>> Narrator: On theCUBE Studios in Palo Alto in Boston,
it's theCUBE
covering IBM think,
brought to you by IBM.
>> We're back and this is Dave Vellante
and you're watching theCUBE.
And we're covering wall-to-wall,
the IBM 2020 Think Digital Experience.
Rob Thomas is here.
He's the Senior Vice President
of the Cloud and Data Platform.
Rob, always a pleasure to see you.
I wish you were face to face,
but hey, we're doing the best we can.
>> As you say,
doing the best we can.
Great to see you Dave.
Hope family's safe,
healthy, happy as best you can be.
>> Yeah. Ditto back out your Rob
and congratulations on the new role.
Look, you and theCUBE,
we've been riding this data wave for quite some time now.
It's really been incredible.
>> It really is.
And last year I talked to you
about how clients we're slowly making progress
on data strategy,
starting to experiment with AI.
We've gotten to the point now
where I'd say it's game on for AI,
which is exciting to see
and that's a lot of what the theme
of this year's Think is about.
>> Yeah, and I definitely want to dig into that.
But I want to start by asking you sort of the moves
that you saw and you're seeing your clients make
with regard to the COVID-19 crisis,
maybe how you guys are helping them.
And I'm very interested in what you see
as sort of the longterm and even quasi-permanent
as a result of this.
>> I would first say it this way.
I'm not sure the crisis is going to change businesses
as much as it's going to be accelerating
what would have happened anyway.
Regardless of the industry that you're in,
we see clients aggressively looking at
how do we get to digital faster?
How do we automate more than we ever have before?
There's the obvious things like business resiliency,
business continuity,
managing the distributed workforce.
So to me,
what we've seen is really about an acceleration,
not necessarily in a different direction,
but an acceleration on the things
that we're already kind of in the back of their minds
or in the back of their plans
now that as we'll come to the forefront.
And I'm encouraged because we see clients moving
at a rate and pace that we'd never seen before.
That's ultimately going to be great for them,
great for their businesses,
and so I'm really happy to see that.
>> You guys have used Watson
to really try to get
some good high fidelity answers out to the citizens.
I wonder if you could explain that initiative.
>> Well, we've had this application called Watson Assistant
for the last few years.
And we've been supporting banks,
airlines, retailers,
companies across all industries
and helping them better interact with their customers.
And in some cases, employees.
We took that same technology
and as we saw the whole COVID-19 situation coming,
we said, "Hey, we can evolve Watson Assistant
"to serve citizens."
And so we started training the models,
which are intent-based models in Watson Assistant
on all the publicly available data
from the CDC as an example.
And we've been able to build a really powerful virtual agent
to serve really any citizen
that has questions about COVID-19
and what they should be doing.
And the response has been amazing.
I mean, in the last two weeks
we've gone live with 20 organizations,
many of which are state and local governments
but also businesses,
city of Austin,
children's healthcare of Atlanta,
state and local governments in Spain,
in Greece,
all over the world.
And in some instances
these clients have gotten live in less than 24 hours.
Meaning they have a virtual agent
that can answer any question,
that can do that in less than 24 hours.
It's actually been amazing to see.
So proud of the team that built this over time.
And it was kind of proof of the power of technology
when we're dealing with any type of a challenge.
>> You know, I had a conversation earlier
with Jamie Thomas about quantum,
and I was asking her sort of how your clients are using it
and the examples that came up were financial institutions,
pharmaceutical,
battery manufacturers,
airlines,
and so it strikes me
when you think about machine intelligence and AI.
The type of AI that you're doing at IBM
is not consumer-oriented AI.
It's really designed for businesses.
And I wonder if you could sort of add some color to that.
>> Yeah, let's distinguished the difference there
cause I think you've said it well.
Consumer AI is mart speakers,
things in our home,
music recommendations,
photo analysis and that's great.
It enriches all of our personal lives.
AI for business is very different.
This is about how do you make better predictions,
how do you optimize business processes,
how do you automate things
that maybe your employees don't want to do
in the first place?
Our focus in IBM
as part of what we've been doing with Watson
is really anchoring on three aspects of AI;
Language,
so understanding language because
the whole business world is
about communication and language.
Trust,
meaning trusted AI.
You understand the models,
you understand the data.
And then third,
automation.
And the whole focus of what we're doing here
in the Virtual Think Experience
is focused on AI for automation.
Whether that's automating business processes
or the new announcement this week,
which is around automating IT operations for a CIO.
>> You've talked over the years
about this notion of an AI ladder.
You actually wrote a book on it and,
but it's been hard for customers to operationalize AI.
We talked about this last year at Think.
What kind of progress have we made in the last 12 months?
>> There's been a real recognition of this notion
that your AI is only as good as your data.
And we use the phrase,
"there's no AI without IA",
meaning information architecture.
It's all the same concept,
which is that your data has to be ready for AI
if you want to get successful outcomes with AI.
And the steps of those ladder is around
how you collect data,
how you organize data,
how you analyze data,
how you infuse that into your business processes.
