September 6, 2023 • 33 mins
As businesses adopt AI, a new era of problem-solving, innovation, and creative decision-making can be brought to scale. In this episode of Smart Talks with IBM, Malcolm Gladwell and Jacob Goldstein explore the future of AI in enterprise business AI for business with Kareem Yusuf, senior vice president of product management and growth for IBM software. They discuss the advent of foundation models, how AI can transform data storage and decision-making, and how next-generation AI platforms like watsonx from IBM can empower businesses to use AI at scale.
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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new season of the Smart Talks with IBM
podcast series.
Speaker 2 (00:09):
Today we are witnessed to one of those rare moments
in history, the rise of an innovative technology with the
potential to radically transform business and society forever. The technology,
of course, is artificial intelligence, and it's the central focus
for this new season of Smart Talks with IBM.
Speaker 1 (00:25):
Join hosts from your favorite Pushkin podcasts as they talk
with industry experts and leaders to explore how businesses can
integrate AI into their workflows and help drive real change
in this new era of AI. And of course, host
Malcolm Gladwell will be there to guide you through the
season and throw in his two cents as well.
Speaker 2 (00:43):
Look out for new episodes of Smart Talks with IBM
every other week on the iHeartRadio app, Apple Podcasts, or
wherever you get your podcasts, and learn more at IBM
dot com slash smart talks.
Speaker 3 (00:57):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. 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
(01:19):
what it means to leverage AI as a game changing
multiplier for your business. Our guest today is Kareem Yousef,
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 and how they can
deliver effective and compelling customer experiences with the current boom
(01:45):
and 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 productivity at new orders
of magnitude. In today's episode, you'll hear Kareem explaining how
AI powered by foundation models can make AI adoption by
enterprise businesses even easier, how generative AI will change the
(02:09):
way businesses process data and make decisions, and how these
considerations influenced the design of Watson x, 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,
(02:32):
and was a longtime host of the NPR program Planet Money. Okay,
let's get to the interview.
Speaker 4 (02:41):
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?
Speaker 5 (02:49):
Ah?
Speaker 6 (02:49):
Hi, I'm Kareem Yusuf.
Speaker 5 (02:51):
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.
Speaker 4 (02:59):
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 chat GPT and you know,
particularly in how it's used in the very kind of
consumer facing way. But I'm curious what is the rise
(03:19):
of chat GPT and you know, AI more generally, what
does it mean for business?
Speaker 5 (03:24):
Well, you know, it's if you kind of step back
and think about what really happens. You know, in a business,
you're really talking about a set of processes, right, you know,
activities that represent what a business needs to get done,
whether it's product, they produce and then sell or service
that they provide. And inherent to operating the business, I
(03:46):
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 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
(04:07):
sense of a lot of the data associated with driving
the 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.
(04:29):
So that's kind of my simple way of thinking of AI,
and we can obviously coloring with examples, but that's my
simplest way of thinking about AI. When you think about
in the business context, gain insights from masses of data
to support decisions and then drive actions.
Speaker 4 (04:44):
That's a really helpful framework. And then if we think
about sort of what's happening in the world now with
you know, enterprise businesses NAI, what are you seeing with
enterprise adoption of AI?
Speaker 5 (04:56):
At this moment, so we're really talking about most a
tale of two periods. So let me first of all
kind of take you back before the advent of what
I would call generative AI and the whole chat gpt
to what has been going on in what I would
term the realm of more standardized machine learning models. A
(05:17):
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
(05:41):
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,
(06:04):
whether you think of that in the context of application
resource management for IT or in the context of supply chain.
These have been very classical applications of machine learning AI
to really make sense of the data and provide a
basis to drive decisions. Now, what is characterized by all
(06:24):
those examples of given and the state of the art
of that kind of technology has always been it's very
task specific. So there was a air quotes, if I may,
kind of limitation in the sense that the tar it
had to be very task specific. And so we've seen
(06:45):
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
four has been this discussion of generative AI, which is
powered by a very specific innovation, this notion of foundation models.
