Boring Problems, Big Wins, Community‑Driven AI Adoption

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Hey crafters.
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Just a reminder, this podcast is for informational entertainment purposes only and should not be considered advice.
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The views and opinions expressed our own and do not represent those of any company or business we currently work with are associated with, or have worked with in the past.
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for tuning in to the FutureCraft podcast.
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Let's get it started.
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uh, uh, uh, um, uh, Hey there.
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Welcome to The Future Craft Go To Market podcast, where we're exploring how AI is changing all things go to market from awareness to loyalty.
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I'm Ken Roden, one of your guides on this exciting new journey.
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And I'm Erin Mills, your co-host, and together we're here to unpack the future of AI and go to market.
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We're gonna share some best practices, how tos, and talk to industry pioneers paving the way in AI and go to market.
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Ken, I usually ask you about how you're paving the way in ai, but I really wanna talk today about is AI being overhyped? Erin, you and everybody else seems to be asking this question.
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MIT recently came up.
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With a study that 95% of generative AI projects are failing.
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And that has caused all of this conversation from, VCs pulling out of investments and rounds of fundraising to companies saying does AI even work? And I think one, we can talk about the validity of the study, but more important.
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There are AI implementations that are working.
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But I do think maybe there was a little bit of an over maybe an over promise of what AI could do and people invested really quickly in it and aren't seeing results.
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But I'm curious what's your take on it? I think one of the things that comes to mind is that everything ultimately is a people problem in a way.
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Like a lot of problems end up coming back to folks, and I think that there is a little bit of, okay, AI is here and so everybody use it.
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Not everybody is gonna be at the same point at which they're using it.
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So if you're putting this really complex problem in the hand of somebody who maybe is really new to ai, like how are you setting them up for success? And I do see that there's a lot of projects that fail without giving people the time to master it.
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Yeah and what does success look like? Is it a revenue target or is it, a decrease in time to market? So I'm curious, like what are these goals that were being set around these initiatives? Yeah, and I think you raise a good point with the, revenue piece because revenue is always a lagging indicator, and so it's a little hard, I think to define upfront when you're trying an experiment with ai.
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Is is it going to yield the results that you have? And it's a little bit like the CEO who says oh, send, a hundred thousand emails and we're gonna get a ton of revenue in.
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And you're like I think that it's.
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It's gotta be a process that that folks there's the early indicators, but then there's a lagging and how do you bridge the two? It takes a little bit of time and effort, and I think that's what people really need to understand.
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But I'm curious, you've been talking to a lot of CEOs and I'm sure the, like the shiny object of AI delivering on revenue has gotta be, at the forefront of their minds.
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But how are they thinking about it? Yeah, so from the executives that I've been talking to, most of them are still pretty gung-ho about the opportunity with ai.
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What I will say though is maybe a little variability by industry, but a lot of them aren't saying that AI is going to give them a 40% increase in revenue or, increase pipeline by, four x.
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They were pretty realistic.
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One of the executives I recently talked to said that they were looking for just a 3% increase in time to market, which is why I keep going back to that one because I thought that was a really.
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Valuable metric because what she did actually is she went in to her her executive team and said.
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I think if we can get 3%, we can drive this much more efficiency.
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It doesn't matter where it comes through.
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It doesn't matter if it comes from product or marketing or sales, but how can we drive this so that we can test out AI before we go heavily implemented.
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And so one of the things I have seen working really well is people not over what is it? What's the expression? Trying to eat the whole elephant.
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They're breaking it down into smaller pieces.
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I don't know why you'd need an elephant, but.
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But so what I think the takeaway is like the, it's not over-hyped, but what I do think you're gonna see less of is people just buying ChatGPT blanket for, their entire org or going all in on a specific tool because, you and I talk about this, but it's just it's not how people are adopting it.
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And really, if you think about it when you're implementing Marketo or Eloqua, you weren't like.
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Rolling it out to all of your divisions globally.
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You were doing a phased rollout and you had to get it started before, pilot it, before you actually rolled it out.
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So that's where we're at.
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What are you seeing and hearing from like the communities you're in and like what you're reading? I don't think it's overhyped, I think that quote from Ethan Malik's book AI is as bad as it's going to be in this very moment, right? And so I think that there is such a promise of all these efficiency gains and not, and we talked about Lisa last year about the, what was it? Multi-trillion dollar opportunity that AI brings Now? Is it gonna bring it in like the next year? Maybe not.
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Like it moves so fast.
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I think that people are struggling a little bit with the uncertainty, and that's why the hype is being questioned a little bit, where it's do we really know where it's going? It reminds me of us talking to Chris Penn a couple weeks ago and we were talking about AI slop, and if you're using.
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Gen AI to create content for your customers.
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That is, it's not gonna help you grow, but find the things that actually can drive the change.
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You're spot on.
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Let's focus on that and that's where you're gonna, that's where the hype's going to.
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There's no hype.
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It's not sexy stuff.
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And I think that's one thing that you hit on, which I always really take to heart, is, find the boring stuff, find the stuff that's repetitive and you don't have to solve the sexiest problem, but just find those little nuggets.
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And maybe, as you build up sort of the AI muscle, then you can take on much bigger projects.
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But the idea of if you can tweak something like a process and make it easier for people, solve that problem and then move on to something that's more complex, that's gonna get your, be able to get your, cut your teeth and then maybe you can deliver those, the promise of ai.
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Exactly.
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Speaking of this topic, do you wanna hear who we're talking to today? Yeah.
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I'm really excited Yeah, we're talking to Sheena Miles and there wasn't really a drum roll to that I guess, but we're talking to Sheena Miles 'cause we both know her.
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We've been talking to her for probably over a year now.
