How to Get Your Team to Actually Use AI at Work
Why do enterprise skills programs always look great on paper, but break down the moment real work happens?
In the most recent HR Leaders Podcast episode, I had an inspiring conversation with Josh Newman, VP of Skills and Talent Readiness at ServiceNow.
He explains why organisations must shift away from perfectionist data collection and embrace good-enough skills capture paired with real-world talent readiness.
By moving training out of LMS silos and leveraging ambient data signals alongside AI simulation playgrounds, ServiceNow tracks true human capabilities in real time while keeping human judgment at the centre of workforce strategy.
5 things you'll learn from this episode:
The single mistake enterprise leaders make with skills data that completely stalls momentum
How ServiceNow silently tracks employee capabilities without relying on LMS course completions
The interactive playground framework replacing traditional, tick-box learning certificates
Why tracking AI log-ins is useless and the hidden metric that actually proves true ROI
The 4-word phrase employees use that instantly signals a high-performing AI culture
What if AI agents were not just another way to create more learning content?
What if AI agents could help talent teams find real skills gaps, build the right intervention, and deliver it at the moment employees need it most?
That is what Arist is building with the first end-to-end AI agent for enablement.
Instead of relying on slow needs analysis, outdated content libraries, and one-size-fits-all training, Arist helps organisations move from guessing what people need to identifying real gaps in real time.
Its AI agents can interview employees, uncover talent gaps, create personalised learning, and deliver it directly through tools like SMS and MS Teams.
Because the future of enablement will not be built by more content or more manual workflows. It will be built by teams that can spot business needs faster, deliver learning that fits the moment, and prove impact.
With Arist, AI agents become a way to close skills gaps faster, improve performance, and help employees get the right learning before the business has already moved on.
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Even though I have the word skills in my title, I am a skills skeptic, and that is
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not because I don't believe it's valuable to capture skills.
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In fact, I think it's utterly critical with the vision of
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readiness. However, I think for the past 15, 20 years,
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enterprise has spun their wheels and not got the ROI out
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of tools, the consulting around what it means to become a skills-based
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organization. I do think with the tools we have access to
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today, it is more possible than ever.
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But at the same time, if we are too
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stuck in the dirt, we will lose the forest.
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If we're too stuck on getting the data exactly right on the supply
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side of skills, we're going to get stuck, and we're going to spin our wheels, and
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we're going to lose that trust and momentum within the
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organization. So at a certain point, we need to be okay
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with good enough from a skills capture perspective and figure
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out where we want to be the most confident in specific
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skills.
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Hey, Josh. Welcome to the show, my friend. How are you?
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Good, Chris. Thanks for having me.
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Nice to see you again. I feel like we just only spoke yesterday,
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to be honest.
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It was only a few weeks ago.
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Yeah.
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Which feels like yesterday.
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Doesn't it feel like the-- I feel like on the first time we chatted, I was like, I
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wish we just hit record
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on that call.
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We should play it back.
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We're doing a replay. Live action replay
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as well. And we found out even between now and then that we seem to know so
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many people in common, have so many people in common.
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That's easy because you know everybody.
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I never thought about it that way. I suppose that does make sense because my job is
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to literally speak to anyone and-- not anyone, everyone I can in the HR
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profession that's doing amazing work.
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And you happen to have worked with a lot of those amazing people
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as well.
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I've been very fortunate to work with a lot of amazing people in the space.
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Yeah. Before we jump in, tell everyone on that note a bit about your
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background and sort of-
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Yeah
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... the journey to where we are now in the exciting role that you have at
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ServiceNow.
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Yeah.
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I'll start with where we're at today, and then I'll
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jump back and talk through the path a bit.
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So currently, I am VP of Skills and Talent
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Readiness at ServiceNow. It's a new practice that we are building
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out to understand
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if organizations, including our own, are ready for what
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comes next, and we can spend the rest of our time digging into that.
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But how I got here, I like to start--
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I've been in my role for four months, so I've done this spiel around
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how I got to where I'm at several times over the past few months.
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I studied journalism at university, and I start there
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because I think there was a
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spark around this concept of
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curiosity as a skill and being able to leverage curiosity
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to
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find answers to questions. Now, I don't know
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that individuals are inherently curious or
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not. Maybe they are. But I do know that curiosity can be
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honed as a skill, and
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looking back, it's highly valuable.
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Well, in my own career, it is proving and
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continuously proving to be highly valuable as a skill.
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My first role out of school, I jumped into human capital consulting
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before I knew what human capital meant and before I knew what consulting was.
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Yeah.
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We were focused on employee engagement and behavior change using
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technology
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and innovative methods. I was often
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the millennial in the room,
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being brought on to talk about our clients' social media presence,
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and then therefore, what we could do with that information
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to affect employee behavior change.
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Mm-hmm.
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So I spent about the first third or so of my career in consulting,
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moved on to the employee experience space at
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Walmart as my first in-house enterprise role.
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We were focused on developing the first enterprise value proposition for
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Walmart, which launched to 2.3 million people globally.
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That gave individuals an identity
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to breathe life into. And the
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way that that was developed was quite interesting, where we set the
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flag of the story we wanted to tell further out than
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we knew what was possible to deliver for employees in the moment,
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so that as we started redesigning specific moments that
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matter across the employee journey, we were aiming towards that
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goal, that flag that was further out, pulling ourselves forward.
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And that is sort of a method that I've seen repeated by
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strong leaders over and over in my career, and we're trying to replicate
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that-
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I like it
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... here where we're at now.
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Most recently, I was at WPP, a large advertising holding
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company,
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London-based, you know them well-
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Yeah
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... where I was leading people strategy and employee experience, a big focus
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on the future of work and understanding AI's impact on the workforce.
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And now,
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I'm at ServiceNow, where we have--
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It feels like a playground of data and
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infrastructure and possibility with the kind of work that I
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absolutely
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love doing and thinking about, and fortunate enough
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to have been offered the opportunity to lean into this
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and to figure out, with the help of an
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incredible team, what it means
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to help organizations, including our own, become ready for the future.
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Amazing. I feel like all of those, that journey culminated into where
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we are now. And to your point, you probably planted your own flag even
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further down because there's no way you would've known where would it be today
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with AI, right? And the transformation-
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I don't-
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... journey
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that we've been-
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Yeah, I don't think this was a role in anyone's
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ATS
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a few years ago.
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Yeah. Exactly, 100%. I love the idea
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of the analogy you used there to flag though.
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Because
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what's the value in that versus what currently happens, which is people
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will plant the flag and are constantly moving it,
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versus-
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Moving the goalposts.
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Yeah, versus... I feel like, I don't know why, when you mentioned that, the first
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person that came to my head was Elon Musk.
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Because his flag is-
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Moving to Mars.
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Yeah.
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Exactly, right? So he's known for not delivering it on
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his deadlines.
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But because he plants-
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However-
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Yeah. But his flag is-
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... the innovation that comes out of that-
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Yeah
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... is epic.
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Yeah, and he says that he does that on purpose to push and motivate his teams to
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achieve what others-
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Exactly
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... to achieve what others feel is impossible.
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Yeah. Having a bold vision is a really
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cool space to work within, right? And again,
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I've been really fortunate to work for innovative
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thought leaders within the HR space, within the people and talent
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space, who know how to articulate what that planted flag looks
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like, even if, one, we'll never get there, or two, we do need to
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move the goalposts.
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Yeah.
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But they're not going to move closer.
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If you set your sights out 10 yards out,
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you might get eight yards. If you set it 100 yards out, you might get 80.
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Mm-hmm.
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Right? So I think the question is how bold are you willing to
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be?
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Yeah.
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I love that, by the way, that you brought up curiosity because I feel like
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we never really spoke about curiosity in the past as a skill.
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Mm.
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Right? Whereas I feel like with now,
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with
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AI, agentic... Access to knowledge is now being democratized.
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And also, I thought a lot of the technical skills that seemed impossible in the
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past, like coding, you can now do with agents.
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And that opens up almost breathing room, I don't know if that's the right
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word, for innovation and curiosity as kind of like
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the power skills, I'm going to call them, because I don't like saying soft skills,
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to really be
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those that are going to be the most important that I see
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in the future. I don't know if that makes sense.
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It makes a lot of sense. We've
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a pretty strong perspective that AI is
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explicitly ushering in this human renaissance where
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the power of whether you...
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We don't call them soft skills per se, but because we do think they
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are quite powerful, right?
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That's why I use power skills. I stole that from Josh Bersin.
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Yeah. Power skills.
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Josh usually gets things right, so we can go with what Josh has.
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We call them human capabilities or human skills.
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Human capability. I like that one, yeah.
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But the human renaissance really, now more than
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ever, these capabilities that
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only humans can display and utilize,
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are the difference makers. To your point, when all else is
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democratized, that is the only difference.
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And things like curiosity and judgment and wisdom and
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accountability, these things, while in
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a role where we're trying to identify and capture
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signals that measure these things, it is the hardest part
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of the skill or capability measurement framework that we have.
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It is the most important and the most valuable and impactful when
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we are able to capture them.
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Yeah. It's going to be really exciting.
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And I'd love to hear your thoughts because we've seen just in the last,
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how about maybe I'd say six months, this shift from AI assistance
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to autonomous agents.
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Mm.
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We started with OpenClore, and now you've got Claude
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Cowork and Perplexity Computer, and all of a sudden there's this
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explosion.
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And it's just happened, and I would love to understand how
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you're navigating that shift at ServiceNow,
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and taking that into action, not just
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sticking with right now, which is what we currently do, which is take
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recommendations from AI.
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Yeah, absolutely. So there's a
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few ways we're operationalizing some of the most advanced
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capabilities.
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Within the ServiceNow platform, absolutely, we just
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announced that every product that is put out there will be AI
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native, AI first, with agents embedded.
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ServiceNow is developing as the AI control
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tower for business reinvention, which means that as
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organizations are adding all of these individual autonomous agents
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on the back end of how their processes and workflows are managed,
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organizations enterprise need a holistic view to ensure,
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one, that there's the proper governance, but also that they're working together
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in optimizing horizontality. Where I
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think right now, as an individual, you and I can
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sit and we can develop a mobile app with Claude
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Code, in a matter of literally minutes.
