WEBVTT

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Nipun, Erica,
thank you so much for joining us.

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Erica, this first question is for you.

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What's it truly mean to scale AI across
all corners of the business?

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So when we think about AI,
we're really thinking about the future of

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work and the way that work happens.

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And if we think about work across the
enterprise,

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it means that work is flowing seamlessly
across functions, employees,

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and customers.

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They don't have to worry about who's
doing the work,

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they just simply know that the outcomes
they want are being delivered.

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And when you put AI into that,
it means it's getting delivered with

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incredible efficiency and effectiveness.

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It means that we have teams of agents and
humans working together seamlessly as one

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collective team to deliver that employee
and customer experience regardless of

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what functional area the work is actually
targeted in.

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And that's what a true enterprise AI
strategy looks like.

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Nipun, What are the challenges
you're seeing and hearing

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from clients when it comes to scaling AI?

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Yeah,
I loved Erica's response on the

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enterprise AI strategy.

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A lot of our clients are investing in
this.

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This is a top strategic priority now for
them.

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But even though three and four are saying
it's a top strategic priority,

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only one in four of them are truly
getting the value at this point.

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So the investments have arrived with the
value and the impact is trailing.

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And when we look at what's different
between the leaders and the rest, we,

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we see a few key themes that emerge,
right.

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The first one is they are very focused on
where they're going deep versus going

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broad with the set of use cases that
they're targeting in terms of the value

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that they want to go after from AI.

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Second, they are very, very focused on
making this a high stakes transformation.

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So very clear-eyed about the value that
they're going to target and then

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rigorously tracking it and,
and making sure the operational outcomes

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are there.

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Third, they're embracing what we call this

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10/20/70 principle,
which is 10% of the focus is on the

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algorithms and the models, 20% is on the,
you know, the data and the technology.

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There's lots of challenges there,
but 70% of it is truly organizational

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change, right?

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What processes do you need to change?

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How do the people need to embrace?

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And then lastly,
they're taking a bold vision, which is,

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it's not just incremental productivity gains,

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but they're also thinking about
reinventing their core functions with AI.

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They're thinking of bringing new offers
to market.

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So that's where I think there's a lot of
opportunity that we see in learning from

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the leaders and taking it to the rest.

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Erica, how do AI agents, workflow,
and data come together in practice?

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And how can organizations
connect these dots?

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And I love this formula,
AI agents plus data plus workflow.

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That is the ServiceNow advantage.

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And we think it's incredibly important
because if you think about agents and you

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just talked about productivity and
transformation of the function,

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if we really want to drive that, the agents
need to be able to work autonomously.

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That means the agents need to be fed
information continuously to educate them

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on how work should happen.

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And so for us,
the workflows that we have in ServiceNow

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that we've been building for decades
provide that level of intelligence into

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the agents.

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That's why we believe that our agents are
really going to be able to drive that

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transformation and get that
productivity value.

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But we also understand that when you're
driving work across the enterprise,

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the data's going to sit,
some of it in ServiceNow,

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some of it in other systems.

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And so the data layer and what we call
our workflow data fabric,

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allows us to take data from all systems
and bring it together with the data in

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ServiceNow to be able to activate that
through agents and then drive insights

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into action.

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Thank you both so much for being here.