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And she, what's really interesting about her is she not only is involved in a AI community that she's a founder of, but she actually is doing AI transformation in-house.
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And I think that's.
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Particularly interesting 'cause she's got both perspectives.
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So we're gonna be talking to her about how things have been going since she took on this new role.
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Are you Great.
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I'm ready.
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Let's do it.
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Uh, uh, uh, um, uh, And we're back Today, we're joined by Sheena Miles, AI strategy and Operation Manager at NMI she's responsible for integrating AI across both.
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Internal operations and customer facing products beyond her corporate role.
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Sheena co-hosts the Women Defining AI Podcast and leads community initiatives focused on democratizing AI knowledge and creating inclusive pathways for AI adoption, which we love.
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Here at Future Craft, she's developed frameworks that help organizations move from AI curiosity to measurable business transformation with a particular focus on behavioral change over tool obsession.
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In today's conversation, we'll explore her three pathways framework for AI strategy, dive deep into community driven adoption strategies and uncover why most successful AI implementations focused on human behavior rather than just technical capabilities.
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Sheena, thanks for joining us.
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Thank you for having me.
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I'm excited to be here and to talk to you all about this.
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All right let's start about where we are right now.
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So you're seeing AI adoption from multiple angles, whether it's leading implementation at NMI, hosting a podcast about AI and building community around it.
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When you look across all these different perspectives, what's the real state of AI adoption and go to market organizations today? Not the hype, but what's actually happening.
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Yeah, it's not what on LinkedIn every day.
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I'm sure Most organizations I feel are in this dual speed reality, so like tons of enthusiasm on the surface.
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But in practice they're still running small pilots.
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If that with kind of adoption like siloed on the individual champions.
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So you obviously have these people who love it and are in it and doing their thing.
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But then there are so many people who haven't progressed beyond that.
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That's where the real.
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Scaling and transformation happens.
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So I know we're gonna talk about the framework a little later, but like in the framework, it's in the activate area.
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And it's not the worst place to be, it's just not where you can say, oh, we've transformed anything.
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Additionally, when we talk about the community side of it, 'cause I spend so much time in the women defining AI community, we're looking at women and non-binary people who haven't used AI at all.
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Or they're not allowed to use it at work.
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If they are using it at work, it might be an internal tool in building an internal tool, it's usually pretty crappy.
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Yeah, it's, that's a whole different conversation.
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So you just, you don't wanna get left behind, but that's the reality of where people really are.
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You've been in marketing or go to market for over 15 years having this unique role of doing AI implementation in-house.
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How has your perspective on technology adoption changed over the years? Yeah.
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So if you read about AI at all, I read every morning Everything changes so quickly.
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it's not like a traditional rollout, like how you could teach a Salesforce, everybody learns sales.
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Force 'cause that's your tool and you're good to go.
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It's less about teaching the tool itself and really ensuring that people understand the concepts behind them because you want them to adopt the concept of how AI can be used as a partner.
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Not specifically just chat GPT or just clode.
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we have our preferred tools internally, obviously, but we want people to really understand like, hey, if there's a better tool.
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And I wanna experiment with this in order to know whether it's good or not.
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I have to understand the concept behind it and be able to apply that.
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So it's really more about understanding versus here just learn this tool.
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And that's really what has changed because no one really lives in an ecosystem anymore.
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Yeah, it's so true.
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I think it's moving so fast.
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The second you train on one thing, you're onto the next.
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you work in payments, which is a space that trust is absolutely critical.
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People are, hypervigilant.
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In that space, what is your framework for deciding, where to experiment and where to be a little more cautious? Yeah, so we don't necessarily have a framework.
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Everything.
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starts with your AI policy.
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You have to have an AI policy.
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everything's changing quickly, so that policy's gonna change quickly, but you have to have something that people.
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Can look at and rely on, it's just like a maternity leave policy.
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It just has to be there.
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And that can really frame how people experiment.
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we have our security team.
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They look at every tool that people want to use to ingest private data.
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And I say that specifically because if it were every tool.
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They'd be just reviewing tools all day for nothing else.
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so if somebody wants to try this to write a blog post, and their blog post is gonna be public anyway, then they can just try it out.
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But if it's something that's gonna have NMI or private partner data in it, the security team.
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to review that.
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And so that sets up our framework.
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And as long as people are sticking to that and we're constantly reviewing it, we feel like we're in a good place.
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additionally we've taken it a step further where we formed an AI council.
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And there are people on our, from our legal team, specifically within this realm, like legal data compliance, so that they are aware.
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Of projects from the start, it is their job to say, Hey, this one needs a little more review.
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So that way they're not caught off guard our framework is based around security and legal, but not over governance, Yeah, it's really important given how many people are using AI at work and the gap of how many leaders think their employees are using AI it's pretty significant.
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So having that governance and guidance for strict policy.
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gives people that freedom with that clarity of what they're actually able to do and should avoid.
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Exactly and we get questions all the time and they'll come directly to me.
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I feel good about it because I have gotten people that come and say, can I use this? Can I put this in? I never make people feel bad.
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sometimes in my head I'm like, oh, that's obvious.
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You can put it in.
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I'd rather them err on the side of caution because if you don't know, at least I know you're asking and you're thinking about it.
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One of the things you've mentioned in the past is this idea of behavioral staff.
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Holding over feature obsession, and I thought that was a really fascinating way to think about it.
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Can you share a little bit more what you mean by that? Yeah, so just as Erin said earlier, we don't wanna just.
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Focus on tools because you'll be trying a new tool every day it's really about the habits and the structures and the confidence to get people to work in something day to day.
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an example was, I was teaching a colleague a couple weeks ago and she just wanted to learn how to build an agent.