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It's got to be based on a good idea for it to be useful.
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But that still is vertical, right?
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So the question becomes, and what AI control tower ultimately
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does, is it turns the management of autonomous agents
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horizontal,
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and enables that visibility and transparency.
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So that's on the enterprise level, specifically within our
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world. So my team sits within our global learning
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function because we believe the first step- To closing readiness
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gaps is in individual skilling. There is
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also a big
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step from individual skilling to organizational transformation.
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So this concept of readiness is woven throughout the global people
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mindset and how we're developing our own processes.
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Within our global learning team, I know you've spoken to our
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head of learning, Janie Howson, recently,
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my boss. She leads ServiceNow
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University, which is the answer to this moment that we're
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in, for us, for our customers, and for our partners.
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So ServiceNow University is
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becoming AI first. What does it
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mean?
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ServiceNow University is not a traditional learning management system by any
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means. It is a system that offers
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up
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AI native coursework. It offers up
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agentic pathways, meaning that if we have
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all of these signals that our team is capturing about you as an individual
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and developing that Chris Rainey fingerprint based on our view,
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we can then do basically unlimited capability building
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and recommendations based on where you want to go, but based on also where we know
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you're at. From a skill presence perspective, do you
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have or not have a skill, and the level of proficiency within that
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skilling.
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Mm-hmm.
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So ServiceNow University is rolling out
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two new features. One is this AI learning guide, which is a real-time
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coaching
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companion in the flow of work, not within an LMS,
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but within the flow of work, as well as something that's super
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powerful, when it comes to developing skills, which is
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SimStudio. SimStudio
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is based on the idea that displaying a skill,
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showing that you have a skill is, I mean, it sounds obvious when you say it,
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way more impactful of a signal for us
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than just taking a course. Right? Using a
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skill in the flow of
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practice. And that's what both the AI learning guide and
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the SimStudio now available on ServiceNow University are
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offering.
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The people that are-- Oh, I've got so many questions now.
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So ServiceNow-
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There was my commercial.
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Yeah. No, but yeah. But I think everyone listening is kind of on that
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journey, right? That learning journey as we kind of...
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Right now, we have a lot of the tools that we could only dream of as learning
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professionals to create these experiences for our employees, right, that we've
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always wanted to do, and you described a few of those.
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Definitely the fact that it's embedding it in
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work itself. So I'd love to talk a little bit about that.
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But just to start for everyone listening, so ServiceNow University is there to
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service your customers and internally?
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So is it-
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That's right
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... so it's for ServiceNow customers, but also for employees.
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Is that-
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That's right. We have a goal of hitting three million
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learners by the end of 2027, and we're more than halfway to that goal.
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Why is that important? Because ServiceNow is a platform that needs to
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be utilized, developed, optimized within
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Enterprise, and there are many people within
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every organization who has their hands on the platform itself.
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So the more they're capable of utilizing the platform,
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the more their organizations are going to get out of it.
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Mm-hmm.
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So our learning strategy, our capability building strategy,
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and our talent readiness points of view hit
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on our employees, our customers, and our partners.
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Why was it so important to build the academy for your customers?
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Because that's something that other companies don't even do.
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Sure.
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It may sound very, a silly question, but I just wanted to understand
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why is that so important to you.
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Yeah. There's two aspects. One, and I'll underline the point
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from earlier, that we have millions and millions of people around the world
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who use ServiceNow as a platform, and the more they're able to
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have the ServiceNow University skills to use the platform, as
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well as the foundational digital
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transformation, AI fluency capabilities that you can learn within
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ServiceNow University itself, the more you're going
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to get out of the platform-
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Mm-hmm
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... and the better for business it is.
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The second part is that there is a capability gap in
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the market for ServiceNow skills.
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So organizations are looking for individuals within all sorts of
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departments, from HR to IT to marketing, who
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know how to utilize the platform.
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So interesting. So not only are you supporting them with your own solution, you're
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also upskilling and reskilling, and you're providing readiness for them in
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their own organizations.
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We're providing readiness, and we are fast working on
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helping them proactively understand how ready they are for what
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they have access to and are trying to deliver now-
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Yeah
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... and whatever comes next.
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No, I love that. When you mentioned that it's embedded in the flow of work, what
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does that mean? Does that mean embedded in Teams, in Slack, into your
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own agent? What does that mean?
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So there's a few elements, and I can talk about this more specifically
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within the realm of what we call talent signature, which again is that
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individual fingerprint that captures
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every skilling and learning interaction that an employee or
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customer or partner
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goes through. So talent signature is this
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sort of data layer. It's not anything anyone interacts with.
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And our objective is to capture signals across our skills taxonomy, which
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includes three buckets, functional skills, AI skills, and human
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skills.
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We cannot just purport to understand everything there is about an
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individual from their career profile, where they're validating their own
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skills or from the learning courses that they're taking.
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People are learning every single day and every moment on the job.
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I'm learning from this interaction Right?
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So on my profile, I should have the fact that I've checked the
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box on being a podcast guest. That is
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valuable to take forward. That's not happening on any sort of LMS
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currently
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at other organizations. But it is what Talent Signature
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captures. It captures every webinar people attend, and it
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captures
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those interactions that happen via performance management and
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coaching moments and mentorship.
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Some can happen in an ambient fashion, and some need to happen
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with a bit more manual work at this point.
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But our objective is to move to
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mostly, if not entirely, ambient capture of this
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data.
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Wow.
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So could an example be that, for
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example, if you're doing performance reviews, that the AI assistant's listening
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in as a note-taker and then providing feedback to that
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manager on how well that call went and maybe suggesting some
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learning in real time?
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There are plenty of external vendors and tools that do
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exactly what you're suggesting. We
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are very thoughtful about governance around all that-
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That's what I was going to ask
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... yeah.
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Yeah.
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Yeah. No, there's
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definitely potential for the creepy factor, and we are-
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... we're not going there. We're not monitoring-
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That was my next question around trust
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... people's individual messaging.
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Yeah.
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Trust is... Yeah. So we have within
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our team,
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within Janie's global learning and development organization and our talent
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readiness team, we have three rallying cries that we talk about, which is AI
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literacy for all-
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Yeah
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... which talks really about that three million learner goal.
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AI native learning, so this is moving to
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that always-on real-time coaching companion, as well as
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the SimStudio simulation playgrounds.
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And then the third one is exactly what you just hit on, closing the trust gap.
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Because we're in this moment that
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AI itself as a conversation topic is creating
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distrust-
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Yes
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... in many places. So that is a piece of it.
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But where there is low trust, there is lower productivity.
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That's been proven out. And if we can close
401
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that trust gap between individuals, their managers, the
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organization at every level,
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we're going to improve the employee experience and have better business
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outcomes.
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Yeah. That's something everyone's struggling with right now, even me in our small
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business. How do you keep AI decisions transparent
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and sort of
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maintain employee trust as that automation
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inevitably is going to continue to expand?
410
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If I can gush a little bit about the culture of ServiceNow,
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I don't know many places that are more
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open about AI use, right? So
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there are plenty of organizations out there that you hear
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through the grapevine, through great reporting,
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that individuals are hesitant to talk about their AI use,
416
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right? They hide it because there is
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potentially a perception that, oh, I don't want my
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manager or this person to know that
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it wasn't only me who produced this content.
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Mm-hmm.
421
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We have an extremely open culture, so we're often using
422
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language that makes it okay to
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jump in, dive in headfirst. For example, you'll often
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hear people via Slack or in a meeting say, "Hey,
425
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Claude and I developed this. It's not 100%
426
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there. Need to go through it with a fine tooth, but it gets us started."
427
00:22:10.722 --> 00:22:12.872
And just that, "Claude and I developed this"-
428
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It's so amazing
429
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... it opens the door, right?
430
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Yeah.
431
00:22:15.812 --> 00:22:18.972
It opens the door for, oh, that psych safety, right?
432
00:22:19.052 --> 00:22:21.112
Which is so important in closing that trust gap.
433
00:22:21.492 --> 00:22:25.372
Oh, it is okay for me to play and then say, "Hey,
434
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I reviewed it, so I know it's not AI slop."
435
00:22:30.642 --> 00:22:33.392
I'm not just throwing some s**t at the wall.
436
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But-
437
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Yeah
438
00:22:34.252 --> 00:22:37.951
... it's a work in progress, and I got to this point
439
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10X faster than I would've.
440
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I had a message today from my assistant this morning, Lisa, and
441
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I replied back saying, "Please don't continue sending those
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emails because I built an automation last night that's going to run for the next
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five days, so you don't need to do that anymore."
444
00:22:54.972 --> 00:22:58.752
Right? And when she called me, we had to have a conversation
445
00:22:59.552 --> 00:23:02.102
about it because I was like, I realized I didn't really communicate it clearly, and
446
00:23:02.152 --> 00:23:05.672
I was like, "Oh, I just found a way to completely automate that entire work." I
447
00:23:05.692 --> 00:23:08.392
think we worked out between us it was like 50 hours-
448
00:23:09.372 --> 00:23:09.452
Mm
449
00:23:09.592 --> 00:23:10.392
... of work.
450
00:23:11.552 --> 00:23:14.882
But I dismissed the value of it. Now I feel like
451
00:23:15.272 --> 00:23:17.902
some people won't tell you they used the
452
00:23:18.932 --> 00:23:22.452
agents because they feel like then therefore it's seen as less than-
453
00:23:22.812 --> 00:23:22.822
Sure
454
00:23:22.822 --> 00:23:26.792
... that outcome. Whereas now we need to almost change
455
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our view on recognition,
456
00:23:29.792 --> 00:23:32.742
of how we recognize when people are using these tools, because
457
00:23:33.612 --> 00:23:36.282
a lot of employees are seeing it as, oh, I'm not even going to share that I did
458
00:23:36.332 --> 00:23:38.252
that with Claude because it'll look like-
459
00:23:38.272 --> 00:23:38.332
Yeah
460
00:23:38.352 --> 00:23:40.872
... I was doing less work. Whereas I'm like, no, this is great.