(07:06):
And in the simplest way to think about it, it's
about training this large model that can then be refined
to various tasks. And the easiest one that everybody recognized
at the moment is the notion of a large language model,
a model that has an understanding of a lot of
(07:28):
the elements of a language such that it can be
refined to a variety of tasks. Write an essay, answer
a question, singer songs, so on, answers so forth. I
like to liken the power if you like, and this
will speak to the why everybody is so excited about it?
Speaker 6 (07:47):
Why would argue at an inflection point?
Speaker 5 (07:49):
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
(08:12):
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, thinking
of doing a poem, doing an essay, so on and
so forth, and so in the enterprise context, that means
(08:33):
that we're now talking about being able to unlock even
additional value at scale because of the nation of nature
foundation models and their appeal to generative use cases. Generative
in this case meaning creation of new content.
Speaker 4 (08:50):
So let's talk about Watson x. IBM recently announced what's
an X Just first of all, what is that? What
is what's an X?
Speaker 5 (08:58):
Well, what's an extra to our is our brand for
our platform, the WHATSAP 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 while,
it leads you to the components of what's next and
to a set of use cases. So let me paint
(09:19):
a few quick pictures for you here. What's anex 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 multi model world,
right and especially when you think about it in the
(09:40):
context of business. Models are going to come from various sources,
the ones we supply, the ones out there in open source,
and so of you. But there are activities you need
to do around these models to as I said, apply
them to your use case.
Speaker 6 (09:55):
And we'll talk about use cases in a bit. So
what's next.
Speaker 5 (09:58):
The AI is that vironment that build a tool if
you like, for being able to do those manipulation of
models to meet your specific use case. Thinks 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. Yeah, the second component
(10:20):
is dot data. So what's the next dot data is Essentially,
a next generation data store is based upon something referred
to as an open data lakehouse 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,
(10:42):
tune it, to train it to your use cases. And
so we provide a very open data store that allows
all manner of data and formats to be brought through
to do that. And the third component is what's next
dot governance, And this is all about the framework and
the toolkit rep required to apply the right governance principles
(11:04):
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 why 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
(11:26):
or for regulatory needs as well.
Speaker 4 (11:28):
So this platform, the system that you're describing, I'm curious,
how is it different from the you know, the generitive
AI options that you know we've all been hearing about
sort of in the press.
Speaker 5 (11:40):
Well, I think it really comes down to the ethos
or the principles that first of all drive the work
that we're doing. The first I would fixate on is
being open.
Speaker 6 (11:52):
Right.
Speaker 5 (11:52):
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.
Speaker 4 (12:14):
And hugging Face, to be clear, is sort of the
open source AI kind of hub.
Speaker 6 (12:19):
That's right, that's correct.
Speaker 5 (12:20):
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
(12:43):
you might say what kind of use cases are we
talking about? I give you three very quick ones that
with our customers are focused on a lot of focus
around enhancing customer service use cases. Think of this as
chatbots or digital assistance that are further trained in more
and more information about what the company has to offer
(13:04):
or could be internal policies, external policies, and 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 as extremely
important for the enterprise right. Another important use case seeing
(13:24):
a lot of focused on what i'd call AI based
orchestration or automation of task 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
(13:44):
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 can do, their secret source or their
differentiated advantage. So once again, a platform that recognizes that
(14:05):
and 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 cod generation,
application modernization, you know, and people enhancing their skills, so
targeted becomes really important.
Speaker 6 (14:23):
Mentioned open, and I mentioned.
Speaker 5 (14:24):
Targeted, targeted to the business to the use cases that
they need to do underpinning that, then it's trusted. So
everything I gave you in those targeted use cases talk
about handling enterprise proprietary and specific data. We are trusted
in this regard right. We have been serving the business
(14:46):
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 to gain and apply, but even in the
way we allow our platform to be deployed in multiple
(15:07):
locations of 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
(15:33):
unleash the intelligence capabilities productivity of their own business. This
isn't about, oh, we've established a bunch of APIs that
you can ask questions. This is about how do you
craft what you need for your business to deliver differentiated
value to your customers, shareholders, employees with all the appropriate
(15:58):
protections as well well. And so there's a lot of
focus on what we've done with the platform and the
tool set to enable that, to enable what we like
to call AI value creators, not just consumers of AI value.