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it's great for me to do these one-on-one sometimes because I.
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Learn this is how I need to teach it.
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what I noticed, I was teaching her and Aiden specifically, and she's like writing down okay, you transform, flatten the data and then you do this.
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I realized she was memorizing steps.
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And it's not about memorizing the steps.
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Again, it's back to the concepts of like inputs in, inputs out.
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And so once you like start forming those habits.
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Those transfer towards any tools.
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So I'm building the same workflow in four different tools, and while there are some nuances it really helps just the behavior piece of I know that this is the process and how it works, those concepts come up over and over.
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Yeah, I think that's so important.
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I feel like people get almost hung up on the, I have to do it this specific way, and AI is not for that.
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I think that being able to train on that, behavioral aspect is super critical.
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Yes.
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It's been very important and those are the people I've seen that have been more successful and can adapt more easily.
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Yeah.
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So switching gears a little bit, you're a huge fan of community.
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I think it's like the best resource that you can have in today's.
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Market, just hearing directly from what people are doing.
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I would love to hear about, the power of community in AI and how, these groups really drive real adoption.
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Most companies don't really think about ruling out new technology in the way of, pooling the resources of the communities out there.
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They just think about training, documentation, maybe change management.
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But why do you think community is so critical for AI adoption? A million reasons.
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I went to school for this.
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I got my master's in it.
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It's a lot of coding and coding inevitably doesn't work.
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You'll never write a piece of code and hit run and it's gonna run the first time.
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So you spend so much time troubleshooting and debugging and wanting to give up for that reason.
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having a community of people In the trenches with you makes such a difference.
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it's also about the accountability piece.
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some of the leaders in the community and women defining AI specifically, are so great at holding us accountable and pushing us, but knowing when to push us.
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An example I'll give you is for me, our founder has been asking me to demo n8n.
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For so long.
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'cause we have the show Don't Tell program.
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She's can you do n8n? You're the one who uses it the most.
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Everybody else is using something else.
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I've said no 10 times.
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Finally last night I was like, okay, fine, I'll do it.
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I kept pushing myself just for this reason, because otherwise I wouldn't have had a reason to get comfortable enough to show it on camera.
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And I'm actually gonna show you today too Awesome.
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What a treat.
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Yeah.
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So yeah, just that accountability piece, having somebody push you, but also these teams aren't big.
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And so when you think of community, it's not just like joining a community, but I started an internal podcast at work Really just a way I use Google Notebook, and I don't know if you've ever done that, where you like generate the podcast.
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And so I would pull in sources and make this, and I wanted to keep up with it.
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And it was difficult.
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because we're two people.
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And so now community.
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I'm like, okay, why don't I just pull episodes from all of these amazing podcasts that other people are doing and just drop them in confluence? there's amazing AI community out there of podcasters that I can use.
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And so yeah, that's another example of using the community to make this easier for everyone.
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I think the podcast format, we love it because we get to talk to people like you and share real examples you've probably seen it on women defining AI hundreds of different AI adoption stories.
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Like they all have a little bit different flavors and people find success maybe with NIN or Lindy or whatever it may be.
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What patterns are you noticing in the people and organizations that successfully integrate AI versus those who struggle? when you're seeing people succeeding or failing, is there anything common amongst those groups? the people that are succeeding, they just don't give up and they build.
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I mentioned that I went to school for it and we had a project.
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We sat in classes and they're like showing the slides.
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that is not how you learn ai.
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at a more.
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Intermediate or advanced level.
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when we seek success internally and community, it's people who are out there building.
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it gets your mind, asking questions that like a basic question I'm putting this question into chat GBT.
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Why is it giving me a different answer every time? Because it's probabilistic, but you don't know that until you start using it.
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And so it's more likely to stick in everyday life I think that having those internal champions is huge.
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So like resources that the team can go to if they have a question, because while we are a two person internal AI team.
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I have a marketing background.
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My director has an engineering background, so that leaves finance, sales, support.
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you need to have a champion for all of these people because resources are limited.
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when you get people who adopt in every area, I think internally and organizations, you really see more success.
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One of the challenges that we're seeing go to market.
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specifically have is they might want to experiment with ai, but everything is so focused on ROI, everything is measured.
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And it can be really challenging to say Hey, you know this group over here, why don't try when there's so much pressure to show results.
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So how do you.
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Give permission to experiment, or how do you get permission to experiment so that we can inspire some people to try stuff out? I think it has to be a top down thing.
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a lot of organizations start bottom up because as individuals we are starting to use these tools in our everyday lives.
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But your leadership has to have one voice of it is okay to experiment.
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And then, we talked about the AI policy.
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It's okay to experiment safely.
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This is how you do it.
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So you know that if you go in, you're not gonna just mess something up or, put a client in jeopardy having that leadership say it's okay to experiment, but also understanding that.
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You still need to define KPIs, but it might not be revenue, ROI.
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And that's okay.
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as long as you define the KPI and you can say what you learned.
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I used a tool for AB testing.
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I learned that this, and I was able to, do it quicker and try more emails or, whatever you're AB testing.
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I think it's important to define the KPI and understand that it's not gonna always be revenue based, so you still need to measure something, as long as you have a learning there, I think that's where you get to a safe place of experimentation.
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there are so many revenue based KPIs that we're all trying to meet that it is hard to walk away from those.
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But if you have one voice saying, Hey guys, it's okay, as long as you can show me you learn something you're in a much better position what I've been seeing working is focusing on either productivity or efficiency.
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if you can say, we were able to go to market faster because of this, that.
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Is okay that you don't have a, hey, we increased pipeline by 40%.
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it gives a more realistic goal for teams.