461
00:23:41.772 --> 00:23:45.612
It's like we're optimizing, and the fact that you said that person communicated,
462
00:23:45.662 --> 00:23:49.272
"Me and Claude," is just amazing. I love the
463
00:23:49.352 --> 00:23:51.172
idea of that.
464
00:23:51.232 --> 00:23:54.872
I'll give you a cool stat that not only creates...
465
00:23:56.332 --> 00:23:57.972
Some people out there might think that
466
00:24:00.672 --> 00:24:04.512
closing the trust gap is a soft exercise, but creating that psych
467
00:24:04.552 --> 00:24:08.092
safety to talk openly about this scales to
468
00:24:08.212 --> 00:24:08.952
real-world
469
00:24:10.392 --> 00:24:14.252
success and business outcomes. So for our go-to-market
470
00:24:14.292 --> 00:24:17.802
team, our sales team, we were able to correlate that
471
00:24:18.416 --> 00:24:21.596
... the power users of not only AI
472
00:24:21.956 --> 00:24:25.876
overall, and we have enterprise Claude licenses here,
473
00:24:26.056 --> 00:24:28.696
but the AI sales coach built within Claude.
474
00:24:28.716 --> 00:24:32.116
So the power users of those specific tools
475
00:24:33.276 --> 00:24:36.946
attained their performance quota, their sales quota, at a higher
476
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rate than those who did not use it, or those who were lower in
477
00:24:41.136 --> 00:24:42.016
the usage stats.
478
00:24:42.436 --> 00:24:42.656
Yeah.
479
00:24:43.096 --> 00:24:46.976
So right there, that freedom, that openness, that
480
00:24:47.176 --> 00:24:50.256
safety to say, "Yeah, I'm playing around with this,"
481
00:24:51.396 --> 00:24:52.296
is driving results.
482
00:24:52.756 --> 00:24:54.776
Yeah. It's so interesting, right?
483
00:24:54.836 --> 00:24:58.716
Because a lot of times if you don't do that, the reality is that there will
484
00:24:58.776 --> 00:25:01.076
be people in the business that are using these tools anyway-
485
00:25:02.276 --> 00:25:02.416
Yeah
486
00:25:02.476 --> 00:25:05.126
... to get ahead. And I was an example of that
487
00:25:05.196 --> 00:25:08.676
about 10 years ago, this is before AI, but I had
488
00:25:08.736 --> 00:25:12.566
an assistant that was built into my LinkedIn.
489
00:25:13.496 --> 00:25:16.956
So I was a sales rep, two and a half hours on the phone, couple of hundred calls a
490
00:25:17.036 --> 00:25:20.436
day, call center, selling solutions to HR executives.
491
00:25:21.096 --> 00:25:24.866
And I found this Polish company had
492
00:25:24.936 --> 00:25:28.556
built this agent that plugs into your Chrome browser that can prospect
493
00:25:29.096 --> 00:25:32.276
LinkedIn 24 hours a day, 365 days a day, while I was sleeping.
494
00:25:32.356 --> 00:25:35.596
So I would wake up with conversations booked in my calendar with
495
00:25:35.616 --> 00:25:38.776
CHROs that they had no idea, they wasn't even chatting to me.
496
00:25:38.956 --> 00:25:39.066
It wasn't
497
00:25:39.916 --> 00:25:43.696
as smart as AI, but it was sending them messages on LinkedIn and setting up calls
498
00:25:43.736 --> 00:25:47.156
for me. And I'd had that in place for about three years.
499
00:25:47.776 --> 00:25:48.176
Mm.
500
00:25:48.216 --> 00:25:51.566
And I was doing four or 5X sales, and no one knew
501
00:25:51.676 --> 00:25:52.256
why.
502
00:25:53.616 --> 00:25:53.826
And
503
00:25:55.516 --> 00:25:59.456
as you can imagine, when I sat down with my boss in my performance
504
00:25:59.496 --> 00:26:01.576
review, they're like, "This is making no sense.
505
00:26:01.656 --> 00:26:04.936
You're doing half the amount of calls of everyone, half the amount of
506
00:26:05.636 --> 00:26:08.116
time on the phone, et cetera, but your sales are
507
00:26:09.396 --> 00:26:12.736
quadruple everyone else's." Because I was only talking to qualified leads
508
00:26:13.516 --> 00:26:14.176
because I had this-
509
00:26:14.196 --> 00:26:14.396
Tell me-
510
00:26:14.796 --> 00:26:15.346
... this agent
511
00:26:15.376 --> 00:26:17.666
... tell me that LinkedIn bought that capability-
512
00:26:17.956 --> 00:26:18.016
No
513
00:26:18.156 --> 00:26:18.676
... from a developer.
514
00:26:18.716 --> 00:26:21.896
They banned it. They actually blocked it.
515
00:26:22.056 --> 00:26:23.576
Yeah, they've blocked it. Yeah.
516
00:26:23.636 --> 00:26:24.346
That's even funnier.
517
00:26:24.436 --> 00:26:27.345
Yeah, they've blocked it. And I got a notification on LinkedIn being like, "We've
518
00:26:27.416 --> 00:26:28.616
noticed some suspicious activity
519
00:26:29.856 --> 00:26:33.576
on there." But the story is, when I did actually finally tell my team,
520
00:26:33.656 --> 00:26:37.056
unfortunately, unlike your incredible culture you have there, it was met with
521
00:26:37.066 --> 00:26:40.976
a defense straight away of like, "I can't
522
00:26:40.996 --> 00:26:43.616
believe you were doing that. You're lazy." I'm like, "No, I'm literally doing more
523
00:26:43.636 --> 00:26:46.686
sales than anyone right now, and I just found a better way." And by the way, and I
524
00:26:46.836 --> 00:26:48.856
was paying $80 a month myself-
525
00:26:48.896 --> 00:26:48.996
Mm
526
00:26:49.336 --> 00:26:50.396
... for my own salary
527
00:26:51.356 --> 00:26:53.656
for this product, to do it.
528
00:26:53.756 --> 00:26:54.156
Yeah.
529
00:26:54.236 --> 00:26:54.476
And-
530
00:26:54.776 --> 00:26:55.216
And look, I-
531
00:26:55.556 --> 00:26:55.706
Yeah
532
00:26:56.196 --> 00:27:00.056
... I understand the balance and the spectrum of
533
00:27:01.396 --> 00:27:03.216
openness to exploration, right?
534
00:27:04.196 --> 00:27:05.885
Certain regulated organizations-
535
00:27:05.926 --> 00:27:05.926
Sure
536
00:27:05.926 --> 00:27:07.706
... they're going to naturally be less.
537
00:27:08.206 --> 00:27:08.206
Yes.
538
00:27:08.216 --> 00:27:11.776
But you hear all of these amazing anecdotes from
539
00:27:11.876 --> 00:27:12.936
how
540
00:27:14.076 --> 00:27:17.776
banking organizations are transforming specific processes.
541
00:27:17.836 --> 00:27:20.456
I think I just saw one the other day, JP Morgan
542
00:27:21.396 --> 00:27:24.915
reinventing their legal process, with being AI first.
543
00:27:25.296 --> 00:27:26.636
Who would think, right, like that-
544
00:27:26.746 --> 00:27:27.016
It's huge
545
00:27:27.096 --> 00:27:28.676
... in such a regulated industry-
546
00:27:28.716 --> 00:27:28.796
Yeah
547
00:27:28.806 --> 00:27:29.616
... for a specific
548
00:27:30.696 --> 00:27:34.566
function of legal, that they're moving to an AI first way of
549
00:27:34.596 --> 00:27:34.956
working.
550
00:27:35.536 --> 00:27:36.996
Yeah. You mentioned how-
551
00:27:37.026 --> 00:27:37.596
So it's possible.
552
00:27:38.036 --> 00:27:41.856
Yeah. It is. We all thought
553
00:27:41.916 --> 00:27:45.376
that before COVID, that going remote would be impossible, and it happened
554
00:27:45.436 --> 00:27:45.816
overnight,
555
00:27:46.836 --> 00:27:47.296
right? So-
556
00:27:47.656 --> 00:27:47.945
There you go
557
00:27:48.096 --> 00:27:51.436
... you had orders come... I literally had a call just before lockdown with a CHRO
558
00:27:51.456 --> 00:27:55.336
that was like, "Yeah, this is our three to five-year plan to go remote." Literally
559
00:27:55.516 --> 00:27:58.636
a few days before lockdown, and we had a call a few months later-
560
00:27:58.656 --> 00:27:58.665
Yeah
561
00:27:58.665 --> 00:28:00.956
... and she went, "Chris, guess what?" And I was like, "What?" She was like, "We
562
00:28:00.996 --> 00:28:04.256
didn't need that three to five-year plan." It's all done, right?
563
00:28:04.636 --> 00:28:05.616
And it was just all like-
564
00:28:05.716 --> 00:28:07.516
It was a three to five-minute plan
565
00:28:07.776 --> 00:28:11.626
Yeah. It was all red tape that they'd always put up in front of
566
00:28:11.636 --> 00:28:12.226
themselves,
567
00:28:13.256 --> 00:28:13.406
right?
568
00:28:13.496 --> 00:28:14.096
Yeah.
569
00:28:14.196 --> 00:28:14.896
Internally.
570
00:28:14.936 --> 00:28:15.376
Yeah.
571
00:28:15.596 --> 00:28:16.856
Preconceptions.
572
00:28:18.356 --> 00:28:21.096
Look, I just want to say,
573
00:28:21.576 --> 00:28:23.196
this is why
574
00:28:24.036 --> 00:28:25.156
I believe that
575
00:28:26.176 --> 00:28:30.116
the possibilities with AI are in enterprise
576
00:28:31.136 --> 00:28:34.996
basically endless, but things are going to move slower
577
00:28:35.076 --> 00:28:36.336
in large organizations-
578
00:28:36.415 --> 00:28:36.576
Sure
579
00:28:36.636 --> 00:28:36.996
... than
580
00:28:38.415 --> 00:28:41.756
the headlines suggest because of exactly what you're describing.
581
00:28:41.766 --> 00:28:45.386
There are still humans running this company with hesitations and
582
00:28:45.416 --> 00:28:48.276
objectives and competing objectives, and there is
583
00:28:48.426 --> 00:28:52.186
traditional silos in organizations that are not connected,
584
00:28:52.656 --> 00:28:55.966
even if there is a possible system that can connect them.