Speaker 4 (16:12):
When you were talking about basically enterprise adoption of AI,
you use the word trust, and I'm curious, you know,
what does trust mean in the context of AI and
the enterprise.
Speaker 5 (16:29):
I would kind of deconstruct trust along these k avenues.
If AI is about giving you insights to help you
support decisions, how do you trust what insights it's provided?
What data did it use? What did it consider based
(16:53):
upon that data that therefore led to the insight provided?
Why is this important? Why this notion of trust? Well, one,
you're about to make a decision, so you want to
understand the basis for a decision. It's no different than
(17:14):
me asking you something and then saying, okay, can you
explain you're working? Right, that would be a notion of
trust that we establish and a very natural interaction as humans, Right,
we do it all the time, right, So there is
that element. The other reason why it becomes important if
you're applying AI into business processes and therefore how your
(17:35):
business works, you want to make sure that you know
what biases are built in to any decision or not
or if the AI the model in effect is drifting
away from kind of the parameters that you would want
it to remain within, right or go trust and so
(17:59):
in many a 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 element of trust that I think is important in
this discussion. Who are you getting your AI from? That's
(18:24):
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?
Speaker 6 (18:38):
Well?
Speaker 5 (18:38):
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
(19:00):
without explicit opting 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?
Speaker 3 (19:27):
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 Green
(19:47):
and Jacob continue the conversation, Jacob asks some more practical
questions about how businesses can adopt AI into their own processes.
Speaker 4 (19:57):
Let's listen, how can businesses move toward integrating AI as
part of their core business model instead of, you know,
sort of as an add on on the periphery.
Speaker 6 (20:09):
It's funny, you know.
Speaker 5 (20:10):
My simple answer to that is it's actually the simplest
thing in the world. To do by thinking about your business,
thinking about your elements of differentiation, and then thinking about
how AI can help you extend expand those Right, what
(20:31):
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
(20:53):
think always becomes a very important one. Right, So, and
you can apply this in many context 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 task, think
about your business processes, think about where help is needed
(21:16):
or where new value could be unlocked, and then you're
applying AI as a tool to achieve that end.
Speaker 4 (21:23):
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.
Speaker 5 (21:42):
I think AI can help people be more creative at
work by automating the mundane to unlock your mind to
be able to focus on higher value. You know, I've
used a couple of times I've talked about deriving insights
from data right to drive informed decisions.
Speaker 6 (22:00):
If you can.
Speaker 5 (22:01):
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?
(22:24):
I think of its freeing us up to do more
of what we actually as humans do extremely well.
Speaker 6 (22:30):
Which is actually that creative thinking.
Speaker 5 (22:33):
Exactly 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.
Speaker 4 (22:46):
Say, what's a check But so let us modernize that.
Speaker 5 (22:54):
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 and add it all up,
or you could begin to use technology to improve that
experience so you can get more time to think about
(23:16):
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.
Speaker 4 (23:37):
So we've been talking basically about the present so far,
and I'm curious if you think about the future and
you think, you know, medium to long term, how do
you think AI is going to transform business? And you know,
how can people now business leaders now prepare for what's coming.
Speaker 5 (23:57):
So to an earlier common 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 at the cusp of being able to address
(24:17):
use cases at scale that were more challenging before.
Speaker 6 (24:22):
And so I do think.
Speaker 5 (24:24):
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
(24:45):
terms of an application or you think about it in
terms of you know, the interact with the website will
have conversational enabled interfaces from the analyst saying give me
the latest reports for the last three months, you know,
typing that or saying it versus the right click file
blah blah. I think you're going to see that change
(25:07):
in interaction to more conversational interaction.
Speaker 6 (25:11):
I think, particularly chat based.
Speaker 4 (25:13):
We forget that the graphical user interface is just a metaphor, right,
It's not like the way computers work. It's just an interface.
And if chat is a better interface, people will use chat.
Speaker 6 (25:24):
And I think we're going to see that rarely explode.