And we've seen major leaps forward in the last nine months
where organizations are understanding that connection,
and then they're using that
to really drive initiatives around AI.
>> So let's talk about that a little bit more.
This notion of AIOps.
I mean, it's essentially the,
take the concept of DevOps and apply it
to the data pipeline if you will.
I mean, everybody complains.
Your data scientists complain
they always spent all their time wrangling data,
improving data quality.
They don't have line of sight across their organization
with regard to other data specialists,
whether it's data engineers or even developers.
Maybe you could talk a little bit more
about that announcement
and sort of what you're doing in that area.
>> Sure. So,
Please let me put a number on it
because the numbers are amazing.
Every year organizations lose $26.5 billion of revenue
because of outages in IT system.
That is a staggering number when you think about it.
And, so then you say,
"Okay, so how do you break down and attack that problem?"
Well, you have to get better at fixing problems
or you have to get better at avoiding problems altogether.
And as you may expect,
it's a little bit of both.
You want to avoid problems obviously.
But in an uncertain world,
you're always going to deal with unforeseen challenges.
Though often the question becomes,
"How fast can you respond?"
And there's no better use of AI
than to do I hope you like those parts,
which is understanding your environment,
understanding what the systems are saying through their data
and identifying issues before they become outages.
And once there is an outage,
how do you quickly triage data across all your systems
to figure out where is the problem
and how you can quickly address it.
So we are announcing Watson AIOps,
which is the nervous system for a CIO.
They manage all of their systems.
What we do is we just collect data,
log data from every source system.
And we build a semantic layer on top of that
so Watson understands the systems,
understands the normal behavior,
understands the acceptable ranges,
and then anytime something's not going like it should,
Watson raises his hand and says,
"Hey, you should probably look at this
"before it becomes a problem."
We've partnered with companies like Slack,
so the UI for Watson AIOps is actually in Slack
so that companies can use
and employees can use a common collaboration tool
to troubleshoot or look at either systems.
It's really powerful.
Something we're really proud of.
>> Well, that kind of leads me to my next question,
which I mean, IBM got the religion 20 years ago on openness.
I mean, I can trace it back to the investment you made
in Linux way back then.
And of course the huge investment last year in Red Hat,
an open source company.
So you just mentioned Slack.
Talk about open ecosystems
and how that fits into your AI and data strategy.
>> Well, if you think about it,
if we're going to take on a challenge this grand,
which is AI for all of your IT,
by definition you're going to be dealing
with a whole ecosystem of different providers
because every organization has a broad set of capabilities.
We identified early on.
That means that our ability to provide open ecosystem
interoperability was going to be critical.
So we're launching this product with Slack,
I mentioned with Box,
we've got integrations into things
like PagerDuty service now.
Really all of the tools of modern IT architecture
where we can understand the data
and help clients better manage those environments.
So this is all about an open ecosystem
and that's how we've been approaching it from start.
>> It's really about data,
applying machine intelligence or AI to that data
and about cloud for scale.
So I wonder what you're seeing,
just in terms of that sort of innovation engine.
I mean, obviously it's got to be secure,
but it seems like those are the pillars of innovation
for the next 10 plus years.
>> I think you're right.
And I would say this whole situation
that we're dealing with
has emphasized the importance of hybrid deployment
because companies have IT capabilities on public clouds,
on private clouds,
really everywhere.
And so being able to operate that as a single architecture
is becoming very important.
You can use AI to automate tasks
across that whole infrastructure.
That makes a big difference.
And for your point,
I think we're going to see a massive acceleration,
boards, hybrid cloud deployments using AI.
And this will be a catalyst for that.
And so that's something we're trying to help clients with
all around the world.
>> You know, you wrote in your book
that O'Reilly published that AI is the new electricity
and you talked about problems
of not enough data.
If your data is on-prem
and you're only in the cloud,
well, that's a problem.
Or too much data.
How you deal with all that data.
Data quality,
so maybe we could close on some of the things
that you talked about in that book.
Maybe how people can get a hold of it
or any other sort of actions you think people should take
to get smart on this topic.
>> Yeah, so look.
Really excited about this.
Paul's a capitalist,
a friend of mine and a colleague.
We've published this book working with O'Reilly,
called The AI Ladder.
And it's all the concepts we talked about
in terms of how companies can climb this ladder to AI.
And we go through a lot of different use cases, scenarios.
I think anybody reading this
is going to see their company
in one of these examples.
Our whole ambition was
to hopefully plant some seeds of ideas
for how you can start to accelerate your journey to AI
in any industry right now.
>> Well, Rob, it's always great having you on theCUBE,
your insights over the years
and you've been a good friend of ours.
We really appreciate you coming on
and best of luck to you,
your family, your wider community.
Really appreciate it.
>> Thanks Dave. Great to be here.
And again, wish you and the whole CUBE team the best
and to all of our clients out there around the world,
we wish you the best as well.
>> All right. You're watching theCUBE's coverage
of IBM Think 2020, the Digital Event.
We'll be right back
right after this short break.
This is Dave Vellante.