585
00:28:56.336 --> 00:29:00.116
Yeah. I've been having this debate with our engineers, and even
586
00:29:00.156 --> 00:29:04.126
last night I was up very late with our CTO, Marvin, who was sitting there at
587
00:29:04.396 --> 00:29:07.236
1:00, 2:00 a.m. just scheming, coming up with ideas.
588
00:29:07.856 --> 00:29:09.716
And he was like, "But why can't we do that?
589
00:29:09.776 --> 00:29:13.296
Why can't we just move that fast?" And I was like, "Our enterprise customers aren't
590
00:29:13.456 --> 00:29:14.596
ready for this, Marvin."
591
00:29:14.996 --> 00:29:15.956
Yep.
592
00:29:16.016 --> 00:29:19.736
Because you have never worked in the learning or HR space, he doesn't
593
00:29:19.836 --> 00:29:20.656
have any of those,
594
00:29:21.496 --> 00:29:25.476
his backpack of beliefs or that
595
00:29:25.536 --> 00:29:29.376
invisible red tape that's there. So he's going crazy going, "No, we should just
596
00:29:29.416 --> 00:29:31.376
change this." I'm like, "No, it's too quick.
597
00:29:31.476 --> 00:29:31.736
We need to
598
00:29:32.576 --> 00:29:34.876
take people on the journey with us."
599
00:29:35.716 --> 00:29:36.076
Absolutely.
600
00:29:36.116 --> 00:29:39.596
And so I'm fighting against them because they're like, "But why do companies do
601
00:29:39.636 --> 00:29:41.676
this, Chris?" I'm like, "I know. I get it."
602
00:29:42.766 --> 00:29:46.236
"I know you can see a better way, but we have to slowly bring people on the
603
00:29:46.276 --> 00:29:49.836
journey. We can't just overnight just change what they know."
604
00:29:51.066 --> 00:29:54.916
And I know you want to do it, and we may even know that it's right, it
605
00:29:54.956 --> 00:29:56.876
doesn't mean we can just change it overnight.
606
00:29:57.916 --> 00:30:01.656
And this is the change management challenge with something like
607
00:30:02.476 --> 00:30:05.996
building out a talent readiness capability, not only for
608
00:30:06.016 --> 00:30:09.916
ourselves, but again, our customers and our partners, is
609
00:30:09.956 --> 00:30:13.926
that everyone is at a different level across the maturity curve, and
610
00:30:13.996 --> 00:30:15.996
that's got to be okay. That is a-
611
00:30:16.006 --> 00:30:16.026
Yeah
612
00:30:16.026 --> 00:30:19.496
... helpful constraint in our design principles.
613
00:30:19.876 --> 00:30:23.816
We should be able to articulate and offer up
614
00:30:23.896 --> 00:30:27.356
a view of readiness as it relates to an organization's
615
00:30:28.088 --> 00:30:32.028
... goals for the year. Not only what the organization can and
616
00:30:32.328 --> 00:30:33.808
should look like five years from now.
617
00:30:34.148 --> 00:30:34.248
Yeah.
618
00:30:34.368 --> 00:30:37.348
So we are working on practicality around that
619
00:30:38.768 --> 00:30:42.668
based on where different functions within ServiceNow
620
00:30:42.708 --> 00:30:46.488
are, but also, of course, the different levels of maturity across our
621
00:30:46.528 --> 00:30:47.028
customers.
622
00:30:47.248 --> 00:30:50.808
Yeah. Tell me a little bit more about, did you call it Sim, or did I just hear that
623
00:30:50.828 --> 00:30:51.048
wrong?
624
00:30:52.168 --> 00:30:53.068
Yeah. The Sim Studio.
625
00:30:53.128 --> 00:30:56.928
Sim Studio. Because I think that's something which, for the longest time is,
626
00:30:57.008 --> 00:31:00.488
traditionally we measure clicks, we measure
627
00:31:00.528 --> 00:31:01.198
completion-
628
00:31:01.438 --> 00:31:01.438
Yep
629
00:31:01.438 --> 00:31:02.188
... we measure,
630
00:31:03.328 --> 00:31:05.708
just because someone's got their little certificate at the end of the course
631
00:31:05.728 --> 00:31:09.688
doesn't mean they actually know how to, they don't actually
632
00:31:09.788 --> 00:31:11.818
have the skill, right? So like-
633
00:31:11.928 --> 00:31:12.068
Yeah
634
00:31:12.288 --> 00:31:16.138
... was that designed to simulate practice, get reps
635
00:31:16.188 --> 00:31:17.438
in? Talk to me a bit more about that.
636
00:31:17.468 --> 00:31:17.928
Exactly.
637
00:31:18.388 --> 00:31:20.948
I think that's fascinating. I think that's an area that I'm really excited about.
638
00:31:22.008 --> 00:31:24.628
Yeah. So I'll give you two examples.
639
00:31:25.148 --> 00:31:28.028
One, imagine you are learning how to
640
00:31:28.048 --> 00:31:31.748
perform a new IT workflow task
641
00:31:31.768 --> 00:31:35.068
within ServiceNow, within the platform.
642
00:31:35.348 --> 00:31:39.248
Having a sandbox for you to go get
643
00:31:39.308 --> 00:31:43.188
in real-time, click here, click here, do this,
644
00:31:43.768 --> 00:31:47.188
take away the training wheels, and then do it
645
00:31:47.208 --> 00:31:49.828
yourself. Do it as many times as you
646
00:31:50.748 --> 00:31:54.708
want to practice that routine, to learn the capability.
647
00:31:55.028 --> 00:31:59.018
You do that over and over with all of the relevant tasks with end
648
00:31:59.048 --> 00:32:01.928
processes within a role, and there's your
649
00:32:02.628 --> 00:32:05.708
simulation. Another example would be
650
00:32:07.168 --> 00:32:07.928
how we train
651
00:32:08.848 --> 00:32:12.588
our sales folks, our go-to-market team on, let's say the
652
00:32:12.648 --> 00:32:13.408
corporate narrative.
653
00:32:13.888 --> 00:32:17.768
Every so often, we roll out a new
654
00:32:17.828 --> 00:32:21.808
corporate narrative. What are the new products and capabilities and angles that we
655
00:32:21.828 --> 00:32:25.628
should be talking to customers about because of how the product is evolving, and
656
00:32:25.688 --> 00:32:27.488
because of how the market is evolving?
657
00:32:28.068 --> 00:32:29.268
We partner with
658
00:32:30.768 --> 00:32:34.348
a vendor who can provide simulation-based
659
00:32:34.788 --> 00:32:38.358
environments, playgrounds really, to go and get
660
00:32:38.408 --> 00:32:42.238
coaching through that practice of that corporate narrative in that
661
00:32:42.268 --> 00:32:46.028
case. Right? So you're practicing, you're having a live
662
00:32:46.768 --> 00:32:50.728
simulation with a bot, basically, even
663
00:32:50.768 --> 00:32:54.388
though it feels real. And then you're getting the pros, the
664
00:32:54.468 --> 00:32:57.148
cons, the feedback immediately after.
665
00:32:57.188 --> 00:32:59.728
You can go back and practice a million times over.
666
00:33:00.328 --> 00:33:03.788
And you're able to now connect all those into
667
00:33:04.708 --> 00:33:06.668
the data? Because I think a lot of times-
668
00:33:06.928 --> 00:33:07.868
Yes
669
00:33:07.878 --> 00:33:11.848
... a lot of these platforms sit in silos in organizations.
670
00:33:11.878 --> 00:33:11.878
Yes.
671
00:33:11.908 --> 00:33:15.048
So you've managed to bring that all together?
672
00:33:15.128 --> 00:33:17.108
Yeah. So think about this from a few ways.
673
00:33:17.148 --> 00:33:20.618
Thinking about our skills
674
00:33:20.688 --> 00:33:24.528
taxonomy across functional skills, AI skills, and human skills.
675
00:33:24.928 --> 00:33:28.208
Think about what you can learn from something like
676
00:33:28.748 --> 00:33:29.068
the
677
00:33:30.008 --> 00:33:33.628
workflow practice. With the workflow sandbox,
678
00:33:34.368 --> 00:33:37.768
you're learning those functional skills, and we're going to be
679
00:33:37.848 --> 00:33:41.788
capturing the signals that you are developing those skills as you
680
00:33:41.828 --> 00:33:44.248
get better at performing those tasks.
681
00:33:44.508 --> 00:33:45.028
Yeah.
682
00:33:45.468 --> 00:33:48.978
Take something like the corporate narrative simulation
683
00:33:50.648 --> 00:33:51.068
example.
684
00:33:52.868 --> 00:33:56.288
For someone who goes and practices that corporate
685
00:33:56.348 --> 00:34:00.188
narrative 10, 15, 20 times across a period of
686
00:34:00.248 --> 00:34:04.168
time, we can infer a skill like
687
00:34:04.208 --> 00:34:06.108
curiosity or resilience,
688
00:34:08.288 --> 00:34:12.028
and pull that into our system. So there's skills across our
689
00:34:12.048 --> 00:34:13.777
entire taxonomy that we can pull,
690
00:34:14.928 --> 00:34:17.629
or signals rather, that we can gather.
691
00:34:17.669 --> 00:34:17.819
And
692
00:34:19.268 --> 00:34:22.649
signals alone might not tell you something, but when you put them together,
693
00:34:23.328 --> 00:34:26.928
they paint a beautiful picture or a portrait of an
694
00:34:26.968 --> 00:34:30.468
individual. And you scale that out beyond an individual, and you
695
00:34:30.508 --> 00:34:33.629
get team readiness, function readiness, and org readiness.
696
00:34:33.868 --> 00:34:37.828
Yeah. I think this is one of the most exciting things that AI's allowed us to do,
697
00:34:37.968 --> 00:34:38.548
quite frankly.