Speaker 5 (25:26):
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 to get and orchestrate.
Speaker 6 (25:37):
That's a big part.
Speaker 5 (25:38):
So I think I see that happening and the appropriate
or associated productivity on locks. 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,
(25:58):
touching everything procurement. HR think about the advent of the
spreadsheet and how many different roles.
Speaker 6 (26:09):
It just ended up touching.
Speaker 5 (26:11):
And everybody can use or does use a spreadsheeting business
in some shape, size or form. So I think of
this as AI at scale. And so what it therefore
means from as you said, getting prepared, Well, it's all
about gaining first of all, the right understanding of the
technologies and part of what a lot we'll be talking
(26:31):
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 got 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
(26:53):
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, will be pretty.
Speaker 6 (27:02):
Different from how we work today.
Speaker 5 (27:04):
You see the seeds today but I would argue, think
of that now, like fully bloomed, it's a forest, not
a not a flowerbed, you know, yeah, yeah.
Speaker 4 (27:14):
Yeah, great, one other one other sort of loose thread
I wanna I want to return to uh. And that's
that's governance, right, you talked about governance and maybe just
just to help sort of set the table, like you
mentioned it in a broadway but narrowly, what does governance
(27:35):
mean in the context of IBM's work on enterprise A high.
Speaker 5 (27:38):
I think, as the Wood 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
(28:02):
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
(28:22):
do not use 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.
Speaker 6 (28:41):
To the policies.
Speaker 5 (28:42):
I've laid out that, in my mind, is a lot
of what governance is about. And in the context of AI,
it always tends to I think structure around three key
areas data where did it come from? And what did
I do with it? And how did I apply it?
And where did I use it? And then usage what
(29:04):
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 they answered that
question is yes or no? And manage that through? And
then importantly so this is then to bridge to regulation.
If you take a look at what's going on in
(29:24):
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
(29:46):
then back to my earlier comments, how that AI is performing.
Speaker 4 (29:50):
And when you talk about governance, how do you make
sure that you have the governance you need without inhibiting innovation?
Speaker 6 (29:58):
I think what is.
Speaker 5 (30:01):
And this is key a key design point for what
we're doing with what's the next is how you make
governance seamless institute versus another activity that you do right,
And so our goal is to try and drive that
kind of seamless interactions or value add in terms of governance,
(30:25):
so that when oh, let's pull through the history right
of everything we've done here, or 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
bringing in your data sets without saying, oh, before I
(30:47):
do that, I need to make sure I run this checker.
And now you can kind of bring it in systems
kind of automatically categorizing it, and then you can go
in and later verified, validate, or explore say I'm no
longer going to take this path based upon these facts.
Speaker 6 (31:02):
I think.
Speaker 5 (31:02):
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 you're to your point,
gain our governance needs or supports the governance needs of
our customers without stifling the innovation of the individuals at
the glass trying to think through, iteratively, think through new
(31:27):
value ways to do work.
Speaker 4 (31:30):
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.
Speaker 6 (31:37):
I think we covered quite a lot of true.
Speaker 5 (31:40):
No, I think we we covered the bases there.
Speaker 3 (31:45):
Earlier, Green mentioned that we are at an inflection point
in AI technology. Implementing AI in business will get easier,
and AI platforms like watsonex can empower even the largest
enterprise businesses to reinvent the way they run. As Greem said,
in the same way the spreadsheet took over business operations,
(32:07):
the adoption of AI at enterprise scale could be just
as ubiquitous. It's not an overstatement to say that a
new era of work may be upon us. I'm Malcolm Gladwell.
This is a paid advertisement from IBM. Smart Talks with
IBM is produced by Matt Romano, David jaw Nisha Venkat
(32:30):
and Royston Deserve with Jacob Goldstein. We're edited by Lydia
gene Kott. Our engineers are Jason Gambrel, Sarah Bruguier and
Ben Tolliday. Theme song by Gramoscope Special thanks to Carli Migliori,
Andy Kelly, Kathy Callahan and eight Bar and the eight
Bar and IBM teams, as well as the Pushkin marketing team.
