Transforming Business with Generative AI
Key Points
- Kareem Yusuf, IBM’s senior vice‑president of product management and growth, explains that AI’s biggest business impact lies in enhancing the two core drivers of any operation: data and the decisions made from that data.
- By leveraging foundation models, IBM aims to make generative AI adoption easier for enterprises, turning AI into a “multiplier” that scales creativity and problem‑solving across entire organizations.
- Yusuf describes how generative AI will fundamentally change data processing and decision‑making workflows, enabling faster insights and more strategic actions throughout the value chain.
- These insights shaped the development of watsonx, IBM’s next‑generation AI and data platform, which is built to simplify AI integration, support scalable enterprise use cases, and deliver compelling customer experiences.
Sections
- AI as Business Multiplier - In this Smart Talks episode, Malcolm Gladwell interviews IBM senior VP Kareem Yusuf about leveraging foundation‑model AI, generative AI’s impact on enterprise decision‑making, and the design of IBM’s next‑gen watsonx platform.
- From Anomaly Detection to Generative AI - Kareem Yusuf contrasts earlier enterprise AI use cases—such as anomaly detection and optimization with traditional machine‑learning models—to the current wave of generative AI adoption.
- Introducing IBM watsonx Platform - The speakers explain watsonx as IBM's enterprise generative AI platform that enables businesses to manipulate foundation models from various sources for customized, multimodal use‑case deployment.
- Open Platform Vision & Use Cases - Kareem Yusuf explains their open‑source‑first strategy, partnership with Hugging Face, and focus on targeted enterprise AI applications such as customer‑service chatbots.
- Defining Trust in Enterprise AI - Kareem Yusuf explains that enterprise trust in AI hinges on understanding the data, reasoning, bias, and model stability behind AI-generated insights used for critical business decisions.
- From Add‑On to Core AI - Gladwell stresses the need for trustworthy data, and Kareem explains how businesses can embed AI into their core model by aligning it with their unique differentiators, starting with simple use cases like customer‑service automation.
- AI Frees Minds, Transforms Business - The speakers discuss using technology, especially generative AI, to automate low‑value tasks—like personal spending analysis—so people can focus on higher‑order thinking, and predict that conversational AI will become a standard layer across enterprise software.
- AI at Scale and Governance - The speakers outline how to prepare for enterprise AI by selecting pilot projects, gathering necessary data, and envisioning a pervasive rollout, while emphasizing the need for clear policies, rules, and frameworks to govern AI activities.
- Seamless AI Use-Case Governance - The speakers argue that regulation should target AI use cases—not the technology itself—and describe how watsonx embeds automated, transparent governance to track prompts and data while allowing unrestricted innovation.
- IBM Ad on Smart Talks - Malcolm Gladwell introduces a paid IBM sponsorship that notes the podcast’s production by Pushkin Industries, Ruby Studio, and iHeartMedia, and directs listeners to find the show on iHeartRadio, Apple Podcasts, or other platforms.