698
00:34:39.388 --> 00:34:40.109
Like the ability to-
699
00:34:40.149 --> 00:34:40.488
Absolutely
700
00:34:40.609 --> 00:34:44.348
... I always use the analogy of my friends, I didn't learn how to ice skate
701
00:34:44.408 --> 00:34:48.208
by watching a YouTube video. You get on the ice, you practice, you fall
702
00:34:48.288 --> 00:34:52.188
over, you try again. And being able to simulate that over and over and over
703
00:34:52.248 --> 00:34:55.468
again. Sales is like, and my whole career has been sales, that's such an exciting
704
00:34:55.508 --> 00:34:58.328
one that I can practice pitching a client and get objections and-
705
00:34:58.348 --> 00:34:58.408
Mm
706
00:34:58.888 --> 00:35:02.088
... or understand our new products like you just said, and now in real-time
707
00:35:02.168 --> 00:35:04.468
practice pitching that
708
00:35:05.288 --> 00:35:09.188
to a CHRO avatar or whoever it may be on the other
709
00:35:09.288 --> 00:35:12.628
end and get that real-time feedback and go again.
710
00:35:12.648 --> 00:35:16.088
And then get on a call straight after and then
711
00:35:16.168 --> 00:35:19.588
implement that straight away. And of course, as an organization, is that you have
712
00:35:19.628 --> 00:35:23.388
that real-time visibility and capability engine right
713
00:35:23.488 --> 00:35:27.468
there. This upskilling people at the speed of business is just
714
00:35:27.508 --> 00:35:30.248
something that we could only dream of.
715
00:35:31.218 --> 00:35:34.848
And imagine with that simulation, your example of the sales
716
00:35:34.928 --> 00:35:37.508
practice, imagine if you can dial up
717
00:35:38.588 --> 00:35:40.628
the rudeness of a client-
718
00:35:40.978 --> 00:35:40.978
Yeah
719
00:35:40.978 --> 00:35:43.288
... or the amenability of a client.
720
00:35:43.348 --> 00:35:43.508
Yeah.
721
00:35:43.708 --> 00:35:44.348
Right? You can
722
00:35:45.268 --> 00:35:48.718
play around with how these things are programmed so that you're
723
00:35:48.748 --> 00:35:52.708
pushing the development of certain capabilities.
724
00:35:52.808 --> 00:35:56.168
Yeah. What advice would you give to leaders, and this is a question that's come up
725
00:35:56.488 --> 00:36:00.448
a lot recently, of how they can assess AI readiness and then act
726
00:36:00.508 --> 00:36:00.868
on it?
727
00:36:02.768 --> 00:36:06.628
AI readiness in particular. So I think there's two things we think about.
728
00:36:07.048 --> 00:36:10.168
One, there is fluency of AI, right?
729
00:36:11.068 --> 00:36:14.828
Anthropic recently released an AI fluency index.
730
00:36:16.188 --> 00:36:19.668
I'm a big fan of it. It's got maybe 26
731
00:36:19.688 --> 00:36:23.128
different factors that determine fluency.
732
00:36:23.588 --> 00:36:26.008
11 of those factors can be
733
00:36:27.368 --> 00:36:30.888
viewed in how someone utilizes, in
734
00:36:30.928 --> 00:36:34.808
Anthropic's case, Claude. So for example, if you
735
00:36:34.868 --> 00:36:38.740
are following up- ... on an initial response
736
00:36:38.780 --> 00:36:42.680
from your prompt, that is one of the 11
737
00:36:42.740 --> 00:36:45.400
indicators of fluency. So there are ways to capture this.
738
00:36:45.890 --> 00:36:45.890
Interesting.
739
00:36:45.900 --> 00:36:49.860
And actually, a fun exercise, if you go into, I'm sure you could do it
740
00:36:49.920 --> 00:36:53.060
with whatever your LLM of choice is, and say,
741
00:36:53.320 --> 00:36:56.780
"Do an analysis of
742
00:36:56.790 --> 00:37:00.580
my AI fluency based on Anthropic's index."
743
00:37:01.620 --> 00:37:05.530
It will give you a report based on those 11 behaviors that
744
00:37:05.580 --> 00:37:07.680
are measurable through LLM usage.
745
00:37:08.080 --> 00:37:11.080
And there's specifics for Cloud
746
00:37:11.760 --> 00:37:13.100
Cowork and Cloud Code,
747
00:37:13.900 --> 00:37:15.260
and just the chat.
748
00:37:15.320 --> 00:37:15.420
Yeah.
749
00:37:15.520 --> 00:37:19.140
So that's on the fluency side. Fluency translates
750
00:37:19.180 --> 00:37:22.560
pretty closely to adoption of tools, but
751
00:37:22.620 --> 00:37:26.040
adoption, this is where it gets interesting, is not the
752
00:37:26.100 --> 00:37:29.700
same as absorption. And I heard this, I don't want to
753
00:37:29.740 --> 00:37:32.930
misquote who I heard it from, but I heard it from... It is not my original idea.
754
00:37:33.480 --> 00:37:37.440
But the idea of adoption being different than absorption, I think is quite
755
00:37:37.480 --> 00:37:41.219
a powerful one to think through. What does it mean to play,
756
00:37:41.680 --> 00:37:43.240
and what does it mean to use-
757
00:37:43.520 --> 00:37:43.720
Mm
758
00:37:43.820 --> 00:37:45.740
... with purpose, right? So I
759
00:37:49.240 --> 00:37:53.160
occasionally speak with early in career talent or
760
00:37:53.360 --> 00:37:56.800
individuals in university thinking about what they want to do in school.
761
00:37:57.520 --> 00:38:01.120
And, I often, if not always ask, "How do you play
762
00:38:01.160 --> 00:38:04.900
around with AI, with the latest tools?" Some
763
00:38:06.100 --> 00:38:06.780
of these
764
00:38:08.320 --> 00:38:10.940
people that I speak to are far and beyond.
765
00:38:11.300 --> 00:38:14.920
It gives me hope that they will define- ...
766
00:38:14.930 --> 00:38:18.620
the future enterprise. But some are also like, "I
767
00:38:18.980 --> 00:38:22.820
use Snapchat filters and that's
768
00:38:22.880 --> 00:38:23.540
kind of
769
00:38:24.420 --> 00:38:26.740
my limit." But me and my friends use it for memes.
770
00:38:27.240 --> 00:38:30.240
We just send memes back and forth and use AI to do that.
771
00:38:31.380 --> 00:38:34.630
So that's definitely adoption, but is that
772
00:38:34.680 --> 00:38:38.520
applicable or how do you translate that to
773
00:38:38.580 --> 00:38:42.550
apply it to drive some sort of impact that you're trying to drive in your
774
00:38:42.600 --> 00:38:42.860
role?
775
00:38:43.540 --> 00:38:44.580
That's so fascinating.
776
00:38:45.700 --> 00:38:48.120
Right. It also reminds me of
777
00:38:49.520 --> 00:38:53.020
one of the things I try and focus on personally, but also talk to the team about
778
00:38:53.560 --> 00:38:57.160
is we're
779
00:38:57.200 --> 00:38:58.620
extracting a lot of knowledge
780
00:38:59.420 --> 00:39:00.100
from all these places.
781
00:39:00.160 --> 00:39:00.180
Mm.
782
00:39:00.220 --> 00:39:02.960
Infinite knowledge, right? You can ask anything, get any reply.
783
00:39:03.000 --> 00:39:06.920
But then there's almost overwhelming to the point that which paralyzes
784
00:39:06.960 --> 00:39:09.180
the teams to go and execute on the learnings.
785
00:39:10.140 --> 00:39:10.199
Yeah.
786
00:39:10.210 --> 00:39:14.090
So for me, my superpower is execution,
787
00:39:14.720 --> 00:39:16.550
so I won't spend so much time
788
00:39:17.520 --> 00:39:21.380
on the absorption of everything. I kind of just immediately move to execution and
789
00:39:21.480 --> 00:39:23.660
I learn quickly. I put my-
790
00:39:24.140 --> 00:39:24.150
Mm
791
00:39:24.180 --> 00:39:27.700
... MVP out there, I chat with the AI, what could I have done differently?
792
00:39:27.740 --> 00:39:31.280
I get feedback and then I just move into execution mode.
793
00:39:31.310 --> 00:39:31.700
And I think-
794
00:39:31.720 --> 00:39:32.100
Learn by doing.
795
00:39:32.220 --> 00:39:35.220
Yes, exactly. Learn by doing. And I see a lot of my team and even my friends, they
796
00:39:35.240 --> 00:39:37.980
get paralyzed into, "Everything has to be perfect.
797
00:39:38.040 --> 00:39:40.600
I've got to do all the research, and everything has to be planned amazingly.
798
00:39:40.630 --> 00:39:44.380
And only then when it's perfectly 100% ready can I go to execution." I'm like, "No,
799
00:39:44.600 --> 00:39:46.940
you're just spending so much time
800
00:39:47.790 --> 00:39:49.500
there. Go and just throw it out there, right?
801
00:39:49.540 --> 00:39:50.790
And do it." And
802
00:39:51.980 --> 00:39:55.060
a lot of the times, it's also a mindset shift of
803
00:39:55.900 --> 00:39:58.700
what questions to even ask is a skill in itself.
804
00:39:59.270 --> 00:39:59.270
Mm.
805
00:39:59.300 --> 00:40:00.240
Like I-
806
00:40:00.560 --> 00:40:01.500
Totally. That goes back to curiosity.
807
00:40:01.900 --> 00:40:03.640
Yeah, exactly. And I realized that with it.
808
00:40:03.740 --> 00:40:07.370
So yesterday we were building an agent in... We use Perplexity Computer.
809
00:40:08.540 --> 00:40:12.430
I was watching this team build an automation for our
810
00:40:12.460 --> 00:40:15.480
production for speaker acquisition for one of our events, and I was like, "Look,
811
00:40:15.520 --> 00:40:18.320
this is a manual task." And I saw the person
812
00:40:19.160 --> 00:40:20.980
manually add the Google
813
00:40:22.420 --> 00:40:24.060
Doc link to the data.
814
00:40:24.260 --> 00:40:24.400
Mm-hmm.
815
00:40:24.700 --> 00:40:26.580
Manually add the Google
816
00:40:27.560 --> 00:40:31.000
Sheet template, Google Doc template,
817
00:40:32.160 --> 00:40:35.720
write a huge prompt, and then hit
818
00:40:35.780 --> 00:40:37.760
Enter. And I was like,
819
00:40:38.600 --> 00:40:41.680
"The entire platform is integrated into all of our apps.