(32:52):
Smart Talks with IBM is a production of Pushkin Industries
and Ruby's studio at iHeartMedia. To find more push can
podcast listen on the iHeartRadio app, Apple Podcasts, or wherever
you listen to podcasts.
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new season of the Smart Talks with IBM
podcast series.
Speaker 2 (00:09):
Today we are witnessed to one of those rare moments
in history, the rise of an innovative technology with the
potential to radically transform business and society forever. The technology,
of course, is artificial intelligence, and it's the central focus
for this new season of Smart Talks with IBM.
Speaker 1 (00:25):
Join hosts from your favorite Pushkin podcasts as they talk
with industry experts and leaders to explore how businesses can
integrate AI into their workflows and help drive real change
in this new era of AI. And of course, host
Malcolm Gladwell will be there to guide you through the
season and throw in his two cents as well.
Speaker 2 (00:43):
Look out for new episodes of Smart Talks with IBM
every other week on the iHeartRadio app, Apple Podcasts, or
wherever you get your podcasts, and learn more at IBM
dot com slash smart talks.
Speaker 3 (00:57):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. 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
(01:19):
what it means to leverage AI as a game changing
multiplier for your business. Our guest today is Kareem Yousef,
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 and how they can
deliver effective and compelling customer experiences with the current boom
(01:45):
and 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 productivity at new orders
of magnitude. In today's episode, you'll hear Kareem explaining how
AI powered by foundation models can make AI adoption by
enterprise businesses even easier, how generative AI will change the
(02:09):
way businesses process data and make decisions, and how these
considerations influenced the design of Watson x, 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,
(02:32):
and was a longtime host of the NPR program Planet Money. Okay,
let's get to the interview.
Speaker 4 (02:41):
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?
Speaker 5 (02:49):
Ah?
Speaker 6 (02:49):
Hi, I'm Kareem Yusuf.
Speaker 5 (02:51):
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.
Speaker 4 (02:59):
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 chat GPT and you know,
particularly in how it's used in the very kind of
consumer facing way. But I'm curious what is the rise
(03:19):
of chat GPT and you know, AI more generally, what
does it mean for business?
Speaker 5 (03:24):
Well, you know, it's if you kind of step back
and think about what really happens. You know, in a business,
you're really talking about a set of processes, right, you know,
activities that represent what a business needs to get done,
whether it's product, they produce and then sell or service
that they provide. And inherent to operating the business, I
(03:46):
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 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
(04:07):
sense of a lot of the data associated with driving
the 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.
(04:29):
So that's kind of my simple way of thinking of AI,
and we can obviously coloring with examples, but that's my
simplest way of thinking about AI. When you think about
in the business context, gain insights from masses of data
to support decisions and then drive actions.
Speaker 4 (04:44):
That's a really helpful framework. And then if we think
about sort of what's happening in the world now with
you know, enterprise businesses NAI, what are you seeing with
enterprise adoption of AI?
Speaker 5 (04:56):
At this moment, so we're really talking about most a
tale of two periods. So let me first of all
kind of take you back before the advent of what
I would call generative AI and the whole chat gpt
to what has been going on in what I would
term the realm of more standardized machine learning models. A
(05:17):
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
(05:41):
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,
(06:04):
whether you think of that in the context of application
resource management for IT or in the context of supply chain.
These have been very classical applications of machine learning AI
to really make sense of the data and provide a
basis to drive decisions. Now, what is characterized by all
(06:24):
those examples of given and the state of the art
of that kind of technology has always been it's very
task specific. So there was a air quotes, if I may,
kind of limitation in the sense that the tar it
had to be very task specific. And so we've seen
(06:45):
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
four has been this discussion of generative AI, which is
powered by a very specific innovation, this notion of foundation models.
(07:06):
And in the simplest way to think about it, it's
about training this large model that can then be refined
to various tasks. And the easiest one that everybody recognized
at the moment is the notion of a large language model,
a model that has an understanding of a lot of
(07:28):
the elements of a language such that it can be
refined to a variety of tasks. Write an essay, answer
a question, singer songs, so on, answers so forth. I
like to liken the power if you like, and this
will speak to the why everybody is so excited about it?