Full Transcript
# Transforming Business with Generative AI **Source:** [https://www.youtube.com/watch?v=hzAUCHh90Fg](https://www.youtube.com/watch?v=hzAUCHh90Fg) **Duration:** 00:32:14 ## Summary - Kareem Yusuf, IBM’s senior vice‑president of product management and growth, explains that AI’s biggest business impact lies in enhancing the two core drivers of any operation: data and the decisions made from that data. - By leveraging foundation models, IBM aims to make generative AI adoption easier for enterprises, turning AI into a “multiplier” that scales creativity and problem‑solving across entire organizations. - Yusuf describes how generative AI will fundamentally change data processing and decision‑making workflows, enabling faster insights and more strategic actions throughout the value chain. - These insights shaped the development of watsonx, IBM’s next‑generation AI and data platform, which is built to simplify AI integration, support scalable enterprise use cases, and deliver compelling customer experiences. ## Sections - [00:00:00](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=0s) **AI as Business Multiplier** - In this Smart Talks episode, Malcolm Gladwell interviews IBM senior VP Kareem Yusuf about leveraging foundation‑model AI, generative AI’s impact on enterprise decision‑making, and the design of IBM’s next‑gen watsonx platform. - [00:03:50](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=230s) **From Anomaly Detection to Generative AI** - Kareem Yusuf contrasts earlier enterprise AI use cases—such as anomaly detection and optimization with traditional machine‑learning models—to the current wave of generative AI adoption. - [00:07:35](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=455s) **Introducing IBM watsonx Platform** - The speakers explain watsonx as IBM's enterprise generative AI platform that enables businesses to manipulate foundation models from various sources for customized, multimodal use‑case deployment. - [00:10:46](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=646s) **Open Platform Vision & Use Cases** - Kareem Yusuf explains their open‑source‑first strategy, partnership with Hugging Face, and focus on targeted enterprise AI applications such as customer‑service chatbots. - [00:15:17](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=917s) **Defining Trust in Enterprise AI** - Kareem Yusuf explains that enterprise trust in AI hinges on understanding the data, reasoning, bias, and model stability behind AI-generated insights used for critical business decisions. - [00:18:32](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=1112s) **From Add‑On to Core AI** - Gladwell stresses the need for trustworthy data, and Kareem explains how businesses can embed AI into their core model by aligning it with their unique differentiators, starting with simple use cases like customer‑service automation. - [00:22:14](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=1334s) **AI Frees Minds, Transforms Business** - The speakers discuss using technology, especially generative AI, to automate low‑value tasks—like personal spending analysis—so people can focus on higher‑order thinking, and predict that conversational AI will become a standard layer across enterprise software. - [00:25:21](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=1521s) **AI at Scale and Governance** - The speakers outline how to prepare for enterprise AI by selecting pilot projects, gathering necessary data, and envisioning a pervasive rollout, while emphasizing the need for clear policies, rules, and frameworks to govern AI activities. - [00:28:27](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=1707s) **Seamless AI Use-Case Governance** - The speakers argue that regulation should target AI use cases—not the technology itself—and describe how watsonx embeds automated, transparent governance to track prompts and data while allowing unrestricted innovation. - [00:31:52](https://www.youtube.com/watch?v=hzAUCHh90Fg&t=1912s) **IBM Ad on Smart Talks** - Malcolm Gladwell introduces a paid IBM sponsorship that notes the podcast’s production by Pushkin Industries, Ruby Studio, and iHeartMedia, and directs listeners to find the show on iHeartRadio, Apple Podcasts, or other platforms. ## Full Transcript
Malcolm Gladwell: Hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries,
iHeartRadio and IBM. I’m Malcolm Gladwell. 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 Kareem Yusuf, senior vice president of product
management and growth for IBM software. Kareem’s focus at IBM is on product strategy,
thinking about the roadmap for IBM software products to ensure they deliver effective
and compelling customer experiences. With the current boom in generative AI,
Kareem’s job is to help businesses figure out how they can apply artificial intelligence at scale,
to help solve big problems and boost creativity at new orders of magnitude.
In today’s episode, you’ll hear Kareem 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 influenced the design of watsonx, IBM’s next-generation AI and data platform.
Kareem 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.
Jacob Goldstein: 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? Kareem Yusuf: Hi. I'm Kareem Yusef. 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.
Jacob Goldstein: 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, uh, and, you know—particularly in how it's used in the very,
kind of, consumer-facing way. But I'm curious: what does the rise of ChatGPT and, you know,
AI more generally—what does it mean for business?
Kareem Yusuf: 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 a product they produce and then sell or a 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.
Jacob Goldstein: That's a really helpful framework. And then if we think about sort of what's happening in the world now with, with,
you know, enterprise businesses and AI, what are you seeing with
enterprise adoption of AI at this moment? Kareem Yusuf: 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 ChatGPT—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, right? Using machine-learning models to do that kind of
work. And where might it apply? Well, think of anomaly detection in security software,
right? 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, uh, machine-learning AI to really make sense of
the data and provide a basis to drive decisions. Now, what—it's 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 an “air quote,” if I may, kind of “limitation,” in the sense that
the task—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 is so excited about it, why I would
argue it’s 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.”