820
00:40:41.800 --> 00:40:41.900
You
821
00:40:42.860 --> 00:40:44.180
could have just said, 'Grab the
822
00:40:45.100 --> 00:40:46.860
Google Doc from here, grab the sheet from here.'"
823
00:40:47.360 --> 00:40:47.370
Yep.
824
00:40:47.370 --> 00:40:49.780
Do all that. And then at the end, they
825
00:40:51.500 --> 00:40:55.079
built it out. And I was like, "Just tell the agent to
826
00:40:55.480 --> 00:40:59.300
turn that into an organizational skill and share it with
827
00:40:59.400 --> 00:41:01.620
the rest of the production team." And they were like-
828
00:41:01.660 --> 00:41:02.400
Yep
829
00:41:02.410 --> 00:41:05.380
... "Is that even possible?" And I was like, "I don't know.
830
00:41:05.440 --> 00:41:09.340
Let's just try it." And it did. It built the skill, it sent it to the rest
831
00:41:09.380 --> 00:41:13.100
of the team in production, added it as one of the core enterprise
832
00:41:13.160 --> 00:41:16.890
skills in the business. And then we realized that it cost a lot of
833
00:41:16.900 --> 00:41:18.680
credits because that's now the new currency.
834
00:41:18.760 --> 00:41:19.380
Yeah, sure.
835
00:41:19.400 --> 00:41:23.190
And I was like, "Optimize this skill." And it kind of reduced it by
836
00:41:23.240 --> 00:41:25.200
about 75% of the credit spent-
837
00:41:25.300 --> 00:41:25.840
Wow
838
00:41:25.850 --> 00:41:29.780
... by optimizing how it was running the agent every single time,
839
00:41:30.300 --> 00:41:32.440
and then updated that skill across everyone's profiles.
840
00:41:32.860 --> 00:41:33.160
But it's like-
841
00:41:33.200 --> 00:41:36.830
And now your colleague knows that that is a possibility, right?
842
00:41:37.450 --> 00:41:37.560
Yeah.
843
00:41:38.380 --> 00:41:38.840
Well, not only that-
844
00:41:38.920 --> 00:41:39.320
So I'll give-
845
00:41:39.640 --> 00:41:43.470
We said for any skill that anyone in the business builds moving
846
00:41:43.500 --> 00:41:45.880
forward, run that same optimization for the credits.
847
00:41:47.400 --> 00:41:49.040
Save you money.
848
00:41:49.160 --> 00:41:49.280
Yeah.
849
00:41:50.240 --> 00:41:50.250
I mean...
850
00:41:50.270 --> 00:41:50.270
Yeah.
851
00:41:50.300 --> 00:41:50.400
Yeah.
852
00:41:51.200 --> 00:41:51.500
Even for-
853
00:41:51.580 --> 00:41:51.810
I'll give-
854
00:41:52.280 --> 00:41:52.570
Oh, sorry
855
00:41:52.720 --> 00:41:54.100
One other
856
00:41:55.100 --> 00:41:57.870
concept around adoption versus absorption.
857
00:41:59.680 --> 00:42:03.000
Having been in a few boardrooms and understand what
858
00:42:03.320 --> 00:42:04.820
C-suite leaders are talking about
859
00:42:05.800 --> 00:42:09.660
within large organizations, this concept of
860
00:42:09.700 --> 00:42:13.520
adoption is limiting, right? So tracking how many people
861
00:42:13.540 --> 00:42:14.850
are using the tools,
862
00:42:15.920 --> 00:42:19.220
like you might track the uptake of, I don't know,
863
00:42:19.520 --> 00:42:22.490
Excel when that came out- ... is not going to
864
00:42:23.120 --> 00:42:26.850
connect to ROI. But if you do track specific behaviors
865
00:42:27.860 --> 00:42:28.300
that are
866
00:42:30.360 --> 00:42:33.820
the leading indicators, if you will, from the adoption,
867
00:42:34.440 --> 00:42:38.200
the question around what is the ROI of AI is
868
00:42:38.800 --> 00:42:39.440
the wrong
869
00:42:40.580 --> 00:42:44.420
... question unless you're getting down to that level of behavior.
870
00:42:44.880 --> 00:42:48.830
So within a sales organization, you might
871
00:42:48.960 --> 00:42:52.700
track: Are individuals using, are sellers using
872
00:42:53.020 --> 00:42:56.980
Claude for account intelligence, for
873
00:42:57.040 --> 00:43:00.480
managing opportunities, to understand licensing
874
00:43:00.540 --> 00:43:03.920
analytics, right? There's all these specifics that you can get into.
875
00:43:03.980 --> 00:43:07.920
You just have to have that level of curiosity and truly data
876
00:43:08.060 --> 00:43:10.780
access and transparency within your organization-
877
00:43:11.180 --> 00:43:11.520
Yeah
878
00:43:11.820 --> 00:43:13.340
... to capture and do something with.
879
00:43:13.580 --> 00:43:14.340
Mm-hmm. And
880
00:43:15.400 --> 00:43:19.240
now being able to also embed this into the flow of work to the
881
00:43:19.860 --> 00:43:23.580
current communication tools that people are using is also now the barrier to
882
00:43:23.620 --> 00:43:25.880
entry. You don't even have to go into your agent.
883
00:43:26.470 --> 00:43:26.470
Yeah.
884
00:43:26.580 --> 00:43:28.460
You could just ask a question in Teams.
885
00:43:28.800 --> 00:43:28.869
It's all there.
886
00:43:28.869 --> 00:43:32.310
Ask a team in Slack, obviously your own agent that you have, which I know is very
887
00:43:32.360 --> 00:43:36.160
well, so many of the CHROs that I speak
888
00:43:36.200 --> 00:43:39.040
to use ServiceNow, and are in love
889
00:43:40.160 --> 00:43:43.180
with your platform because they can integrate all the rest of their tech stack into
890
00:43:43.240 --> 00:43:45.740
one seamless experience, and they always rave about that.
891
00:43:46.520 --> 00:43:48.220
Not a sales pitch or anything, but just literally-
892
00:43:48.400 --> 00:43:49.640
Yeah. No, I appreciate that
893
00:43:49.660 --> 00:43:53.530
... real feedback. It means that learning is no longer
894
00:43:53.540 --> 00:43:56.870
something you have to go to. I can be on a sales call and ask a
895
00:43:56.900 --> 00:44:00.560
question, and as I'm on the call, get real-time feedback right there and then,
896
00:44:00.980 --> 00:44:02.220
and then-
897
00:44:02.270 --> 00:44:02.270
Yeah
898
00:44:02.270 --> 00:44:06.150
... deliver it right there. It's kind of like a dream to have in there
899
00:44:07.720 --> 00:44:08.570
as well. Well, at-
900
00:44:08.580 --> 00:44:08.800
That's-
901
00:44:09.400 --> 00:44:09.520
Yeah.
902
00:44:09.560 --> 00:44:11.280
That is true readiness right there.
903
00:44:11.720 --> 00:44:15.640
Yeah. And for you and the team, you can instantly get that feedback in
904
00:44:15.680 --> 00:44:19.080
real time and be like, "Hey, what is the top challenges right now that our sales
905
00:44:19.090 --> 00:44:20.460
team's facing?" And boom,
906
00:44:21.880 --> 00:44:25.600
instantly get that. So I asked Perplexity, for example,
907
00:44:25.650 --> 00:44:29.280
"Why is the rest of my team's credit
908
00:44:29.380 --> 00:44:31.530
spend so high?" And I didn't know if it could-
909
00:44:31.580 --> 00:44:31.690
Mm.
910
00:44:31.820 --> 00:44:34.950
I didn't know if it was going to allow me to get the context from their,
911
00:44:36.720 --> 00:44:37.179
I don't know what ca-
912
00:44:37.280 --> 00:44:38.300
Yep, their usage.
913
00:44:38.320 --> 00:44:41.940
From their usage, but it did. And it came back, and it was like, "Shane's doing
914
00:44:41.980 --> 00:44:45.360
this and this and this, and it's meaning that it's costing more because of that."
915
00:44:45.420 --> 00:44:47.900
And I was like, "Update that to help Shane."
916
00:44:49.080 --> 00:44:52.960
But I also said, "What are the challenges that my team's facing?" And it
917
00:44:53.000 --> 00:44:56.590
gave me all of the answers to all of the questions they're asking, and
918
00:44:56.900 --> 00:44:57.039
the
919
00:44:58.320 --> 00:45:01.780
big challenges. So I could see the challenges in each
920
00:45:01.820 --> 00:45:05.800
department and actually deploy learning straight back to
921
00:45:05.840 --> 00:45:08.080
them in real time to help solve that gap.
922
00:45:08.120 --> 00:45:08.950
And I was like, "That is just crazy."
923
00:45:08.960 --> 00:45:10.160
Only slightly creepy.
924
00:45:10.930 --> 00:45:13.980
Yeah. It's slightly creepy, but it's not an invasion like-
925
00:45:14.040 --> 00:45:15.630
As long as they know you have access to the
926
00:45:17.400 --> 00:45:17.640
data.
927
00:45:17.820 --> 00:45:21.020
Quite frankly, until yesterday I didn't even know that.
928
00:45:21.060 --> 00:45:21.480
There you go.
929
00:45:21.840 --> 00:45:25.590
I'm admin, so maybe I should assume that I had
930
00:45:25.660 --> 00:45:29.520
that, but I didn't. And by the way, a lot of it is of course anonymized data, just
931
00:45:29.540 --> 00:45:31.980
to be clear. It's not like this specific person,
932
00:45:33.800 --> 00:45:36.380
that they did this, this and this. So that's super helpful-
933
00:45:36.740 --> 00:45:36.749
Yeah
934
00:45:36.749 --> 00:45:36.749
...
935
00:45:37.560 --> 00:45:39.110
as well. But yeah. Is it-
936
00:45:39.249 --> 00:45:41.800
And that ties to incentives, right?
