Rebecca Hinds (00:00)

Welcome back to Intelligence Real and Imagined from the Work AI Institute at Glean.

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Rebecca Hinds (00:05)

This is the show where we sort through what's real, what's hype and what actually works with AI at work.

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Rebecca Hinds (00:13)

I'm your host Rebecca Hinds and I lead our Work AI Institute here at Glean.

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Rebecca Hinds (00:18)

Today I'm joined by two incredible female AI leaders, Shweta Puri from Nextdoor and Nichole Sterling from Women Defining AI.

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Rebecca Hinds (00:27)

This episode is inspired by our AI Transformation 100 report and we're talking about AI adoption, how it happens not through hype, but through trust, experimentation, better workflows and people who are willing to rethink how work gets done.

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Rebecca Hinds (00:46)

Let's dive in.

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Rebecca Hinds (00:50)

Shweta, why don't you kick us off and please introduce yourself.

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Shweta Puri (00:55)

Hi everyone.

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Shweta Puri (00:56)

I'm super excited to be here.

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Shweta Puri (00:58)

I'm Shweta Puri, Marketing technology and AI operations lead at nextdoor, the neighborhood network connecting people to local communities across us and beyond.

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Shweta Puri (01:08)

My background is at the intersection of marketing, technology, operations and more recently enterprise AI.

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Shweta Puri (01:15)

What that practically means is I'm not a researcher or a vendor.

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Shweta Puri (01:19)

I'm a practitioner who has actually had to figure out how to make AI real inside a company with real budget constraints, real skeptics and real workflows that would pause for a pilot.

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Shweta Puri (01:32)

So.

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Shweta Puri (01:32)

So I've been a Glean customer and also a power user since our very early days with the platform and it's been a year or so.

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Shweta Puri (01:39)

I have somewhat unusual kind of vantage point here.

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Shweta Puri (01:43)

I've lift both sides, the person rolling it out and also the person using it every day.

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Shweta Puri (01:48)

So the dual perspective is what I'm hoping to bring to this conversation today.

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Shweta Puri (01:52)

And I've also written this journey in Glean's blog which I'll share as a follow up resource.

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Shweta Puri (01:59)

But the short version is that the technology was the easy part.

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Shweta Puri (02:02)

Everything else is what we are here to talk about.

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Shweta Puri (02:05)

I'll pass it back to Rebecca.

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Rebecca Hinds (02:07)

Wonderful.

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Rebecca Hinds (02:08)

And I'm so excited for that blog.

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Rebecca Hinds (02:10)

I'm sure we'll send it as a follow up.

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Rebecca Hinds (02:12)

It's very insightful.

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Rebecca Hinds (02:14)

Nichole, please introduce yourself.

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Nichole Sterling (02:16)

Hello everyone, my name is Nichole Sterling.

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Nichole Sterling (02:18)

I am the co founder of Women Defining AI.

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Nichole Sterling (02:22)

We are an AI and education company for women and non binary individuals.

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Nichole Sterling (02:28)

We started several years ago as just a lunch and learn and now we have a community that's nearly 2,000 members strong.

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Nichole Sterling (02:38)

And I've stepped away from the operational role within Women Defining AI because I have started my own AI company.

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Nichole Sterling (02:45)

This is one of the beautiful things that we've seen come out of our women defining AI community is women are getting new jobs, starting new companies.

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Nichole Sterling (02:55)

I happen to be a publicly elected official.

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Nichole Sterling (02:58)

I sit as mayor pro tem for my community.

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Nichole Sterling (03:01)

And so my AI company has merged my subject matter expertise in local government with my technologist background.

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Nichole Sterling (03:08)

And so I come at it from not only someone who has had to drive AI amongst the community, but also uses AI herself and also from a vendor perspective in how I interact with local governments.

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Nichole Sterling (03:24)

I also sit on the Gov AI coalition though, so I help local governments procure safe, ethical and responsible AI all across the United States.

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Nichole Sterling (03:33)

So excited for the conversation today and thank you for having me.