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I think that's a really good point.
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Or like engineers in their sprints, how much more did you finish in this sprint? Did you get one more thing done than you did the last sprint? Or we thought we would get this much done, but then you exceeded it.
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those are all perfectly fine measurements.
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Of understanding whether something is working or not.
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Yeah.
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And I think those efficiencies can often yield revenue outcomes, right? Like you build an extra product that year or a new feature that you weren't gonna be able to get out.
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ultimately, I think when you look back as a whole, if you're using it correctly and measuring correctly, you are gonna see those revenue based outcomes as well.
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Yeah, I totally agree.
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at Future Craft, we love a really good framework, one I love that you developed is that three pathways framework I know it's a little tongue twister.
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I also love alliteration.
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So activate, amplify, accelerate.
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Love that as a model for scaling AI efforts based on maturity of an organization.
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And so can you walk us through how that works in action and how would a go to market team use this to assess where they are, but also more importantly, where they wanna go? it's less about understanding how mature you are as an organization and where each.
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I wanna say project each initiative is like in what stage an initiative kind of lives.
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And so the first part of it, and I'll give you examples, and they're not all connected, but I was trying to come up with easy examples.
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The first part would be like, activate.
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And this is exploring and validating an idea.
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So like I come to you, I have an idea, it's early stage AI initiative.
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It could be any initiative, but we're talking about AI here, so we really need a proof of concept.
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So I, and I will give you an internal example.
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our compliance team, and I, this is probably every team you're getting the form that people need you to fill out to say Hey, what do you use? How do you use our data? Blah, blah, blah.
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And so the team kept seeing the same questions over and over, but they were filling out each form separately.
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And so we were looking for something that that could.
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That could access a knowledge base of questions and answers that have already been written and say Hey, let's get us halfway there.
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Get us 80% there.
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And so each stage of the framework has an execution piece like the AI opportunity canvas.
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So what you'd be looking at is what's the exact problem? Is AI a fit? Can we define a quick pilot? can we run this for example, with a small batch of requests and capture how much time was saved, how many more did we get out? How many questions did it accurately answer? what level of accuracy do I need? With legal compliance, it's pretty important.
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you really want to be strict about that.
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So that's the first piece and that's an example of how you would run something and activate using a third party tool.
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Let's try a few and see how it works.
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And then you move to amplify.
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Amplify is really about, screaming it from the rooftops.
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I want to expand this across teams and make this better.
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this is for projects that are already showing value, but they haven't yet scale.
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And the execution piece here is really, like I said, cross team adoption.
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Governance and shared learning.
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in our situation, one of the things we could do is identify transaction issues before a partner raises them.
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And so obviously who gets an issue first support they're the first stop, so they know everything that happens, Definitely hard for other teams to go in and reach support tickets in the same way.
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So How do we scale this a little bit? this would look like connecting teams.
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payments, compliance, partner success.
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how do we connect all of these teams to make sure that they have visibility and then test collaboratively? how do we run pilots across teams? And then how do we layer in that light governance at first? Depending on the risk, if it's more risky, this is where compliance would come in and say, we really need to get a handle on this.
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And so that's how we can move from a single team success Repeated capability that the whole organization can trust.
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That's the amplify stage.
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then we get to accelerate, which is really about scaling and operationalizing and institutionalizing to make it enterprise wide.
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This is really about focusing on ownership.
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Who's gonna own it? What team is it gonna be the person to go to.
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So let's assign the AI ad copy process to demand gen with content as a quality gate.
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And then training, like every marketer has to be trained in campaign creation.
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And then integration.
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how do these tools all connect to each other? And then measurement, obviously this is where we talk about this is R oi is pretty important here 'cause we're out of that pilot phase and really into.
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Scaling it.
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If there's no ROI, we shouldn't be at this point with any process.
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And then how do you roll that out to, let's say, all digital channels? So how do we get on the same process for Google Ads and Facebook and newsletters? So that means the tools are no longer in pilot and they are just in everyday process.
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So that's how we look at it to make sure things can grow without spending too much money or resources on the front end.
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Helpful though I think everybody's trying to figure it out and it's like the community conversation.
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If you can try a framework somebody else has done that's worked well and you're struggling to adopt, give it a shot.
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Yeah.
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What can it hurt? And then that's the whole point of discipline is you get that small piece first.
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So if it's not working, you don't take it any further.
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Yeah.
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Yeah, so one of the segments and you referenced it earlier, we're very excited you're gonna be showing us an n8n workflow.
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I'm a huge fan of n8n I've been trying to convince Ken for a year now.
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maybe this is gonna be the turning point, Ken.
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You're gonna see Sheena do it, and it's gonna be a big thumbs up and you're gonna dive in.
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I feel it.
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I'm ready Okay.
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Are you ready to take the gladiator challenge? Yes, I am.
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All right, so we're gonna share my screen now.
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Okay.
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So I have to piece it out and do a little bit before, otherwise we'd be here all day.
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So let's share my screen here.
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Did you pick it up on your own or did you follow, how'd you get into it? My manager made me, Are you seeing the clo text things? Are you seeing the right thing? Okay.
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so again, software engineer, he's oh, this is so low code.
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We everybody can pick this up.
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No, not everybody.
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We just had a conversation today about another tool we might use, what I like about n8n is as it scales it's very cost effective.
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I'm not gonna name the competitors they are better for people who don't code at all.
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But this one, if you're building a lot of workflows, is very cost effective.
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one of the things I wanted to show you, and this is what we always talk about with ai, is you should not start from a blank page.
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there's so many options.
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for our podcast, we were trying to automate.
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Creating assets.