Speaker 6 (07:47):
Why would argue at an inflection point?
Speaker 5 (07:49):
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
(08:12):
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, thinking
of doing a poem, doing an essay, so on and
so forth, and so in the enterprise context, that means
(08:33):
that we're now talking about being able to unlock even
additional value at scale because of the nation of nature
foundation models and their appeal to generative use cases. Generative
in this case meaning creation of new content.
Speaker 4 (08:50):
So let's talk about Watson x. IBM recently announced what's
an X Just first of all, what is that? What
is what's an X?
Speaker 5 (08:58):
Well, what's an extra to our is our brand for
our platform, the WHATSAP 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 while,
it leads you to the components of what's next and
to a set of use cases. So let me paint
(09:19):
a few quick pictures for you here. What's anex 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 multi model world,
right and especially when you think about it in the
(09:40):
context of business. Models are going to come from various sources,
the ones we supply, the ones out there in open source,
and so of you. But there are activities you need
to do around these models to as I said, apply
them to your use case.
Speaker 6 (09:55):
And we'll talk about use cases in a bit. So
what's next.
Speaker 5 (09:58):
The AI is that vironment that build a tool if
you like, for being able to do those manipulation of
models to meet your specific use case. Thinks 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. Yeah, the second component
(10:20):
is dot data. So what's the next dot data is Essentially,
a next generation data store is based upon something referred
to as an open data lakehouse 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,
(10:42):
tune it, to train it to your use cases. And
so we provide a very open data store that allows
all manner of data and formats to be brought through
to do that. And the third component is what's next
dot governance, And this is all about the framework and
the toolkit rep required to apply the right governance principles
(11:04):
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 why 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
(11:26):
or for regulatory needs as well.
Speaker 4 (11:28):
So this platform, the system that you're describing, I'm curious,
how is it different from the you know, the generitive
AI options that you know we've all been hearing about
sort of in the press.
Speaker 5 (11:40):
Well, I think it really comes down to the ethos
or the principles that first of all drive the work
that we're doing. The first I would fixate on is
being open.
Speaker 6 (11:52):
Right.
Speaker 5 (11:52):
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.
Speaker 4 (12:14):
And hugging Face, to be clear, is sort of the
open source AI kind of hub.
Speaker 6 (12:19):
That's right, that's correct.
Speaker 5 (12:20):
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
(12:43):
you might say what kind of use cases are we
talking about? I give you three very quick ones that
with our customers are focused on a lot of focus
around enhancing customer service use cases. Think of this as
chatbots or digital assistance that are further trained in more
and more information about what the company has to offer
(13:04):
or could be internal policies, external policies, and 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 as extremely
important for the enterprise right. Another important use case seeing
(13:24):
a lot of focused on what i'd call AI based
orchestration or automation of task 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
(13:44):
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 can do, their secret source or their
differentiated advantage. So once again, a platform that recognizes that
(14:05):
and 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 cod generation,
application modernization, you know, and people enhancing their skills, so
targeted becomes really important.
Speaker 6 (14:23):
Mentioned open, and I mentioned.
Speaker 5 (14:24):
Targeted, targeted to the business to the use cases that
they need to do underpinning that, then it's trusted. So
everything I gave you in those targeted use cases talk
about handling enterprise proprietary and specific data. We are trusted
in this regard right. We have been serving the business
(14:46):
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 to gain and apply, but even in the
way we allow our platform to be deployed in multiple
(15:07):
locations of 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
(15:33):
unleash the intelligence capabilities productivity of their own business. This
isn't about, oh, we've established a bunch of APIs that
you can ask questions. This is about how do you
craft what you need for your business to deliver differentiated
value to your customers, shareholders, employees with all the appropriate
(15:58):
protections as well well. And so there's a lot of
focus on what we've done with the platform and the
tool set to enable that, to enable what we like
to call AI value creators, not just consumers of AI value.
Speaker 4 (16:12):
When you were talking about basically enterprise adoption of AI,
you use the word trust, and I'm curious, you know,
what does trust mean in the context of AI and
the enterprise.