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Rebecca Hinds (03:36)

So today's session is all about how AI adoption really happens.

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Rebecca Hinds (03:42)

And what we consistently see is most organizations don't struggle with access to AI.

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Rebecca Hinds (03:48)

We often think that AI is fundamentally an access problem.

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Rebecca Hinds (03:52)

If we give employees this technology, they'll use it.

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Rebecca Hinds (03:55)

The reality is not as such and what we see is that conversion from real exposure to AI to real change, exposure to change is very difficult.

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Rebecca Hinds (04:07)

How do you get this technology in the hands of people in a way that they're actively using it and using it to their benefit day to day?

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Rebecca Hinds (04:15)

So, Shweta, one of the things we're seeing, I know you're seeing as well, is more and more organizations are establishing new roles that contain AI in the title.

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Rebecca Hinds (04:25)

And your role is an example of that.

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Rebecca Hinds (04:29)

Marketing technology and AI operations lead.

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Rebecca Hinds (04:32)

Tell us a little bit about your role, what you're responsible for and the significance of having AI in the title.

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Shweta Puri (04:41)

Yeah, I know it's exciting times.

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Shweta Puri (04:44)

Everybody wants to embrace AI in some way or the other.

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Shweta Puri (04:47)

But I'll give you the perspective from my, from my side, I've spent 10 plus years kind of leading martech and operations, which is again, not so glamorous, connective tissue work of making tools actually work for people.

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Shweta Puri (05:02)

So last year I pivoted into AI operations and enablement at Nextdoor and Lean was one of our very first platforms that we implemented.

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Shweta Puri (05:13)

What I've come to appreciate about having AI in my title is that it's less about deep technical expertise and more about like being accountable for real outcomes, helping teams find meaningful ways to work differently with AI so it becomes a genuine part of how people do their jobs rather than just another tool that we invested in.

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Shweta Puri (05:37)

So my role kind of sits squarely between business technology and people processes.

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Shweta Puri (05:44)

And the goal is not to deploy more tools, it's to change the behavior or the foundation itself.

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Shweta Puri (05:51)

Here's what that looked like a practice we kind of started with 50 people pilot, we didn't just open a signup link and hope for the best.

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Shweta Puri (06:03)

We selected the champions who deeply understood the problem.

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Shweta Puri (06:06)

And also that meant from day one, everyone in the room had the context, conviction and reason to care.

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Shweta Puri (06:13)

So that deliberate curation made all the difference.

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Shweta Puri (06:16)

And within a month we had a strong monthly active user base and also quite a few daily active users.

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Shweta Puri (06:24)

So we made the decision to roll it out broadly across the company to around 550 employees.

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Shweta Puri (06:31)

And we are now at 500 plus users, 700 agents built and almost at 82% stickiness in just six months of that broad rollout.

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Shweta Puri (06:43)

So yeah, I mean phase one was getting people to use.

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Shweta Puri (06:46)

Search was the entry point and not the destination and finding answers was useful but not transformational.

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Shweta Puri (06:52)

So phase two is where things actually started to get interesting when we moved from, you know, I search from Glean through to like I work through Glean, which is all about agents, automations, AI embedded in the actual job and not like as a side note.

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Shweta Puri (07:12)

So yeah, that's a high level journey so far at Nextdoor.

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Rebecca Hinds (07:19)

And that embedding AI in the flow of work is something we consistently hear is incredibly important.

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Rebecca Hinds (07:25)

It's sometimes referred to as the toggle tax.

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Rebecca Hinds (07:28)

And this is something we unpack in our report as well.

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Rebecca Hinds (07:30)

The reality is employees are often so overwhelmed, so overburdened that they're not wanting to switch to another platform or tool to use the technology.

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Rebecca Hinds (07:42)

So shweta, one of the things I want to double click on in your journey is what we often see in organizations is what's sometimes referred to as the ambition execution gap.