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for every podcast we obviously create a LinkedIn post, we create a description we create all these assets and every, each time someone's going in, putting in the prompt, blah, blah blah.
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So what we wanted to do was yeah, we wanted to automate that.
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funny enough, the other day I was on LinkedIn and a guy posted this prompt for aid I never try those 'cause everybody's just trying to sell something.
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but he didn't make you say prompt in the comments or anything like that.
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He was just like, here it is.
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So I decided to try it.
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So I will share all of this with you so you can share with your community.
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But this is his original prompt.
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It's just role tasks, requirements, output, style, and expandability.
320
00:29:09,720.6200476 --> 00:29:12,0.6200476
And so breaking it down is really helpful.
321
00:29:12,190.6200476 --> 00:29:16,690.6200476
what I did was I took that and then I put that into chat, GPT.
322
00:29:17,20.6200476 --> 00:29:22,955.6200476
Now, I had already created a prompt for how I want you to build a LinkedIn post.
323
00:29:23,335.6200476 --> 00:29:25,345.6200476
those all had to be created separately.
324
00:29:25,345.6200476 --> 00:29:28,705.6200476
I want to be clear about that, but anybody can go in and do that.
325
00:29:28,755.6200476 --> 00:29:31,365.6200476
Give me five points for X, Y, Z.
326
00:29:31,935.6200476 --> 00:29:47,415.6200476
So here we are, this is the final prompt that I got for N eight this was after taking this original prompt telling you what I wanted to do, going back and forth Important here was that you wanted a workflow that's adjacent skeleton.
327
00:29:47,505.6200476 --> 00:29:52,995.6200476
So we wanted to be sure that we did that, because I want to be able to upload that file.
328
00:29:53,485.6200476 --> 00:30:00,145.6200476
what I'm going to do is take this and copy it into Claude here.
329
00:30:01,960.6200476 --> 00:30:10,40.6200476
And this is specific to Claude because I did try this in chat GPT and it gave me incorrect code over and over.
330
00:30:10,100.6200476 --> 00:30:14,180.6200476
So I am going to focus that I'm using Claude Opus 4.1
331
00:30:14,630.6200476 --> 00:30:15,890.6200476
and it works correctly.
332
00:30:16,200.6200476 --> 00:30:23,460.6200476
before I added it, I forgot to add this, and this is important that I can import directly, with coordinated notes.
333
00:30:23,560.6200476 --> 00:30:25,540.6200476
Community members, have a really big community.
334
00:30:25,540.6200476 --> 00:30:30,490.6200476
They can build notes and so your instance won't come with those notes.
335
00:30:30,730.6200476 --> 00:30:34,270.6200476
if you don't specify this, then you'll get things that won't work.
336
00:30:34,700.6200476 --> 00:30:37,490.6200476
it's giving me all the code I need here.
337
00:30:40,225.6200476 --> 00:30:54,915.6200476
Isn't that incredible? Can you imagine 10 years ago or even five years ago, being able to see that level of code get developed that quickly by a non-engineer? Exactly, and even by an engineer I actually don't know how long this would've taken for me.
338
00:30:54,915.6200476 --> 00:30:58,95.6200476
It would've been like, I don't know, two months.
339
00:30:58,425.6200476 --> 00:30:59,775.6200476
not have happened if it was for me.
340
00:30:59,775.6200476 --> 00:30:59,935.6200476
Yeah.
341
00:31:00,340.6200476 --> 00:31:02,920.6200476
For a good engineer, probably just a couple of days.
342
00:31:02,920.6200476 --> 00:31:08,240.6200476
But so what we're gonna do once it's done is we need to change it.
343
00:31:08,240.6200476 --> 00:31:09,440.6200476
It shouldn't take too long.
344
00:31:09,860.6200476 --> 00:31:12,140.6200476
Is this the old one? Okay.
345
00:31:12,380.6200476 --> 00:31:13,40.6200476
Let me see.
346
00:31:13,100.6200476 --> 00:31:15,755.6200476
It's still problem we, we have editing software, so no big deal.
347
00:31:16,340.6200476 --> 00:31:17,540.6200476
It's still all right.
348
00:31:17,600.6200476 --> 00:31:19,280.6200476
We'll let it continue to work.
349
00:31:19,670.6200476 --> 00:31:21,350.6200476
Oh, actually, here we go.
350
00:31:24,200.6200476 --> 00:31:24,530.6200476
All right.
351
00:31:24,530.6200476 --> 00:31:28,940.6200476
So I think this is when that is ready.
352
00:31:28,940.6200476 --> 00:31:30,470.6200476
All right, so we're gonna copy that.
353
00:31:30,750.6200476 --> 00:31:33,330.6200476
then you need to change it to adjacent file.
354
00:31:33,380.6200476 --> 00:31:37,430.6200476
I have a Mac, I use text, edit, paste it here.
355
00:31:37,530.6200476 --> 00:31:40,110.6200476
we have to make it plain text, so that's important.
356
00:31:41,385.6200476 --> 00:31:44,535.6200476
And then we are gonna file, we're gonna save it with text edit.
357
00:31:44,535.6200476 --> 00:31:48,825.6200476
You have to be careful 'cause you can save it here, but you can't change the extension.
358
00:31:49,105.6200476 --> 00:32:03,595.6200476
I'm gonna save here, and then we're gonna save podcast transcripts or podcast code n8n.
359
00:32:03,775.6200476 --> 00:32:06,55.6200476
And then I'm removing this extension.
360
00:32:06,970.6200476 --> 00:32:09,430.6200476
And changing it to adjacent.
361
00:32:11,410.6200476 --> 00:32:12,550.6200476
So now we have that.
362
00:32:13,60.6200476 --> 00:32:17,320.6200476
So then we pop into n8n and I've already opened a new workflow.