937
00:45:41.880 --> 00:45:45.360
What are the incentives? I saw, I think it was Meta,
938
00:45:47.080 --> 00:45:50.900
someone within Meta launched a company-wide leaderboard for AI token
939
00:45:50.980 --> 00:45:52.150
usage, right?
940
00:45:53.380 --> 00:45:56.130
Do you want to be at the top of that? Because that's costing the organization
941
00:45:57.560 --> 00:45:57.980
quite a bit.
942
00:45:58.000 --> 00:46:00.040
But did you see the NVIDIA CEO
943
00:46:01.100 --> 00:46:04.780
viral clip about his engineers? Did you see that one about token spend?
944
00:46:05.740 --> 00:46:06.200
I did, yeah.
945
00:46:06.440 --> 00:46:10.040
Yeah. Right. He's like, "If I'm paying you half a million dollars a year, and
946
00:46:10.080 --> 00:46:13.380
you're not even using half a million dollars worth of credits, what are you doing
947
00:46:13.420 --> 00:46:13.920
here?"
948
00:46:14.270 --> 00:46:14.270
Yeah.
949
00:46:14.400 --> 00:46:15.920
He literally said that pretty blunt.
950
00:46:16.580 --> 00:46:20.380
I was like, "What am I paying you for if you've not even used half a
951
00:46:20.420 --> 00:46:21.600
million dollars worth of credits?"
952
00:46:21.720 --> 00:46:21.779
Yeah.
953
00:46:22.080 --> 00:46:24.700
I think he was saying they need to be using 5, 10 X-
954
00:46:25.560 --> 00:46:25.710
Mm
955
00:46:25.920 --> 00:46:27.270
... of the salary they're paying in credits.
956
00:46:27.300 --> 00:46:29.380
Which I was like of course, they're NVIDIA, they've got unlimited money
957
00:46:30.320 --> 00:46:33.180
to do that. But that was quite eye-opening, so.
958
00:46:33.380 --> 00:46:35.040
Yeah. Pushing the usage.
959
00:46:35.520 --> 00:46:35.640
Yeah.
960
00:46:35.700 --> 00:46:37.490
Pushing the token usage in particular.
961
00:46:37.640 --> 00:46:41.460
Crazy. I know we've been chatting way over time, but
962
00:46:41.540 --> 00:46:44.820
is there anything I didn't ask you that I should have, that you want to share?
963
00:46:45.140 --> 00:46:48.540
We didn't talk too much about
964
00:46:49.200 --> 00:46:52.580
specifically skills of the future.
965
00:46:53.600 --> 00:46:56.120
But I guess this whole concept of skills, and we can
966
00:46:57.420 --> 00:47:01.080
take this where you want or end it around here, but this
967
00:47:01.120 --> 00:47:03.920
whole concept of skills is not new, right?
968
00:47:04.240 --> 00:47:04.250
Mm-hmm.
969
00:47:04.300 --> 00:47:08.080
Organizations have been trying to become, quote, "skill-based" for a long
970
00:47:08.140 --> 00:47:08.420
time.
971
00:47:09.600 --> 00:47:13.290
And I often start talks internally or
972
00:47:13.320 --> 00:47:16.770
externally with the fact that even though I have the word skills in my title, I am
973
00:47:16.880 --> 00:47:20.520
a skills skeptic. And that is not because I don't believe it's
974
00:47:20.560 --> 00:47:24.040
valuable to capture skills. In fact, I think it's utterly
975
00:47:24.080 --> 00:47:27.550
critical with the vision of
976
00:47:27.580 --> 00:47:31.210
readiness. However, I think for the past 15, 20 years,
977
00:47:32.700 --> 00:47:36.600
enterprise has spun their wheels and not gotten the ROI out
978
00:47:36.780 --> 00:47:40.300
of the tools, the consulting around what it means to become a skills-based
979
00:47:40.380 --> 00:47:42.330
organization. I do think with
980
00:47:43.620 --> 00:47:47.160
the tools we have access to today, it is more possible than
981
00:47:47.220 --> 00:47:51.080
ever. But at the same time, if we are
982
00:47:51.220 --> 00:47:55.020
too stuck in the dirt, we will
983
00:47:55.080 --> 00:47:58.389
lose the forest. Right? The vision of the forest from the trees.
984
00:47:58.920 --> 00:48:02.840
If we're too stuck on getting the data exactly right on the
985
00:48:02.880 --> 00:48:06.860
supply side of skills, we're going to get stuck, and we're going to spin our
986
00:48:06.880 --> 00:48:10.740
wheels, and we're going to lose that trust and momentum
987
00:48:10.750 --> 00:48:14.520
within the organization. So at a certain point, we need to be
988
00:48:14.580 --> 00:48:18.300
okay with good enough from a skills capture perspective,
989
00:48:18.720 --> 00:48:22.540
and figure out where we want to be the most confident
990
00:48:22.940 --> 00:48:24.600
in specific skills.
991
00:48:25.120 --> 00:48:27.420
Mm-hmm. Yeah. No, I'm with you. Yeah.
992
00:48:28.880 --> 00:48:31.510
But one of the things that's changed, though, is how companies are actually
993
00:48:31.540 --> 00:48:35.000
thinking about skills. In the past, I would speak to leaders and they're like,
994
00:48:35.010 --> 00:48:38.389
"Yeah, we've just spent 12 months building our skills taxonomy."
995
00:48:39.280 --> 00:48:40.300
That no longer works.
996
00:48:40.970 --> 00:48:41.380
Yeah. It's old.
997
00:48:41.720 --> 00:48:44.320
It's already out of date, right? That no longer works anymore.
998
00:48:44.360 --> 00:48:45.460
And then also how they
999
00:48:46.760 --> 00:48:48.460
measure a skill
1000
00:48:49.656 --> 00:48:53.546
... is now completely changed. Now, to your point,
1001
00:48:54.036 --> 00:48:57.866
you can prove that Chris did the reps through the practice, and
1002
00:48:57.896 --> 00:49:00.656
he definitely has that skill of negotiating because we can see-
1003
00:49:00.716 --> 00:49:01.216
Yeah
1004
00:49:01.226 --> 00:49:03.016
... in the data that he did it 20 times.
1005
00:49:04.376 --> 00:49:07.866
Yes. I'm so excited about what we're
1006
00:49:07.896 --> 00:49:08.806
building around
1007
00:49:09.616 --> 00:49:11.316
talent signature because-
1008
00:49:11.396 --> 00:49:11.556
Yeah
1009
00:49:11.956 --> 00:49:15.756
... there is something about capturing the presence of a skill,
1010
00:49:16.316 --> 00:49:20.096
and there's confidence you can have across varying
1011
00:49:20.116 --> 00:49:23.576
degrees of the presence of a skill, but there's also confidence you can
1012
00:49:23.636 --> 00:49:26.656
have at the proficiency based on certain input
1013
00:49:26.716 --> 00:49:29.626
signals. So we need to look at both of those.
1014
00:49:30.296 --> 00:49:33.416
And again, focusing on the most critical skills for the most critical roles,
1015
00:49:34.236 --> 00:49:37.676
and where the business is focused is how we build momentum.
1016
00:49:38.176 --> 00:49:38.336
Yeah.
1017
00:49:39.196 --> 00:49:40.436
One of the ways I'm seeing companies
1018
00:49:41.276 --> 00:49:44.836
get better at this is, I don't know if you've seen this,
1019
00:49:45.896 --> 00:49:46.466
there's a lot of
1020
00:49:47.656 --> 00:49:51.416
tech consolidation happening in organizations.
1021
00:49:51.436 --> 00:49:53.626
Especially amongst every CHRO that I'm speaking to right now
1022
00:49:54.496 --> 00:49:55.476
are consolidating-
1023
00:49:55.726 --> 00:49:55.736
Yeah
1024
00:49:55.756 --> 00:49:58.936
... to make that... Because at the moment, a lot of the
1025
00:49:59.956 --> 00:50:03.616
data points, the data is sitting in so many different parts of the organization in
1026
00:50:03.776 --> 00:50:04.836
so many different platforms-
1027
00:50:05.676 --> 00:50:05.936
Yeah
1028
00:50:06.076 --> 00:50:07.556
... that you don't get a real picture of it.
1029
00:50:07.856 --> 00:50:11.056
When it was Nickel or IBM, the CHRO, they went from nearly
1030
00:50:11.196 --> 00:50:11.856
1,000,
1031
00:50:12.936 --> 00:50:16.556
just in HR solutions, down to less than 100 in
1032
00:50:16.576 --> 00:50:17.836
the space of a year.
1033
00:50:18.056 --> 00:50:19.156
Limiting the tech stack.
1034
00:50:19.336 --> 00:50:19.556
Yeah.
1035
00:50:19.656 --> 00:50:20.116
Yeah, wow.
1036
00:50:20.246 --> 00:50:24.216
So that they can create a more connected ecosystem, to your point,
1037
00:50:24.756 --> 00:50:25.196
and they can have-
1038
00:50:25.216 --> 00:50:25.526
For sure
1039
00:50:25.576 --> 00:50:28.076
... a real clear vision of what... Because otherwise, you've got your LMS over
1040
00:50:28.096 --> 00:50:30.616
here, your HRIS over here. You've got your AI coach over there.
1041
00:50:31.516 --> 00:50:34.516
You've got LinkedIn Learning and Udemy over here collecting data.
1042
00:50:34.916 --> 00:50:37.296
It's just a bit of a minefield, right?
1043
00:50:37.536 --> 00:50:37.816
And then-
1044
00:50:37.956 --> 00:50:38.766
Yeah. Absolutely
1045
00:50:38.766 --> 00:50:42.206
... every department's got their own something else that they're using
1046
00:50:42.856 --> 00:50:45.816
as well. So I think that's a big part of it, a challenge.
1047
00:50:47.616 --> 00:50:51.096
Even now I'm speaking to companies, large enterprise companies that everyone knows
1048
00:50:51.136 --> 00:50:55.106
of, that still have an intranet portal that someone goes into that just
1049
00:50:55.156 --> 00:50:55.416
sends them-
1050
00:50:55.426 --> 00:50:55.426
Mm
1051
00:50:55.556 --> 00:50:56.736
... into a learning black hole
1052
00:50:58.096 --> 00:51:01.356
of stuff right now. So it's going to be interesting-
1053
00:51:01.436 --> 00:51:01.445
Yeah
1054
00:51:01.445 --> 00:51:03.126
... to see how that evolved. What-
1055
00:51:03.236 --> 00:51:03.436
Yeah.