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Rebecca Hinds (07:54)

A lot of ambition associated with the technology, a lot of hype, but then when the rubber hits the road, not seeing that translate into real results.

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Rebecca Hinds (08:04)

Are there aspects of your journey and Nextdoor's journey in which you've seen you've run the training, you've run the pilot pilots, broad rollouts, but there's been a gap in translating that, you know, ambition and hard work in preparation into real results.

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Shweta Puri (08:23)

Yeah, that's true.

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Shweta Puri (08:25)

I mean that's the real challenge.

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Shweta Puri (08:26)

And again, training alone almost never moves the needle.

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Shweta Puri (08:31)

So I would say that as someone who has run a lot of it, and here's what I learned the hard way.

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Shweta Puri (08:37)

Think about the last time you actually, you know, changed how you work because of a company wide announcement.

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Shweta Puri (08:44)

Probably never like there are always announcements happening in the company, right.

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Shweta Puri (08:48)

But if a peer you respect, someone in a role like you pulled you aside and said, you know, this saved me two hours Last week you'd probably try it pretty quickly and drop everything else.

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Shweta Puri (09:01)

Right.

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Shweta Puri (09:02)

So AI adoption works exactly the same way.

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Shweta Puri (09:05)

The signal has to come from someone with credibility in the room and not from a central team, just kind of broadcasting to everyone.

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Shweta Puri (09:13)

So the second unlock was the okrs.

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Shweta Puri (09:16)

When we embedded AI usage into how teams measure success not as a side initiative, but as part of how they hit their goals, AI stopped being, you know, overhead and started being how people actually worked.

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Shweta Puri (09:33)

If your AI program is not touching, the okrs is still an optional thing and people would consider that, that as optional, even role.

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Shweta Puri (09:43)

So I would say start from the basic, like embed AI into the OKRs or have people kind of drive more adoption that way.

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Rebecca Hinds (09:53)

Yeah, that's, that's great insight for sure.

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Rebecca Hinds (09:55)

Nichole, I want to turn to you.

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Rebecca Hinds (09:57)

Have you seen this gap between ambition and execution as well in, in your work?

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Rebecca Hinds (10:03)

And what do you think is, is driving it?

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Nichole Sterling (10:06)

Yes, absolutely.

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Nichole Sterling (10:09)

There is always this disconnect between leaders who say we got to get, have, that we have to have.

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Nichole Sterling (10:15)

And yet they may not.

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Nichole Sterling (10:16)

And if they do provide training, that's great.

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Nichole Sterling (10:19)

We've seen a lot of companies across, whether it's women defining AI or even in my realm of local government, where they don't provide training, they don't provide guidance.

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Nichole Sterling (10:29)

And so the, everyone's left wondering, well, how do we do this thing?

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Nichole Sterling (10:34)

And I think one of the reasons that's driving it is because there is a lot of hype that, you know, 2025 was all, you know, like a lot about agents.

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Nichole Sterling (10:42)

And so everyone is asking agents, is this something I should know?

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Nichole Sterling (10:45)

And unfortunately, because there's like this pressure towards height, folks internally, they're not doing some of the basic, you know, just blocking and tackling of training, experimentation, usage.

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Nichole Sterling (11:00)

And so folks don't have a very good understanding of what they could get out of AI, what the tools are that they could use to their advantage.

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Nichole Sterling (11:08)

And so it creates what this, what we like to call this jagged edge frontier.

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Nichole Sterling (11:12)

And it's this idea that, you know, some AI tools are super, are very good at certain things and so, you know, but then it doesn't do so good at other things.

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Nichole Sterling (11:23)

And it's also context specific to, you know, potentially your company and your role.

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Nichole Sterling (11:28)

And so it feels like AI has like this very jagged edge, really good at some things maybe not so great.

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Nichole Sterling (11:32)

And then of course it's context specific.

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Nichole Sterling (11:36)

And so if you're not helping your folks internally to understand and to experiment and to experiment out in public within the company, we just Continue to chase this hype cycle.