363
00:32:17,320.6200476 --> 00:32:21,960.6200476
So you would just open a new workflow, go, there are no steps, you just hit Create workflow.
364
00:32:22,860.6200476 --> 00:32:23,820.6200476
And here we go.
365
00:32:23,820.6200476 --> 00:32:30,510.6200476
We are gonna import this file that we just created and open.
366
00:32:34,905.6200476 --> 00:32:35,565.6200476
There it is.
367
00:32:35,925.6200476 --> 00:32:36,215.6200476
What.
368
00:32:36,450.6200476 --> 00:32:36,865.6200476
So cool.
369
00:32:37,395.6200476 --> 00:32:39,15.6200476
that's your whole workflow now.
370
00:32:39,115.6200476 --> 00:32:41,275.6200476
It is obviously not done.
371
00:32:41,435.6200476 --> 00:32:47,215.6200476
So everything within X needs, some love here, but it's things like credentials.
372
00:32:47,215.6200476 --> 00:32:50,5.6200476
if you're trying to get something from Google Drive, you have to credential it.
373
00:32:50,5.6200476 --> 00:32:56,235.6200476
You have to tell what folder you want it from and then if it's a folder, what file? So that's what these things are.
374
00:32:56,295.6200476 --> 00:33:06,985.6200476
You might need to write a message here, but trust me when I say this far, probably saves a good couple of hours easily.
375
00:33:07,285.6200476 --> 00:33:08,245.6200476
Yeah, countless.
376
00:33:08,320.6200476 --> 00:33:08,890.6200476
Yes.
377
00:33:09,80.6200476 --> 00:33:11,900.6200476
they probably make it a little more complicated than they need to.
378
00:33:12,60.6200476 --> 00:33:32,880.6200476
Because I did try to build this from scratch and I did not have all these notes, but I'd rather have everything there and delete versus have to try to figure out where I need to transform data, which is like the bane of my existence is why I can, this is text and this is Jason, and this is an array, and I don't know what can read what, so this saves us all that time.
379
00:33:32,930.6200476 --> 00:33:33,155.6200476
it's crazy.
380
00:33:33,295.6200476 --> 00:33:33,515.6200476
All.
381
00:33:34,10.6200476 --> 00:33:34,400.6200476
shared.
382
00:33:34,700.6200476 --> 00:33:42,830.6200476
It's just crazy how accessible things like n8n make building well nodes into workflows.
383
00:33:43,70.6200476 --> 00:33:50,990.6200476
Like just in, was that even minutes, seconds? It only took a little longer because I was trying to be clean about how I showed it.
384
00:33:51,710.6200476 --> 00:33:52,40.6200476
Yeah.
385
00:33:53,690.6200476 --> 00:33:56,800.6200476
But yeah, five minutes and we were up, Yeah.
386
00:33:56,930.6200476 --> 00:33:57,860.6200476
Thank you for showing us that.
387
00:33:57,860.6200476 --> 00:33:58,165.6200476
That was really cool.
388
00:33:58,605.6200476 --> 00:33:59,415.6200476
of I love it.
389
00:33:59,670.6200476 --> 00:34:00,600.6200476
I'm gonna give you a thumbs up.
390
00:34:00,600.6200476 --> 00:34:05,340.6200476
I will say I am more inspired to try it after that.
391
00:34:05,565.6200476 --> 00:34:06,735.6200476
Now All right.
392
00:34:06,780.6200476 --> 00:34:07,380.6200476
from scratch.
393
00:34:07,995.6200476 --> 00:34:08,700.6200476
Hey, that's a win.
394
00:34:11,65.6200476 --> 00:34:20,745.6200476
The other cool thing is even if you didn't have the doc, you could screenshot what Sheena did, and then drop that into Claude or chat to Yeah.
395
00:34:20,795.6200476 --> 00:34:21,425.6200476
it's unbelievable.
396
00:34:21,815.6200476 --> 00:34:23,585.6200476
Yeah, I know.
397
00:34:24,360.6200476 --> 00:34:24,810.6200476
Wow.
398
00:34:24,990.6200476 --> 00:34:25,350.6200476
Okay.
399
00:34:25,350.6200476 --> 00:34:28,530.6200476
Before I get overwhelmed on how I'm gonna use it I wanna talk a little bit about.
400
00:34:29,295.6200476 --> 00:34:43,215.6200476
What you're doing in your role, one of the things you've said before is there is a difference between an AI first organization and an AI native organization for established GTM teams that are working on a transformation.
401
00:34:43,425.6200476 --> 00:34:50,495.6200476
What are the key shifts they need to think about and what goes into the day-to-day change for them? This really goes back to, leadership.
402
00:34:50,495.6200476 --> 00:34:51,725.6200476
It has to come from the top.
403
00:34:51,765.6200476 --> 00:34:53,835.6200476
We are all running on lean teams.
404
00:34:54,85.6200476 --> 00:35:00,80.6200476
if the expectation isn't set early that this is what we're going to use, then nobody's gonna stop and use it.
405
00:35:00,80.6200476 --> 00:35:01,910.6200476
that looks different on every team.
406
00:35:02,100.6200476 --> 00:35:09,990.6200476
you have to figure out how you measure it and how you don't, and maybe that is incorporating that into reviews after a while.
407
00:35:09,990.6200476 --> 00:35:11,430.6200476
you set the expectation early.
408
00:35:11,790.6200476 --> 00:35:15,680.6200476
You talk about it in your quarterly reviews or however often you do it.
409
00:35:15,680.6200476 --> 00:35:19,940.6200476
then eventually it's okay, now we're documenting it.