Jacob Goldstein: So, let’s talk about watsonx. IBM recently announced watsonx. Just—first of all,
what is that? What is watsonx?
Kareem Yusuf: 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. watsonx, first of all, is about enabling our
customers to manipulate models against their task, manipulate these foundation models against their
task. Our belief is that the world is a multimodel 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, sort of, view. 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 watsonx.ai is that environment that builds—a 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 watsonx.data is essentially a next- generation data store.
It's 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 watsonx.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?
Jacob Goldstein: 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?
Kareem Yusuf: Well, I think it really comes down to the, 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,
right? 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. So that's the—
Jacob Goldstein: Hugging Face, to be clear, is sort of the open-source AI, kind of, hub.
Kareem Yusuf: 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, you know, 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,
right? Another important use case: seeing a lot of focus on what I would call “AI-based
orchestration” or automation of tasks, whereby—think about, like, 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 sauce” 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,
you know, we're also seeing a lot of work around code generation,
application modernization, you know, 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 talks 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, kind of,
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 take advantage of AI to unleash the intelligence, capabilities,
productivity of their own business. This isn't about, “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 in 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.
Jacob Goldstein: When you were talking about, basically, enterprise adoption of AI, you used the word “trust.” And I'm curious:
you know, what does, what does “trust” mean in the context of AI and the enterprise?
Kareem Yusuf: 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 insight is provided?
So: “What data did it use? What did it consider based upon that data that therefore led to the
insight provided?” Why is this important? Why—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 you're working?”
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 into 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?”
Jacob Goldstein: Huh.
Kareem Yusuf: 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?
Malcolm Gladwell: I find Kareem’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 Kareem and Jacob continue the conversation,
Jacob asks some more practical questions about how businesses can adopt AI into
their own processes. Let’s listen.
Jacob Goldstein: How can—how can businesses move toward integrating AI as part of their core business model instead of sort
of as an add-on on the periphery?
Kareem Yusuf: 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.” Jacob Goldstein: Uh huh.
Kareem Yusuf: Thinking about your elements of differentiation, and then thinking about
how AI can help you extend, expand those, right? What—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.
Jacob Goldstein: 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.
Kareem Yusuf: 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. 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. Think 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, just,
“What's a checkbook?” I've thought 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 thought and higher-value problems.
Jacob Goldstein: So we've been talking, basically, about the present so far. And I'm curious if you—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?
Kareem Yusuf: 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 interaction 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.
Jacob Goldstein: 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.
Kareem Yusuf: 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 it 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 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 so much—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?
Jacob Goldstein: Yeah. Yeah. Great. One other, sort of, loose thread I
want to—I want to return to, and that's, that's governance, right? You talked about
governance. And maybe just—just to help sort of set the table: you mentioned it in a broad way,
but narrowly, what does governance mean in the context of IBM's work on enterprise AI?
Kareem Yusuf: 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—uh,
my customer's data is their customer's 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 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, the bridge to regulation.
If you take a look at what's going on in the, 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.
Jacob Goldstein: And when you talk about governance, how do you make sure that you have the governance you need without inhibiting innovation?
Kareem Yusuf: I think what is key—and this is key, a key design point for what we're doing with
watsonx—is how you make governance seamless in situ 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? 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.” No, 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, and iteratively
think through, new valuable ways to do work.
Jacob Goldstein: 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?
Kareem Yusuf: I think we covered quite a lot, truth be told. No, I think we, we covered the bases there.
Malcolm Gladwell: Earlier, Kareem 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 Kareem 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.
Smart Talks with IBM is produced by Matt Romano, David Zha Nisha Venkat, and Royston Beserve, with
Jacob Goldstein. We’re edited by Lidia Jean Kott. Our engineers are Jason Gambrell, Sarah Brugueire,
and Ben Tolliday. Theme song by Gramoscope. Special thanks to Carly Migliori, Andy Kelly,
Kathy Callaghan, and the EightBar 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. To find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
or wherever you listen to podcasts. I’m Malcolm Gladwell.
This is a paid advertisement from IBM.