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Nichole Sterling (11:54)

And so it feels like there is this execution ambition gap for sure.

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Rebecca Hinds (11:58)

And one of the things that both of you have articulated is the importance of the learning journey.

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Rebecca Hinds (12:04)

And I often think that's underestimated in terms of first having your employees know how to use AI in a search capacity, something that most employees are already familiar with.

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Rebecca Hinds (12:17)

Then perhaps switching to assistants and understanding, okay, how do you interact with an intelligent assistant as part of your day to day work?

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Rebecca Hinds (12:26)

And then the leap becomes agents.

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Rebecca Hinds (12:29)

It's very difficult to go from zero to agent in a way that employees feel comfortable with.

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Rebecca Hinds (12:36)

I want to talk about champions, AI champions.

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Rebecca Hinds (12:40)

I think this is a phrase that has become more and more common and I think it's recognized as an important strategy.

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Rebecca Hinds (12:47)

Not just the top down change, but how do you activate these AI champions and influencers within an organization?

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Rebecca Hinds (12:54)

Shweta, how do you identify champions?

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Rebecca Hinds (12:57)

What are you looking for in identifying these people who can help lead the charge around AI adoption?

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Shweta Puri (13:06)

Yeah, of course, champions at our company were not who you would expect naturally.

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Shweta Puri (13:14)

They weren't always the most technical people.

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Shweta Puri (13:17)

They were rather the curious people who were cross functionalists and who were willing to try out things, fail and come back with a better version.

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Shweta Puri (13:27)

So in our pilot, champions actually picked the participants.

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Shweta Puri (13:31)

So they had the skin in the game.

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Shweta Puri (13:33)

So they weren't like, you know, assigned a role, they chose it.

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Shweta Puri (13:37)

So the ownership mattered.

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Shweta Puri (13:39)

And after almost like six to seven months of working with these folks, now, in fact longer, including the pilot, it's almost been a year, you would exactly know what the what content to give someone depending on where they are in the journey.

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Shweta Puri (13:55)

So you know, a new glean user always needs a different kind of a nudge than someone who's already built, say five or six agents.

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Shweta Puri (14:03)

So we also build infrastructure for champions to succeed.

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Shweta Puri (14:08)

A private, like Slack channel, where anyone could share ideas, showcase some wins and exchange tips.

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Shweta Puri (14:14)

Power users definitely influence other employees at the company.

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Shweta Puri (14:20)

And then we also started with recurring office hours, which were not really just presentations, but they were more collaborative debugging sessions, bite size updates and company bulletins.

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Shweta Puri (14:32)

And we also gamified it, spotlighting the top agent builders, for example, most active chat users, most creative workflows.

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Shweta Puri (14:41)

And that made success visible and repeatable.

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Shweta Puri (14:44)

So our mantra became like, bring AI to the work, don't bring employees to another tool.

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Shweta Puri (14:50)

So embed AI where the employees actually work already.

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Rebecca Hinds (14:56)

That's great.

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Rebecca Hinds (14:57)

And for the folks just tuning in, we're chatting about AI champions and the importance of AI champions within the Organization.

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Rebecca Hinds (15:05)

That's something we see consistently as well, you know, marrying that important top down change with activating your AI influencers and champions and recognizing that they might not be who we think they are.

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Rebecca Hinds (15:17)

And we consistently see they don't tend to be highly technical, they don't tend to be at the top of the org chart, but they have big, big influence in encouraging other people around them to adopt.

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Rebecca Hinds (15:29)

Nichole, I'm curious, from your perspective, what are the characteristics you see in these AI champions?

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Rebecca Hinds (15:35)

Do they have a certain Persona or footprint that tends to show up time and time again?

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Nichole Sterling (15:41)

Yeah, I would agree with Shweta when she says, you know, these are usually just curious early adopters that are cross functional generalists.

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Nichole Sterling (15:50)

That actually describes me and Helen, the co.

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Nichole Sterling (15:52)

Founder, co founder of Women Defining A to A T.

