Vaishali, Manoj, Welcome. Manoj, to you first.
How are you seeing AI and GenAI.
impacting networks today?
First and foremost, as you know, AI and GenAI
is transforming everything we know from customer care to how people write code,
how people write unit testing, and every facet of life is changing.
Specifically in the case of networks, it's fundamentally changing the things
which we used to do manually.
Everything is getting automated.
Everything is becoming more self-driving.
You get to figure out things before they happen,
you find the problems before they happen, and pretty much 90% of the trouble
tickets vanish because of the AI factors involved.
So this is a genuine revolution which is happening in networks because networks
used to be incessantly complex before, tough to manage, tough to operate, everything.
But that's going to become much more simple with new AI-driven products coming
from companies like ours at Juniper Networks.
Vaishali, what are some of the key challenges that
organizations face when it comes to GenAI deployment?
And I guess what can they do to overcome those?
It's a good question, Georgie, because as Manoj said,
you can use it on so many different fronts.
But at least I find there are two or three things that differentiate what I
call the leaders from the laggards.
And I would say the first is
picking a few focus areas.
And note I'm not using the word use cases, but domains that you really want to apply
AI and GenAI.
to and really make a difference there.
That's I would say the first point.
The second is it's not just about technology for technology's sake.
Having some financial and operational outcomes is quite key.
And we find that only 20% of companies actually do that within a time-bound way.
And the third is it is 10% about, you know, the algorithms,
it's 20% about the tech and so on.
But it's 70% about driving the change.
And if you don't drive the change, then you know,
you don't get really the impact.
So in my mind, if we have these three things,
it would make a big delta, whether it's a network or in any other
domain that we pick.
I just want to pick up—why were you so adamant about saying not use cases?
Because, you know, everyone defines use cases in their own way.
And it can be this small or it can be this big,
and you can then have a scatter plot across the whole organization.
And then, you know, it loses the focus.
So you'd rather pick a domain, and then whether it's two use cases, ten,
five, whatever, you really are revolutionizing or
reshaping or reinventing a domain.
I would like to chime in a little bit because use cases tend to focus you on a
microcosm, you know, because it's a defined thing.
By definition, GenAI
and AI can relieve you from those, you know, constraints.
So if you look at the entire domain, you kind of look at a fresh way of doing
things, you know, just because it was done a certain way
before, that's not the same way it's going to be
done with AI.
So that's the opportunity.
How can business leaders then balance the need or the desire for GenAI
innovation but with sustaining critical network infrastructure?
How do you make that balance?
In some ways, I think they're closely tied together.
I think like Vaishali said, if you embrace AI and GenAI especially..
you start to think about things differently.
So instead of thinking about your fundamental critical business problems as
completely separate from enabling AI and GenAI,
if you think of them together, you may have new ideas sprouting.
Having said that, every business has some critical aspects
they have to continue to do well at or maintain.
But the more you embrace newer ways of doing things like GenAI,
you will find newer ways to tackle the same old problems.
For instance, you know, in our business at Juniper Networks,
by embracing AI and especially GenAI,
now we have AIOps managing almost every aspect of the network flawlessly,
which used to be done by hordes of people, people sitting in the network operating
center and doing that stuff.
So we simplified things, which means people have the freedom to do
other stuff, better work, where they can use their intellectual
horsepower better, rather than doing the mundane work.
So without actually combining the two, we wouldn't have been able to come up
with those answers.
So I would say think of them together rather than think of them separately.
And from my perspective, I would say there are so many things that
can be used and applied and solved that you don't have to go for your most
critical part of the process and say I'm going to apply AI and GenAI there.
There is so much else that can be first, you know,
adapted and revolutionized before you jump into, you know,
the most critical part.
So in my mind there is always a balance that can be found while still making
great strides and moving forward.
Vaishali, Manoj, thank you so much.
Thank you.