in how it's used in the very kind of
Speaker 3: consumer facing way. But I'm curious what is the rise
Speaker 3: of chat GPT and you know, AI more generally, what
Speaker 3: 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 data and then actually lastly
Speaker 4: the processes or activities you do in accordance with that decision.
Speaker 4: So if you then think about AI as applied to
Speaker 4: business right in that context, well, the first place it
Speaker 4: often starts is how do you make sense of a
Speaker 4: lot of the data associated with driving the business? And
Speaker 4: so AI has always been, in my mind at its
Speaker 4: 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 3: Actions, that's a really helpful framework. And then if we
Speaker 3: think about sort of what's happening in the world now
Speaker 3: with you know, enterprise businesses NAI, what are you seeing
Speaker 3: 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 AI,
Speaker 4: and the whole chat gpt to what has been going
Speaker 4: on in what I would term the realm of more
Speaker 4: standardized machine learning models. A lot of what has been
Speaker 4: going on has been very much in the realms of
Speaker 4: certain things like anomaly detection or optimization, right, using machine
Speaker 4: learning models to do that kind of work, and where
Speaker 4: might it apply well, think of anomaly detection in security
Speaker 4: software right detecting threats based upon different events flowing through
Speaker 4: or in enterprise asset management software monitoring equipment and detecting
Speaker 4: anomalies within their behavior, or even in IT automation software
Speaker 4: once again detecting anomalies based upon what's going on with
Speaker 4: various IT events and then tasks that should occur. Optimizations
Speaker 4: 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. Now, what has happened
Speaker 4: recently and has been brought to the four has been
Speaker 4: this discussion of generative AI, which is powered by a
Speaker 4: very specific innovation, this notion of foundation models. And in
Speaker 4: the simplest way to think about it, it's about training
Speaker 4: this 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: would argue at an inflection point? I like to liken
Speaker 4: it to teaching a child the alphabet. When you teach
Speaker 4: a child and 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. Generative in this case meaning
Speaker 4: creation of new content.
Speaker 3: So let's talk about what's in X. IBM recently announced
Speaker 3: what's an X. Just first of all, what is that?
Speaker 3: What 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,
Speaker 4: while it leads you to the components of what's an
Speaker 4: X and to a set of use cases. So let
Speaker 4: me paint a few quick pictures for you here. What's
Speaker 4: an X first of all, is about enabling our customers
Speaker 4: to manipulate models against their task, manipulate these foundation models
Speaker 4: against their task. Our belief is that the world is
Speaker 4: a multi model world, right and especially when you think
Speaker 4: about it in the context of business. Models are going
Speaker 4: to come from various sources, the ones we supply, the
Speaker 4: ones out there in open source, and so of you.
Speaker 4: But there are activities you need to do around these
Speaker 4: models 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
Speaker 4: manipulation of models to meet your use case. Yeah. The
Speaker 4: second component is dot data, So what's the next? Dot
Speaker 4: data is essentially a next generation data store is based
Speaker 4: upon something referred to as an open data lakehouse architecture
Speaker 4: that helps to bring together the data that's needed to
Speaker 4: actually do the AI. In this case, when you think
Speaker 4: about manipulating a model, a foundation model, you're generally using
Speaker 4: some data to prompt it, tune it, to train it
Speaker 4: to your use cases. And so we provide a very
Speaker 4: open data store that allows all manner of data and
Speaker 4: formats to be brought through. Today you that and the
Speaker 4: third component is what's next up governance And this is
Speaker 4: all about the framework and the toolkit required to apply
Speaker 4: the right governance principles across doing this kind of work,
Speaker 4: because when you're deploying AI within the enterprise, governance is
Speaker 4: actually important, right, It's critical to understand why is your
Speaker 4: data coming from? What data did you add in? How
Speaker 4: is your model performing? Are you able to keep an
Speaker 4: appropriate audit trail of your activities for your own internal
Speaker 4: policy and compliance needs or for regulatory needs as well.
Speaker 3: So this platform, the system that you're describing, I'm curious,
Speaker 3: how is it different from the you know, the generative
Speaker 3: AI options that you know we've all been hearing about
Speaker 3: 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. Right. We fundamentally believe that to do this
Speaker 4: kind of work within the enterprise, you need an open
Speaker 4: platform that, as I said, is open to all manner
Speaker 4: of models from all sources. It's one of the reasons
Speaker 4: why we announced our partnership with hugging Face to make
Speaker 4: sure that our clients can gain access to open source
Speaker 4: innovation within the platform to do their work.
Speaker 3: And hugging Face, to be clear, is sort of the
Speaker 3: open source AI kind of hub.