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Nichole Sterling (15:56)

And you know, just like you had mentioned, it's oftentimes they're not always the most, you know, technical but they have the ideas, they know how their workflows and they can look at AI and say, oh, this could probably work here.

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Nichole Sterling (16:11)

And that's one of the other characteristics that we've seen time and time again with some of these champions is that they're power users that are willing to just try, fail, talk about that failure, what worked, what didn't, and then keep going.

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Nichole Sterling (16:26)

This is very much kind of the ethos that we built our community around is just, just put it out there, just, just try, fail, fail multiple times until you can kind of find something that works.

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Nichole Sterling (16:37)

And so one of that, and that's actually one of the other things that we see is that they can apply solutions to different problems instead of just keeping AI to a single lane.

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Nichole Sterling (16:46)

It's like, oh no, I only use AI for this over here.

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Nichole Sterling (16:49)

No, they're, they're constantly applying solutions to different problems.

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Nichole Sterling (16:54)

And so that's really exciting to watch even in my own experience.

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Nichole Sterling (16:58)

Right.

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Nichole Sterling (16:58)

So in terms of a champion, because many other industries are just emerging with AI and local government happens to be one of them.

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Nichole Sterling (17:07)

They're not, they're, they're not as forward as the private sector, but they also don't want to be left behind and we, I'm actually leading a session later today about vibe coding and local government.

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Nichole Sterling (17:19)

You wouldn't think many local governments are, are interested in that or it's just, it's pushing the boundaries a little too much.

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Nichole Sterling (17:24)

But it's speaking very much to this idea that they are the subject matter experts, they know how work flows and what if we put a tool in their hands that allowed them to Create the thing that they usually have to rely on expensive consultants for or you know, other folks to bring their idea to life.

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Nichole Sterling (17:44)

And so it's great to see that happening in different sectors.

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Nichole Sterling (17:48)

And again, it always comes back to who's, who's willing and who has the idea.

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Rebecca Hinds (17:54)

And I think that's one of the most fascinating shifts we're seeing across the board is how the product development lifecycle is rapidly changing in terms of being able to rapidly prototype much easier and being able to demonstrate a proof of concept where previously you were relying on these experts.

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Rebecca Hinds (18:13)

It's quite exciting.

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Rebecca Hinds (18:14)

But it also opens up some risk for the organization and needing to be intentional about how you manage that as well.

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Rebecca Hinds (18:22)

I love that you touch touched on cross functional workers as well and those being great candidates for AI champions.

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Rebecca Hinds (18:30)

It's something that we consistently see as well, you know, because in part they're not just building for themselves, they're thinking more holistically about the organization.

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Rebecca Hinds (18:40)

And I think that's, you know, often a strategy we'll recommend to organizations is don't just, you know, find the people who are curious, that's a very important group, but find the people who span different functional groups and teams as well.

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Rebecca Hinds (18:54)

Yeah, so I want to switch to measurement.

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Rebecca Hinds (18:59)

This is very challenging in the era of AI.

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Rebecca Hinds (19:03)

You know, how do you measure success?

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Rebecca Hinds (19:04)

How do you measure effectiveness?

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Rebecca Hinds (19:06)

Nichole, I'd like to start with you.

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Rebecca Hinds (19:09)

How do you think leaders should be thinking about measurement right now?

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Nichole Sterling (19:16)

Yeah, this has been a conversation that's happened for a couple of years now because I think the first gut reaction is, well, how do I measure the success of this thing?

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Nichole Sterling (19:26)

And especially with AI being new and new to our behaviors, folks have really tried to figure out a way to successfully measure it.

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Nichole Sterling (19:34)

And we don't know that there are really any perfect metrics yet.

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Nichole Sterling (19:39)

It's still mostly about usage.

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Nichole Sterling (19:41)

It's about building habits, as we've already talked about.

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Nichole Sterling (19:45)

It's about understanding how work may shift or change.

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Nichole Sterling (19:49)

And so what good looks like actually depends on the job that's to be done.