Speaker 5 (16:29):
I would kind of deconstruct trust along these k avenues.
If AI is about giving you insights to help you
support decisions, how do you trust what insights it's provided?
What data did it use? What did it consider based
(16:53):
upon that data that therefore led to the insight provided?
Why is this important? Why this notion of trust? Well, one,
you're about to make a decision, so you want to
understand the basis for a decision. It's no different than
(17:14):
me asking you something and then saying, okay, can you
explain you're working? Right, that would be a notion of
trust that we establish and a very natural interaction as humans, Right,
we do it all the time, right, So there is
that element. The other reason why it becomes important if
you're applying AI into business processes and therefore how your
(17:35):
business works, you want to make sure that you know
what biases are built in to any decision or not
or if the AI the model in effect is drifting
away from kind of the parameters that you would want
it to remain within, right or go trust and so
(17:59):
in many a 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 element of trust that I think is important in
this discussion. Who are you getting your AI from? That's
(18:24):
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?
Speaker 6 (18:38):
Well?
Speaker 5 (18:38):
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
(19:00):
without explicit opting 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?
Speaker 3 (19:27):
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 Green
(19:47):
and Jacob continue the conversation, Jacob asks some more practical
questions about how businesses can adopt AI into their own processes.
Speaker 4 (19:57):
Let's listen, how can businesses move toward integrating AI as
part of their core business model instead of, you know,
sort of as an add on on the periphery.
Speaker 6 (20:09):
It's funny, you know.
Speaker 5 (20:10):
My simple answer to that is it's actually the simplest
thing in the world. To do by thinking about your business,
thinking about your elements of differentiation, and then thinking about
how AI can help you extend expand those Right, what
(20:31):
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
(20:53):
think always becomes a very important one. Right, So, and
you can apply this in many context 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 task, think
about your business processes, think about where help is needed
(21:16):
or where new value could be unlocked, and then you're
applying AI as a tool to achieve that end.
Speaker 4 (21:23):
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.
Speaker 5 (21:42):
I think AI can help people be more creative at
work by automating the mundane to unlock your mind to
be able to focus on higher value. You know, I've
used a couple of times I've talked about deriving insights
from data right to drive informed decisions.
Speaker 6 (22:00):
If you can.
Speaker 5 (22:01):
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?
(22:24):
I think of its freeing us up to do more
of what we actually as humans do extremely well.
Speaker 6 (22:30):
Which is actually that creative thinking.
Speaker 5 (22:33):
Exactly 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.
Speaker 4 (22:46):
Say, what's a check But so let us modernize that.
Speaker 5 (22:54):
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 and add it all up,
or you could begin to use technology to improve that
experience so you can get more time to think about
(23:16):
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.
Speaker 4 (23:37):
So we've been talking basically about the present so far,
and I'm curious if you think about the future and
you think, you know, medium to long term, how do
you think AI is going to transform business? And you know,
how can people now business leaders now prepare for what's coming.
Speaker 5 (23:57):
So to an earlier common 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 at the cusp of being able to address
(24:17):
use cases at scale that were more challenging before.
Speaker 6 (24:22):
And so I do think.
Speaker 5 (24:24):
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
(24:45):
terms of an application or you think about it in
terms of you know, the interact with the website will
have conversational enabled interfaces from the analyst saying give me
the latest reports for the last three months, you know,
typing that or saying it versus the right click file
blah blah. I think you're going to see that change
(25:07):
in interaction to more conversational interaction.
Speaker 6 (25:11):
I think, particularly chat based.
Speaker 4 (25:13):
We forget that the graphical user interface is just a metaphor, right,
It's not like the way computers work. It's just an interface.
And if chat is a better interface, people will use chat.
Speaker 6 (25:24):
And I think we're going to see that rarely explode.
Speaker 5 (25:26):
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 to get and orchestrate.
Speaker 6 (25:37):
That's a big part.
Speaker 5 (25:38):
So I think I see that happening and the appropriate
or associated productivity on locks. 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,
(25:58):
touching everything procurement. HR think about the advent of the
spreadsheet and how many different roles.