Speaker 4: That's right, that's correct. Yes, it's a marketplace hub for
Speaker 4: all kind of ecosystem coordinator for open source models. And
Speaker 4: I believe there's a lot of innovation going on out there. So,
Speaker 4: first of all, open becomes important. The second targeted So
Speaker 4: our focus is very much on enabling these business use cases, right,
Speaker 4: And you might say what kind of use cases are
Speaker 4: we talking about? I give you three very quick ones
Speaker 4: that with our customers are focused on a lot of
Speaker 4: focus around and enhancing customer service use cases. Think of
Speaker 4: this as chatbots or digital assistance that are further trained
Speaker 4: in more and more information about what the company has
Speaker 4: to offer, or could be internal policies, external policy, so
Speaker 4: on and so forth. This means a platform that makes
Speaker 4: it really easy to bring your own data to train
Speaker 4: and tune the model, while protecting your own data as
Speaker 4: extremely important for the enterprise right. Another important use case
Speaker 4: seeing a lot of focused on what i'd call AI
Speaker 4: based orchestration or automation of task whereby think about like
Speaker 4: an HR professional as an example, going through a job
Speaker 4: requisition is able to interact with multiple systems via a
Speaker 4: very simple chat interface and have work dynamically sequenced to
Speaker 4: support them in doing their task. That once again requires
Speaker 4: a notion of working with models and technology in a
Speaker 4: way that in many ways can be unique to how
Speaker 4: a business wishes to work and indeed, in various cases
Speaker 4: can embody what they consider their their secret source or
Speaker 4: their differentiated advantage. So once again, a platform that recognizes
Speaker 4: that and designed for business that's not the same scope
Speaker 4: or frame of reference for a consumer platform. And then
Speaker 4: you know, we're also seeing a lot of work around
Speaker 4: cod generation, application modernization, you know, and people enhancing their skills.
Speaker 4: So targeted becomes really important. Mentioned open and I mentioned
Speaker 4: targeted targeted to the business to the use cases that
Speaker 4: they need to do. Underpinning that then it's trusted. So
Speaker 4: everything I gave you in those targeted use cases talk
Speaker 4: about handling enterprise proprietary and specific data. We are trusted
Speaker 4: in this regard right. We have been serving the business
Speaker 4: for many, many a year, and we are designing our
Speaker 4: platform and even our principles and way of creating to
Speaker 4: recognize and enable that. Both in terms of the work
Speaker 4: we do around the governance framework and transparency that you're
Speaker 4: able to gain and apply, but even in the way
Speaker 4: we allow our platform to be deployed in multiple kind
Speaker 4: of locations, of footprints consumed as a service on a hyperscaler,
Speaker 4: running your own private footprint on prem or 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 different
Speaker 4: shaped value to your customers, shareholders, employees with all the
Speaker 4: appropriate protections as well. And so there's a lot of
Speaker 4: focus on what we've done with the platform and the
Speaker 4: tool set to enable that, to enable what we like
Speaker 4: to call AI value creators, not just consumers of AI value.
Speaker 3: When you were talking about basically enterprise adoption of AI,
Speaker 3: you use the word trust, and I'm curious, you know,
Speaker 3: what does trust mean in the context of AI and
Speaker 3: 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? What are the
Speaker 4: customers trying to understand? Well, first and foremost, what's your
Speaker 4: ethos around AI? We're very clear on the customer's data
Speaker 4: is their data when they tune or refine those models
Speaker 4: to meet their use cases. That is all THEIRS actually
Speaker 4: provide the ability for them to do that in very
Speaker 4: isolated and protected ways as they choose, and we never
Speaker 4: use that data without explicit opting and permissions. Right, customers
Speaker 4: might say Oh yeah, use this so that you can
Speaker 4: make a generally overall better model. But it's full awareness,
Speaker 4: full transparency that is important. That's a trust of who
Speaker 4: you're doing business with. So that's how I think about trust.
Speaker 4: How do you build systems you trust? And are you
Speaker 4: working with people you trust?
Speaker 2: I find Kareem's point about trust when it comes to
Speaker 2: data to be so important because as powerful as AI
Speaker 2: tools can be, their helpfulness is dependent on how trustworthy
Speaker 2: the data is. Humans will have to decide if our data,
Speaker 2: our decision making, and our AI insights live up to
Speaker 2: the vision we hope to achieve in business. As Green
Speaker 2: and Jacob continue the conversation, Jacob asks some more practical
Speaker 2: questions about how businesses can adopt AI into their own processes.
Speaker 3: Let's listen, how can businesses move toward integrating AI as
Speaker 3: part of their core business model instead of, you know,
Speaker 3: sort of as an add on on the periphery.
Speaker 4: It's funny, you know. My simple answer to that is
Speaker 4: it's actually the simplest thing in the world to do.
Speaker 4: By thinking about your business, thinking about your elements of differentiation,
Speaker 4: and then thinking about how AI can help you extend
Speaker 4: expand those Right, what do you want to be known for.
Speaker 4: I picked a very simple use case of customer service interaction.
Speaker 4: Almost every business needs to do that and wants to
Speaker 4: do it better, and so it becomes 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 made more productive?
Speaker 4: I think always becomes 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 3: One of the themes we return to on this show
Speaker 3: a lot is creativity and the relationship between technology and creativity.
Speaker 3: And I'm curious how you think that AI can help
Speaker 3: 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. 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 its 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 modernize that.