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Nichole Sterling (19:55)

With AI, I have the privilege of working in different industries and different sectors.

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Nichole Sterling (20:02)

And at first it was always about the productivity metric.

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Nichole Sterling (20:05)

It's like, yeah, but then if you are just trying to get so many hours back there and if you just take on more administrative work, is that really helping the organization?

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Nichole Sterling (20:15)

And that is something that I've seen echoed across multiple industries.

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Nichole Sterling (20:19)

And so then it becomes about, okay, well, what's those higher order goals that we could be going toward?

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Nichole Sterling (20:25)

Whether that's you know, a higher, an overarching higher goal of, you know, in local government it might be something like, you know, maybe citizen satisfaction or something like that.

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Nichole Sterling (20:37)

But again, like folks are still trying to figure out, like how that attaches, like, how does it all connect?

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Nichole Sterling (20:44)

So it really, for me it's, it's, it's been very contextual.

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Rebecca Hinds (20:48)

It really depends function to function.

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Rebecca Hinds (20:51)

We see team to team anchoring in Those existing business KPIs, I think is, is a hallmark of what we see work, given how malleable and flexible the technology is now.

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Rebecca Hinds (21:05)

Closely related to the conversation around metrics is this phenomenon that's sometimes referred to as AI theater.

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Rebecca Hinds (21:12)

Using the technology, to use the technology.

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Rebecca Hinds (21:15)

There's a lot of pressure right now to show you're using AI, show you're investing in AI and building that capability within your organization.

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Rebecca Hinds (21:23)

Nichole, how do you see AI theater show up day to day?

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Rebecca Hinds (21:28)

What are some of those telltale signs?

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Nichole Sterling (21:31)

So I've said that I get to see across industries and it's always when I hear that somebody's, oh, we're using ChatGPT on like a superficial level.

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Nichole Sterling (21:45)

And the real kicker for me is when they say that, oh, it's the free version.

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Nichole Sterling (21:50)

And I'm just like, okay, that means that it's just a lot of hand wavy, it's a lot of theater.

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Nichole Sterling (21:56)

You don't really have that grounded strategy.

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Nichole Sterling (22:01)

And like I said, it pops up everywhere.

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Nichole Sterling (22:03)

And so I think externally, CEOs make a lot of big statements about AI, but employees don't actually know what it means for their day to day.

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Nichole Sterling (22:13)

And they're not prioritizing the training or the experimentation in public.

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Nichole Sterling (22:18)

And they can just say, oh, well, we're using ChatGPT.

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Nichole Sterling (22:20)

But again, like all the things that we've talked about, whether that's the training, understanding how it works in the workflows, are we failing at it?

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Nichole Sterling (22:27)

We want to hear like those failing stories just as much as the success stories.

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Nichole Sterling (22:32)

And so, you know, if folks don't have that guidance and it really is just a bunch of hand waving internally, like people just lack the sense of proficiency.

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Nichole Sterling (22:42)

They don't know what good AI looks like for their role, for their team's role.

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Nichole Sterling (22:47)

And that's just where it's very frustrating to hear companies still engaging in that way just so they can say they're doing AI.

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Rebecca Hinds (22:58)

And I think one way to combat this is Shweta, what you were discussing earlier in terms of the importance of embedding AI in the flow of work.

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Rebecca Hinds (23:08)

And I want to return to that conversation because I think it is very relevant here.

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Rebecca Hinds (23:13)

Can you walk us through one workflow where AI went from a side project, maybe more of that theater and talk as opposed to action to IT being deeply embedded in the flow of work in the day to day work of employees?

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Shweta Puri (23:31)

Yeah, of course.

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Shweta Puri (23:32)

The way I kind of think about is our agents or our automations that we've built with AI or lean so far they fall under like two buckets.

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Shweta Puri (23:43)

One is like evergreen or always on kind of agents and then the second bucket I would call it as seasonal and both are essential Evergreen always on kind of agents would be answering the same questions that never go away.