Speaker 6 (26:09):
It just ended up touching.
Speaker 5 (26:11):
And everybody can use or does use a spreadsheeting business
in some shape, size or form. So I think of
this as AI at scale. And so what it therefore
means from as you said, getting prepared, Well, it's all
about gaining first of all, the right understanding of the
technologies and part of what a lot we'll be talking
(26:31):
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 got 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
(26:53):
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, will be pretty.
Speaker 6 (27:02):
Different from how we work today.
Speaker 5 (27:04):
You see the seeds today but I would argue, think
of that now, like fully bloomed, it's a forest, not
a not a flowerbed, you know, yeah, yeah.
Speaker 4 (27:14):
Yeah, great, one other one other sort of loose thread
I wanna I want to return to uh. And that's
that's governance, right, you talked about governance and maybe just
just to help sort of set the table, like you
mentioned it in a broadway but narrowly, what does governance
(27:35):
mean in the context of IBM's work on enterprise A high.
Speaker 5 (27:38):
I think, as the Wood 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
(28:02):
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
(28:22):
do not use 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.
Speaker 6 (28:41):
To the policies.
Speaker 5 (28:42):
I've laid out that, in my mind, is a lot
of what governance is about. And in the context of AI,
it always tends to I think structure around three key
areas data where did it come from? And what did
I do with it? And how did I apply it?
And where did I use it? And then usage what
(29:04):
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 they answered that
question is yes or no? And manage that through? And
then importantly so this is then to bridge to regulation.
If you take a look at what's going on in
(29:24):
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
(29:46):
then back to my earlier comments, how that AI is performing.
Speaker 4 (29:50):
And when you talk about governance, how do you make
sure that you have the governance you need without inhibiting innovation?
Speaker 6 (29:58):
I think what is.
Speaker 5 (30:01):
And this is key a key design point for what
we're doing with what's the next is how you make
governance seamless institute versus another activity that you do right,
And so our goal is to try and drive that
kind of seamless interactions or value add in terms of governance,
(30:25):
so that when oh, let's pull through the history right
of everything we've done here, or 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
bringing in your data sets without saying, oh, before I
(30:47):
do that, I need to make sure I run this checker.
And now you can kind of bring it in systems
kind of automatically categorizing it, and then you can go
in and later verified, validate, or explore say I'm no
longer going to take this path based upon these facts.
Speaker 6 (31:02):
I think.
Speaker 5 (31:02):
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 you're to your point,
gain our governance needs or supports the governance needs of
our customers without stifling the innovation of the individuals at
the glass trying to think through, iteratively, think through new
(31:27):
value ways to do work.
Speaker 4 (31:30):
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.
Speaker 6 (31:37):
I think we covered quite a lot of true.
Speaker 5 (31:40):
No, I think we we covered the bases there.
Speaker 3 (31:45):
Earlier, Green mentioned that we are at an inflection point
in AI technology. Implementing AI in business will get easier,
and AI platforms like watsonex can empower even the largest
enterprise businesses to reinvent the way they run. As Greem said,
in the same way the spreadsheet took over business operations,
(32:07):
the adoption of AI at enterprise scale could be just
as ubiquitous. It's not an overstatement to say that a
new era of work may be upon us. I'm Malcolm Gladwell.
This is a paid advertisement from IBM. Smart Talks with
IBM is produced by Matt Romano, David jaw Nisha Venkat
(32:30):
and Royston Deserve with Jacob Goldstein. We're edited by Lydia
gene Kott. Our engineers are Jason Gambrel, Sarah Bruguier and
Ben Tolliday. Theme song by Gramoscope Special thanks to Carli Migliori,
Andy Kelly, Kathy Callahan and eight Bar and the eight
Bar and IBM teams, as well as the Pushkin marketing team.
(32:52):
Smart Talks with IBM is a production of Pushkin Industries
and Ruby's studio at iHeartMedia. To find more push can
podcast listen on the iHeartRadio app, Apple Podcasts, or wherever
you listen to podcasts.
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