Speaker 4: If you're trying to check your expenses for the month
Speaker 4: and your performance against budget, yes you could print out
Speaker 4: all your statements, circle everything and add it all up.
Speaker 4: Or you could begin to use technology to improve that experience,
Speaker 4: so you can get more time to think about what
Speaker 4: really am I learning from my spending patterns and what
Speaker 4: do I want to do about it. It's a very
Speaker 4: simple personal example, but I think it's fundamentally what we're
Speaker 4: talking about here, and that's always been in my mind,
Speaker 4: the promise of technology freeing us up to actually apply
Speaker 4: ourselves to higher value thought and higher value problems.
Speaker 3: So we've been talking basically about the present so far,
Speaker 3: and I'm curious if if you think about the future
Speaker 3: and you think, you know, medium to long term, how
Speaker 3: do you think AI is going to transform business? And
Speaker 3: you know, how can people now, business leaders now prepare
Speaker 3: 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 think, whether
Speaker 4: you think of it in terms of an application or
Speaker 4: you think about it in terms of you know, the
Speaker 4: interact with the website will have conversational enabled interfaces from
Speaker 4: the analyst saying give me the latest reports for the
Speaker 4: last three months, you know, typing that or saying it
Speaker 4: versus the right click file blah blah. I think you're
Speaker 4: going to see that change in interaction to more conversational interaction.
Speaker 4: I think, particularly chat based.
Speaker 3: We forget that the graphical user interface is just a metaphor, right,
Speaker 3: It's not like the way computers work. It's just an interface.
Speaker 3: 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 to get and orchestrate. That's a big part.
Speaker 4: So I think I see that happening and the appropriate
Speaker 4: or associated productivity on locks you begin to see with
Speaker 4: that will just change what kind of decisions, the ease
Speaker 4: with which we can make more and more formed 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 technologies.
Speaker 4: And part of what a lot we'll be talking about
Speaker 4: necessary ingredients began 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 ubiquitu just in the way it supports
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. You know, yeah, yeah, yeah, great.
Speaker 3: One other one other sort of loose thread I want
Speaker 3: to I want to return to UH and that's that's governance. Right,
Speaker 3: you talked about governance and maybe just just to help
Speaker 3: sort of set the table, like you mentioned it in
Speaker 3: a broadway but narrowly, what does governance mean in the
Speaker 3: context of IBM's work on enterprise AHI.
Speaker 4: I think, as the word tries to suggest, it is
Speaker 4: about having the way to govern one's activity 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. 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.
Speaker 4: That in my mind is a lot of what governance
Speaker 4: is about, and in the context of AI, it always
Speaker 4: tends to I think structure around three key areas data
Speaker 4: where did it come from? And what did I do
Speaker 4: with it? And how did I apply it? And where
Speaker 4: did I use it? And then usage, what do I
Speaker 4: expect this model to do? Is this model still performing
Speaker 4: the way I think it should be performing? What are
Speaker 4: my processes to address whether they answered that question is
Speaker 4: yes or no? And manage that through. And then importantly
Speaker 4: so this is then to bridge to regulation. If you
Speaker 4: take a look at what's going on in the world
Speaker 4: of AI regulation and our point of view on this,
Speaker 4: by the way, is that you actually regulate the use cases,
Speaker 4: not the technology. Then from a governance perspective, how are
Speaker 4: you able to clearly understand, track and account for what
Speaker 4: use cases you are leveraging AI for? And then back
Speaker 4: to my earlier comments how that AI.
Speaker 3: Is performing and when you talk about how do you
Speaker 3: make sure that you have the governance you need without
Speaker 3: 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 systems kind of automatically categorizing it,
Speaker 4: and then you can go in a lead very five,
Speaker 4: validate or explore, say I'm no longer going to take
Speaker 4: this path based upon these facts. I think the more
Speaker 4: we can make it more of a natural extension of
Speaker 4: the activities that need to be done, the more we
Speaker 4: can make it then just a part of what needs
Speaker 4: to be done. And as you're to your point, gain
Speaker 4: our governance needs or supports the governance needs of our
Speaker 4: customers without stifling the innovation of the individuals at the
Speaker 4: glass trying to think through I iteratively think through new
Speaker 4: value ways to do work excellent.
Speaker 3: Let me ask you are there things I didn't ask
Speaker 3: you that I should? Are there things you want to
Speaker 3: talk about that we didn't talk about.
Speaker 4: I think we covered quite a lot true it. Oh No,
Speaker 4: I think we we covered the bases there.
Speaker 2: Earlier, Green mentioned that we are at an inflection point
Speaker 2: in AI technology. Implementing a in business will get easier,
Speaker 2: and AI platforms like Watson x can empower even the
Speaker 2: largest enterprise businesses to reinvent the way they run. As
Speaker 2: Greem said, in the same way the spreadsheet took over
Speaker 2: business operations, the adoption of AI at enterprise scale could
Speaker 2: be just as ubiquitous. It's not an overstatement to say
Speaker 2: that a new era of work may be upon us.
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