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Shweta Puri (24:00)

And a good example here is for example like the IT help agent is the is a, you know is a highly used agent in our company.

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Shweta Puri (24:10)

We already had a slack channel where employees asked IT questions, right?

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Shweta Puri (24:14)

The problem was not that the answer the problem was actually here that the answers were buried in the scroll history and not discoverable in that moment when people are looking for.

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Shweta Puri (24:24)

So every question felt new and obviously there was earlier a human interacting or answering these questions.

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Shweta Puri (24:32)

However we put a glean agent in that existing channel and now it's the first line answer.

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Shweta Puri (24:38)

It deflects repeated questions, pulls from historical databases docs and routes only the genuinely new issues to humans.

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Shweta Puri (24:48)

So we didn't create a new process here, we made the existing one smarter.

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Shweta Puri (24:53)

And the same principle kind of applies to another agent that we are soon rolling out instead of pinging a data analyst.

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Shweta Puri (25:02)

And this is called a data help agent where employees will now be able to ask natural language questions and get answers pulled directly from databricks environment in our case.

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Shweta Puri (25:14)

And that's you know a saving for both the people asking and also the team that used to field those requests earlier.

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Shweta Puri (25:22)

So these are some of a couple of examples of evergreen or always on kind of agents.

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Shweta Puri (25:28)

Then there are these seasonal agents that we experimented with recently.

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Shweta Puri (25:32)

And you know, because these use cases are kind of built in they have deadlines and captive audience open enrollment was was a perfect example here every year people people team gets flooded with same questions about plans, options, deadlines, workday actions, etc.

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Shweta Puri (25:54)

So our you know the benefits agent named to feel approachable you know not just employees get clarity instantly and get pointed answers right away instead of waiting for an HR response.

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Shweta Puri (26:11)

And another seasonal sort of an example is a self performance review agent which we launched ahead of the review cycle who likes to write their self reviews every quarter or every six months.

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Shweta Puri (26:24)

Employees could only ask the agent to structure their assessment, provide Examples and how to, and get help on how to frame the impact right at the moment they need it.

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Shweta Puri (26:35)

So that those were some of the impactful agents that we recently started using and they've shown impact significantly.

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Rebecca Hinds (26:45)

That's great.

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Rebecca Hinds (26:45)

And there's a comment in the chat from Joshua that I think speaks to this really well as well.

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Rebecca Hinds (26:52)

We often think that AI agents should do everything.

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Rebecca Hinds (26:55)

They should automate the complete process.

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Rebecca Hinds (26:57)

And what you've done so well is you've thought very carefully about the division of labor for the aspects of work, for example, that are routine and there is precedent for, you know, delegate that to an agent.

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Rebecca Hinds (27:10)

The novelty in terms of new tasks, new asks from employees, that's a uniquely human pursuit often.

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Rebecca Hinds (27:19)

And I think that division of labor, it's not easy, but I think it's important when we think about agents in particular and workflows.

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Rebecca Hinds (27:28)

I want to wrap up with one final question and Shweta, I'll ask you this one first.

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Rebecca Hinds (27:35)

There's a lot of pressure to move quickly with AI now and I think there's a lot of fear of going too slow and feeling behind.

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Rebecca Hinds (27:46)

What is one concrete action you'd recommend folks take in the next week or next month in terms of taking that extra step to move their AI adoption forward?

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Shweta Puri (27:58)

Yeah.

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Shweta Puri (27:59)

If there's one thing I want you to walk away with today is, is it's not basically it's don't start with a strategy deck.

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Shweta Puri (28:07)

Start with one flow workflow your team already does, and make it smarter.

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Shweta Puri (28:12)

That's it.

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Shweta Puri (28:13)

That's the whole playbook.

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Shweta Puri (28:14)

Find the friction, drop AI in it, pick one metrics to prove what, what's working, and then let your teams pull it forward.

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Shweta Puri (28:23)

That's how we went from, you know, a 50%, 50% file to 700 plus agents.

