Time now for our Wall Street week daily segment, the host of Wall Street Week
David Westin joining us now as he does every day at this time.
And David, which columnist through the end of earnings season?
And one thing we knew heading into this earnings season was every investor
wanted to hear what these companies had to say about artificial intel.
And you were right. We've heard a lot about it.
And what is your favorite subject, of course, Romania's artificial
intelligence. There's a big announcement out of IBM
today. Actually, they're having their think
conference there in the middle of that right now.
And to bring us up to date, not just on Watson, but on Watson X..
We welcome now Rob Thomas. He's IBM chief commercial officer said.
Rob, thank you for being with us. Coming out.
I know of the conference as a wanton ex. Explain to us what it is and why it's
going to make our lives better. David, look, something's happening and
I. Everybody knows it.
Every CEO I talk to says we're excited but were unsure of what to do.
We are really pleased to announce Watson.
It delivers what every company wants trusted, scalable, adaptable, a system
for building a high, working with what we call foundational models.
This is going to have a huge impact for businesses.
So it's very different, I understand, from getting chat GP team to write a
poem like Emily Dickinson and this is quite different from that.
Explain what a company would do with this.
What's an X, what he gets from IBM and then again, what it builds on top of
what it gets from Watson X? It is different because I don't think
people want their customers support in the style of Shakespeare as an example.
We're focused on business use cases like customer service, I.T., automation,
cybersecurity and Watson X gives you a capability for building models, for
training models, for using open source models.
We also have Watson next door data, a way to bring data and that is trusted
and understandable. And then lastly, we have Watson next
stock governance, which is about A.I. fairness.
Think of this as a nutrition label for your A.I.
to make sure that what you're putting into work in your business is safe,
reliable and trusted. Rob, Romania, there's been quite a lot
of talk not only about the promise of this technology, but at least right now
the costs, the relatively high costs for the end user and how they use this here.
When do we start to see a level of scale and more importantly, I guess a level of
adoption where maybe the cost becomes a little bit more manageable for the
companies that do want to use these tools.
What with what's an. We've actually absorbed the cost.
If you go back to 2020, we invested a lot of money to build the initial
foundational models and so clients no longer have to use that expense.
The Magic and A.I. comes with models.
Customer data, that's their proprietary data and then domain knowledge or
industry knowledge. So we bring a model that's already
trained to do something. Customer just has to bring their data so
they can get value much quicker because we've already absorbed the expense
upfront. How much discussion has been had about
where that data is coming from? There's been a lot of concern that
certain companies aren't just going to be using their own internally produced
data, but they're going to go out there into the world, into cyberspace or
wherever to scrape that data, data that in a lot of cases belongs to someone
else. This is a really important principle for
us, so we use data to train our models, that is IBM data.
So far our data customers bringing their data to the platform.
That is their data, that is their model. It cannot be used anywhere else, which
is one reason that we're able to avoid this phenomenon known as hallucination,
because we're training on very narrow use cases like how do you better support
customers? How do you better run your I.T.
system so we avoid those risks. But it is a risk for every company.
Which is why you need a capability like Watson next door governance, which
ensures that you understand where the data came from, how it's being used, how
it's making decisions that is going to give CEOs the confidence to give
foundation models and generative A.I. a try.
And their business. So, Rob, as a customer, it sounds good
that you've invested all this money, but you expect to make some money of it.
How do you get paid? Has IBM make us into a real business?
There's a couple different approaches. What's next is the platform.
Anybody can subscribe to the platform and start using it, so will earn money
that way. We've also built a capability in IBM
Consulting around its center of excellence for generative A.I..
So when clients start to work on this, they might need help with technology,
with data. They might need to work through the
change transformation of changing business processes in their organization
so we can monetize through software, the subscription to the platform, as well as
consulting for clients that want a little extra assistance as they bring
this into their company. Rob, you would've gotten this far and if
you hadn't talked to a few prospective customers, were you on the rollout?
When do you expect to actually see this in practice?
What sort of appetite is there out there?
Who are your first customers? We've been working with clients since
January of this year. Many are using it.
The big announcement we made last week was SJP, where SVP said they going to
adopt Watson as their embedded a high platform and they're shipping product
right now with it. So this is happening at the moment and
we'll continue from here. I think clients are still trying to
figure out what is the first use case that I use.
You take what we did with Watson originally around machine learning, deep
learning. We have clients like Verdasco who now
automate 300000 customer service calls a month and ninety five percent accuracy.
So machine learning was working. Foundational models is even better
because you can get up and running faster.
So this is happening right now. But what about for the world of finance,
Rob? Is there a use case there specifically
for Watson X and what you are and what you've guys have developed?
We've decided to go after what I would call horizontal domains, I.T.,
automation, cybersecurity, automating human resources, automating finance,
because this applies to every industry. And what we found is vertical use cases
tend to not be as scalable so they can become very complex and long running
projects. So we focused on domains that are
relevant to every industry. Every employee has companies.
Every employee has a CFO and an office of finance.
Most companies have a supply chain. So we're thinking about these horizontal
solutions that we know apply to every business.
Rob every powerful tool has the power to do great, good, but also maybe some harm
along the way, whether intentionally or otherwise.
Are you anticipating in your business while the possible government
regulation? We are working closely with Washington
to evaluate what is the best way to regulate this, because I think the last
thing anybody wants is unexpected consequences.
The key point is in this platform we have designed in governance.
So if you are using Watson X, the odds of something happening that you don't
expect. I would say are incredibly low.
But we will continue to work with authorities and governments around the
world to say what is the right way to regulate A.I.?
How do we prevent unexpected things from happening?
I think it's a very important topic for all of us.