410
00:35:20,110.6200476 --> 00:35:26,590.6200476
But then I think the other piece is embedding it into a workflow and not just on the side.
411
00:35:26,640.6200476 --> 00:35:35,340.6200476
I'll give an example, I built a workflow for our content team, but it is great for our sales enablement team as well, and it is taking insights from Gong.
412
00:35:35,340.6200476 --> 00:35:37,590.6200476
That's what we use for recordings for our sales team.
413
00:35:37,710.6200476 --> 00:35:39,690.6200476
Taking insights from Gong and having.
414
00:35:40,220.6200476 --> 00:35:47,130.6200476
AI help understand where there's constant confusion across the board, like our partners asking the same questions over and over.
415
00:35:47,500.6200476 --> 00:35:55,930.6200476
So pulling that out and then taking those insights and saying, okay, content team, sales enablement team, this is where gaps are.
416
00:35:55,990.6200476 --> 00:35:58,0.6200476
This may be where we can write more content.
417
00:35:58,340.6200476 --> 00:35:59,510.6200476
that's a great first step.
418
00:35:59,560.6200476 --> 00:36:01,815.6200476
the ultimate transformation step would be okay.
419
00:36:02,480.6200476 --> 00:36:08,90.6200476
Maybe there's a human reviewer and you click, you check and you say, Hey, we wanna, follow up on these topics.
420
00:36:08,400.6200476 --> 00:36:12,100.6200476
then it creates the blog the white paper the sales enablement deck.
421
00:36:12,380.6200476 --> 00:36:15,20.6200476
a LinkedIn post and pushes that LinkedIn post out.
422
00:36:15,110.6200476 --> 00:36:18,290.6200476
And obviously you can put the humor reviewer in wherever you want.
423
00:36:18,560.6200476 --> 00:36:19,580.6200476
But that's embedding it.
424
00:36:19,580.6200476 --> 00:36:23,990.6200476
That's not taking it from the side, reading it and saying, okay, now we're gonna do this.
425
00:36:23,990.6200476 --> 00:36:26,210.6200476
I think that's where the transformation really happens.
426
00:36:26,745.6200476 --> 00:36:39,380.6200476
Do you have any advice for someone who might not have the authority but is trying to drive adoption or get AI implemented at their company? Yeah, I would say just start building if you can.
427
00:36:39,380.6200476 --> 00:36:41,300.6200476
Solve a problem that needs to be solved.
428
00:36:41,300.6200476 --> 00:36:42,470.6200476
And it's been sitting on the side.
429
00:36:42,470.6200476 --> 00:36:45,950.6200476
People have been pushing it out, just say Hey, this is what I did.
430
00:36:46,230.6200476 --> 00:36:48,990.6200476
solving a problem for a champion is huge.
431
00:36:48,990.6200476 --> 00:36:54,90.6200476
somebody who has a louder voice than you can say, Hey, everybody, look at this.
432
00:36:54,400.6200476 --> 00:37:00,540.6200476
we were working on a project as a team and our VP of success loved it he.
433
00:37:00,860.6200476 --> 00:37:04,280.6200476
Is a loud voice, and that really got us going.
434
00:37:04,280.6200476 --> 00:37:08,915.6200476
if you feel like you don't have influence, solve a problem for somebody who does have influence.
435
00:37:10,60.6200476 --> 00:37:11,680.6200476
I think that's a really sharp idea.
436
00:37:11,860.6200476 --> 00:37:17,170.6200476
solve the most boring, least sexy problem because it's probably one of the biggest pains that companies have.
437
00:37:17,200.6200476 --> 00:37:20,940.6200476
But I like finding someone with a big voice who has a problem that AI can solve.
438
00:37:20,940.6200476 --> 00:37:22,230.6200476
I think that's phenomenal.
439
00:37:22,385.6200476 --> 00:37:22,675.6200476
Yeah.
440
00:37:23,700.6200476 --> 00:37:26,730.6200476
The future of AI at work.
441
00:37:26,830.6200476 --> 00:37:33,880.6200476
you've started women defining AI and you're working to change who defines and leads the conversations around innovation.
442
00:37:34,240.6200476 --> 00:37:41,925.6200476
Why do you think this perspective and the work you're doing is especially important right now? Because women aren't going anywhere.
443
00:37:45,395.6200476 --> 00:37:58,565.6200476
It's the same thing as why is diversity important in anything else? diverse voices ensure representation for different genders, different cultures and it gets problem solved that otherwise might not be solved.
444
00:37:58,845.6200476 --> 00:38:05,255.6200476
it changes the messaging and women typically get the brunt of the household mental load.
445
00:38:05,255.6200476 --> 00:38:12,115.6200476
so everybody just started school and our founder built a little bot She takes a picture of a note and it puts it into that.
446
00:38:12,295.6200476 --> 00:38:24,35.6200476
And it just really helps her figure out, what do I need to do now? Do we need to sign this permission slip tonight? And then what can I do later? I'm not saying that there are not men out there who don't take on this mental load.
447
00:38:24,255.6200476 --> 00:38:31,935.6200476
There definitely are, but we all know it usually falls to women, and solving those problems that otherwise probably would not get solved.
448
00:38:32,145.6200476 --> 00:38:36,995.6200476
Having women being a voice makes this, so important for that reason.
449
00:38:38,705.6200476 --> 00:38:49,300.6200476
Yeah, one of the stats that you cited is sad, but also opens up a lot of opportunity is that only 35% of women compared to 52% of men are using AI at work.
450
00:38:49,600.6200476 --> 00:38:52,990.6200476
And it's creating this, big digital divide.