1056
00:51:04.776 --> 00:51:07.216
The elegance of simplicity is quite challenging.
1057
00:51:07.376 --> 00:51:11.276
It's tough, though, right? Especially when a lot of these solutions have been
1058
00:51:11.336 --> 00:51:13.236
embedded in the organization for so long.
1059
00:51:13.276 --> 00:51:13.466
It's a
1060
00:51:14.396 --> 00:51:15.376
big challenge. It's real.
1061
00:51:16.396 --> 00:51:19.956
I did want to ask you quickly before we go is what are your views on the
1062
00:51:20.196 --> 00:51:23.936
current noise and chatter around AI and jobs?
1063
00:51:24.756 --> 00:51:28.096
We've seen, obviously, a lot of companies using AI as a,
1064
00:51:28.836 --> 00:51:30.916
I don't want to say a scapegoat for cutting jobs, but
1065
00:51:32.456 --> 00:51:35.315
it's real. We're seeing it happen around us.
1066
00:51:35.376 --> 00:51:38.596
Do you think that this is just a great excuse for companies,
1067
00:51:39.156 --> 00:51:40.696
or do you think that
1068
00:51:42.316 --> 00:51:44.736
AI will impact jobs?
1069
00:51:44.796 --> 00:51:46.956
Well, broad brush is difficult here.
1070
00:51:47.016 --> 00:51:50.856
AI will impact 100% of jobs. Over
1071
00:51:50.956 --> 00:51:52.236
time, for sure.
1072
00:51:53.216 --> 00:51:55.756
White collar workers first and fast.
1073
00:51:56.656 --> 00:51:58.596
Absolutely. Every job will change.
1074
00:51:59.016 --> 00:52:02.666
The way we think about this is that certain jobs, the outcomes that
1075
00:52:02.696 --> 00:52:06.666
those roles produce will change, and other jobs, the outcomes
1076
00:52:06.676 --> 00:52:08.456
that those roles produce will not change.
1077
00:52:08.476 --> 00:52:10.936
So for example, recruiters will always recruit.
1078
00:52:11.456 --> 00:52:14.426
Salespeople will always sell. Engineers will always build, right?
1079
00:52:14.536 --> 00:52:18.476
Creators will always create. The way those roles
1080
00:52:18.516 --> 00:52:21.056
achieve those outcomes will drastically change.
1081
00:52:21.616 --> 00:52:23.276
You take the role of an HR business partner.
1082
00:52:23.596 --> 00:52:26.436
The outcomes that they drive, those are going to change.
1083
00:52:26.956 --> 00:52:29.236
Finally, the vision of
1084
00:52:30.076 --> 00:52:33.316
strategic value add for every business partner out
1085
00:52:33.356 --> 00:52:36.036
there is achievable because the
1086
00:52:37.036 --> 00:52:40.896
paperwork will be replaced by, to be kind of
1087
00:52:40.936 --> 00:52:41.896
cheesy, the people work-
1088
00:52:43.296 --> 00:52:43.456
Yeah
1089
00:52:43.796 --> 00:52:47.636
... because of the technology. So I think it's important to think
1090
00:52:47.696 --> 00:52:51.516
about it with some nuance at the role level, at the process
1091
00:52:51.596 --> 00:52:53.716
level, at the skill level for sure.
1092
00:52:55.616 --> 00:52:59.256
At the organizational level, your question around companies and layoffs,
1093
00:52:59.676 --> 00:53:03.376
I think a lot of it is... What'd you say,
1094
00:53:03.436 --> 00:53:06.256
scapegoat? I think some of it is absolutely scapegoat.
1095
00:53:06.336 --> 00:53:10.246
Organizations that scale too quickly over time are using this as
1096
00:53:10.296 --> 00:53:10.816
a moment.
1097
00:53:11.636 --> 00:53:15.406
In other organizations, I think there are roles that are going away
1098
00:53:15.776 --> 00:53:19.116
now. Does that mean that the organization won't come to
1099
00:53:19.136 --> 00:53:22.916
regret the reduction in force because they're going to
1100
00:53:22.976 --> 00:53:26.476
pay whatever the stat is, more than they would for the internal
1101
00:53:26.536 --> 00:53:30.376
development and the reskilling of those individuals into roles
1102
00:53:30.416 --> 00:53:33.376
that will exist and are evolving in the future.
1103
00:53:33.836 --> 00:53:35.016
Yeah. Time will tell.
1104
00:53:36.056 --> 00:53:36.516
Time will tell.
1105
00:53:36.576 --> 00:53:38.836
No one has the answers. Last question before I let you go.
1106
00:53:39.116 --> 00:53:42.096
What advice would you give to your colleagues that
1107
00:53:42.196 --> 00:53:45.756
are currently on their own workforce skills and talent
1108
00:53:45.816 --> 00:53:49.486
readiness journey? Because many people are really at the beginning of this
1109
00:53:49.496 --> 00:53:51.366
curve right now. I know you've only-
1110
00:53:51.416 --> 00:53:51.746
Definitely
1111
00:53:51.896 --> 00:53:55.876
... you're a few months in, but I'd love to, what advice would you give to
1112
00:53:55.916 --> 00:53:57.596
people just starting?
1113
00:53:57.776 --> 00:54:00.946
Yeah. So I'd say three things. One,
1114
00:54:01.976 --> 00:54:05.896
understand the work that your people are doing.
1115
00:54:06.796 --> 00:54:10.716
And if you want to start somewhere, understand the work that some of the
1116
00:54:10.756 --> 00:54:13.836
most critical roles in your organization are doing.
1117
00:54:14.116 --> 00:54:16.136
How do they get done what they get done?
1118
00:54:16.736 --> 00:54:20.546
It sounds basic, but process documentation,
1119
00:54:22.716 --> 00:54:25.296
even if that's anecdotal, is absolutely critical
1120
00:54:26.136 --> 00:54:27.436
if you want to do something with it.
1121
00:54:27.536 --> 00:54:28.036
Mm-hmm.
1122
00:54:28.256 --> 00:54:29.496
Next is I would say
1123
00:54:30.536 --> 00:54:34.216
don't try and boil the ocean. Get hyper-focused on a
1124
00:54:34.276 --> 00:54:37.836
few high impact use cases. These don't need to be
1125
00:54:38.116 --> 00:54:41.896
something that covers the largest population, but it should be something that
1126
00:54:41.906 --> 00:54:45.796
drives impact, that's going to build the organizational momentum
1127
00:54:47.116 --> 00:54:48.066
to go and do
1128
00:54:49.316 --> 00:54:52.976
more. And then finally, keep finding
1129
00:54:52.996 --> 00:54:55.536
individuals who are talking and thinking about this.
1130
00:54:56.496 --> 00:54:58.596
There are people out there who are thinking about readiness.
1131
00:54:58.936 --> 00:55:02.306
There are people out there whose roles are all around readiness.
1132
00:55:03.376 --> 00:55:07.206
I think if you have the luxury to make this someone's
1133
00:55:07.236 --> 00:55:10.866
job, someone who gets to wake up every day thinking
1134
00:55:10.936 --> 00:55:14.896
about what it means to identify the signals, to build the system
1135
00:55:15.296 --> 00:55:19.236
based on what you have, not adding new, to your point around tech simplification,
1136
00:55:19.776 --> 00:55:20.756
right? There's so much
1137
00:55:21.616 --> 00:55:25.456
data out there in your organizations that you can capture and do something
1138
00:55:25.516 --> 00:55:28.536
with, even if it's not going to be a perfect answer.
1139
00:55:28.956 --> 00:55:32.716
The goal is to use readiness as a compass versus
1140
00:55:32.726 --> 00:55:35.756
an automated decision maker, right?
1141
00:55:35.856 --> 00:55:38.856
Human in the loop is utterly critical with talent decisions.
1142
00:55:39.616 --> 00:55:42.936
But some intelligence is better than purely
1143
00:55:42.946 --> 00:55:43.896
intuition.
1144
00:55:43.956 --> 00:55:46.476
Yeah. Love that. Lastly, where can people connect with you?
1145
00:55:46.876 --> 00:55:49.416
If they want to reach out, say hi, where's the best place?
1146
00:55:49.496 --> 00:55:51.716
Find me on LinkedIn. I'm on LinkedIn.
1147
00:55:52.376 --> 00:55:54.736
I think it's Josh-Newman-1.
1148
00:55:54.776 --> 00:55:55.956
There'll be a link below, don't worry.
1149
00:55:55.966 --> 00:55:57.596
Anyone who's watching right now, there'll be a link below.
1150
00:55:57.656 --> 00:55:57.666
Yeah. Good luck.
1151
00:55:57.666 --> 00:56:01.096
And then, is there anything in terms of people learning about the academy and stuff
1152
00:56:01.116 --> 00:56:04.036
like that? Is there any specific links that we want to be sending people to, or
1153
00:56:04.076 --> 00:56:06.256
just servicenow.com? What's the best place?
1154
00:56:06.636 --> 00:56:09.396
Go check out ServiceNow University. It's free.
1155
00:56:09.616 --> 00:56:11.976
It's gamified. A ton out there.
1156
00:56:14.296 --> 00:56:14.836
Go play.
1157
00:56:14.936 --> 00:56:18.356
Amazing. Anyone who's listening right now, those links will be below, so make sure
1158
00:56:18.396 --> 00:56:18.656
you go
1159
00:56:19.876 --> 00:56:22.336
check that out and follow Josh. But Josh, thanks so much.
1160
00:56:22.856 --> 00:56:26.676
Appreciate you, and looking forward to reconnecting and following the
1161
00:56:26.716 --> 00:56:27.636
journey. Thanks a lot.
1162
00:56:28.096 --> 00:56:29.576
All right. Thanks for the chat, Chris.
1163
00:56:29.636 --> 00:56:29.896
Thanks.
Josh Newman, Vice President, Workforce Skills & Talent Readiness at ServiceNow.