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Shweta Puri (28:29)

Not by planning it all upfront, but by following the signals one workflow at time.

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Shweta Puri (28:35)

So we need to divide and conquer.

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Shweta Puri (28:37)

And you know, not everything can be implemented on day one.

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Shweta Puri (28:41)

So I promise you, once your team feels that, you know, they get 20 minutes back, the next question would be, should we use AI or should we, you know, where can we use AI better?

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Shweta Puri (28:55)

So basically embed the AI in the processes themselves that already exist.

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Rebecca Hinds (29:01)

That's great, great advice.

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Rebecca Hinds (29:02)

Nichole, how about you?

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Rebecca Hinds (29:03)

What's one action that you'd recommend every organization take as part of their AI adoption journey right now?

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Nichole Sterling (29:11)

Yeah.

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Nichole Sterling (29:12)

So as I, as I mentioned, I have my own AI company and I am typically, because I'm essentially the customer and the subject matter expert, I oftentimes will Prototype out new feature sets and then hand off to my engineering team to put into production.

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Nichole Sterling (29:27)

And I tell the story as an example for like the takeaway.

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Nichole Sterling (29:31)

I was prototyping basically an ADU analyzer.

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Nichole Sterling (29:35)

So ADUs are accessory dwelling units.

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Nichole Sterling (29:39)

They're a great way to jumpstart affordable housing initiatives within jurisdictions.

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Nichole Sterling (29:44)

And as I started to build it I, you know, I was like eh, killed that, you know, session, started another one because I'm vibe coding and tried multiple ways on how to solve it.

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Nichole Sterling (29:55)

And ultimately what came out of it was a new idea that I hadn't even thought of beforehand because after I kind of built a prototype of where could these detached accessory dwelling units go in one of my trial municipalities or cities.

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Nichole Sterling (30:10)

And then I realized, you know what would actually make this great is if you could do a policy analyzer on this and basically adjust policy metrics.

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Nichole Sterling (30:20)

So hey, what if we all the reduce the residential footprint for these parcels to 5,000 and we made it so that any ADU could be on a 5,000 square foot parcel because every jurisdiction is different and started to do some toggles there.

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Nichole Sterling (30:35)

And so I was like oh my goodness, that's actually the coolest part and that's not what I intended to started with.

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Nichole Sterling (30:41)

So my takeaway for you is to take one reoccurring problem and solve it six different ways with AI as a team exercise.

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Nichole Sterling (30:52)

Hopefully you'll have some of those failure points because maybe 1, 2 and 3 didn't work out well, but maybe by the time you get to a 6 version there's something interesting and maybe something interesting came out of it that you didn't initially intended.

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Nichole Sterling (31:07)

So six different ways to solve one problem and align on the best pattern and make that a habit.

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Rebecca Hinds (31:12)

I love that.

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Rebecca Hinds (31:13)

And, and so concrete and it aligns with, you know, overwhelmingly there's evidence to suggest that we're more creative, we're more innovative when we have a greater quantity of ideas to work with as well.

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Rebecca Hinds (31:24)

And I think that very much feeds into that.

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Rebecca Hinds (31:26)

So six different ways, I think that's something we can all put into, into practice.

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Rebecca Hinds (31:31)

Well, thank you Shweta.

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Rebecca Hinds (31:32)

Thank you Nichole for joining us.

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Rebecca Hinds (31:33)

Thank you everyone for tuning in.

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Rebecca Hinds (31:35)

I learned a lot.

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Rebecca Hinds (31:36)

I think our audience did as well.

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Rebecca Hinds (31:38)

And stay tuned for the next episode.

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Rebecca Hinds (31:41)

If you enjoyed this episode, subscribe to Intelligence Real and Imagined so you never miss a conversation.

‍

Rebecca Hinds (31:47)

And download the AI Transformation 100 report using the link in the show notes.

‍

Rebecca Hinds (31:52)

Thanks for listening and we'll see you next time.