451
00:38:52,990.6200476 --> 00:39:02,960.6200476
there was girls who code and there was so much emphasis for a while on women getting into more technical roles and there was some momentum, It feels like maybe that's widening again.
452
00:39:03,260.6200476 --> 00:39:16,80.6200476
So for go to market listeners or leaders who are listening, why should they really care about, making sure that women and non-binary folks are included and go beyond doing it because it's the right thing.
453
00:39:16,390.6200476 --> 00:39:16,680.6200476
Yeah.
454
00:39:17,140.6200476 --> 00:39:19,540.6200476
I'll repeat it again because women aren't going anywhere.
455
00:39:19,730.6200476 --> 00:39:24,45.6200476
if you have 50% of your workforce that can't contribute as quickly.
456
00:39:25,205.6200476 --> 00:39:27,425.6200476
As a company, you're falling behind.
457
00:39:27,475.6200476 --> 00:39:31,945.6200476
it's just like having Gen Z or boomers learn something.
458
00:39:32,75.6200476 --> 00:39:44,455.6200476
Everybody is here to stay and if you have an entire group of people that can't use a tool or can't produce at the highest level, then you're missing.
459
00:39:44,765.6200476 --> 00:39:46,115.6200476
a huge opportunity.
460
00:39:46,605.6200476 --> 00:39:49,965.6200476
And it's not just from a I Care because it's a right thing to do.
461
00:39:50,15.6200476 --> 00:39:59,225.6200476
if you are a business leader and care about making money, you want all the people that work for you to be able to make you money in the easiest, quickest, best way possible.
462
00:40:00,630.6200476 --> 00:40:10,760.6200476
Yeah, there's definitely economic impact and value in having more people on your team knowing how to use AI that can drive efficiency, productivity, potentially revenue growth.
463
00:40:10,810.6200476 --> 00:40:11,500.6200476
Makes sense.
464
00:40:11,740.6200476 --> 00:40:14,590.6200476
Now, you talked about my favorite topic, which is learning.
465
00:40:14,750.6200476 --> 00:40:41,695.6200476
So what skills do you think people need to be developing now to stay relevant? prompting that's a basic and not the technical pieces, not the chain of thought or anything like that, but really just basic, How do you ask it a question? How do you talk to it? So I'm gonna use my parents as an example, my mom called me and she's where am I supposed to put the question? I'm like, you just talk to, you know how you like to tell me to do things.
466
00:40:41,845.6200476 --> 00:40:44,575.6200476
Just talk to it like that and pretend it's me.
467
00:40:44,700.6200476 --> 00:40:45,785.6200476
A parent's dream.
468
00:40:46,125.6200476 --> 00:40:46,635.6200476
Yes.
469
00:40:46,725.6200476 --> 00:40:47,415.6200476
And it worked.
470
00:40:47,415.6200476 --> 00:40:48,15.6200476
She got it.
471
00:40:48,15.6200476 --> 00:40:50,235.6200476
She was like, okay, I got this and we were good.
472
00:40:50,685.6200476 --> 00:40:57,815.6200476
But it just I think that it's also prompting in that you understand that it's not, don't take it at face value.
473
00:40:57,815.6200476 --> 00:40:59,705.6200476
Always take it a step further.
474
00:40:59,945.6200476 --> 00:41:03,665.6200476
Ask it to tell you something that maybe you didn't think about asking.
475
00:41:03,665.6200476 --> 00:41:07,925.6200476
It's like understanding that you can have different conversations with it.
476
00:41:08,205.6200476 --> 00:41:13,535.6200476
And then other skills, I'd say rabbit testing, like you need to be able to pivot really quickly.
477
00:41:13,655.6200476 --> 00:41:14,645.6200476
This may not work.
478
00:41:14,645.6200476 --> 00:41:16,745.6200476
Don't hold onto something that's not working.
479
00:41:16,985.6200476 --> 00:41:18,785.6200476
I think that's a really big skill.
480
00:41:19,45.6200476 --> 00:41:28,465.6200476
I'd say also being able to communicate your thought process as well, because when I build these workflows, I don't just.
481
00:41:28,835.6200476 --> 00:41:30,755.6200476
Sit down and start pulling notes.
482
00:41:30,755.6200476 --> 00:41:38,45.6200476
I have to think about what's my end problem that I'm trying to solve, and then work backwards from there and start building.
483
00:41:38,105.6200476 --> 00:41:39,545.6200476
Otherwise, I'll be here all day.
484
00:41:39,595.6200476 --> 00:41:47,885.6200476
if you can't, communicate your thought process because you have to communicate it to AI and understand the problem that you're trying to solve.
485
00:41:48,45.6200476 --> 00:41:49,275.6200476
it's not gonna work for you.
486
00:41:49,870.6200476 --> 00:41:50,160.6200476
Yeah.
487
00:41:50,840.6200476 --> 00:41:51,990.6200476
Yeah, I think that's such a good point.
488
00:41:51,990.6200476 --> 00:41:57,630.6200476
So many people are looking for a problem to solve when they get into AI versus starting with a problem.
489
00:41:57,810.6200476 --> 00:42:03,100.6200476
Yes, starting with the most boring unsexy just pick a problem.
490
00:42:03,130.6200476 --> 00:42:11,210.6200476
And it's so funny that you use those words because I went to a talk, and it was the CTO or CIO of Databricks.
491
00:42:11,375.6200476 --> 00:42:17,315.6200476
he had owned a company before and sold it for billions of dollars, and he was like, I just solved a boring problem.
492
00:42:17,315.6200476 --> 00:42:19,355.6200476
Like it wasn't even a fun problem.
493
00:42:19,655.6200476 --> 00:42:24,390.6200476
And yeah, the boring problems are out there to be solved, and AI is a perfect way to do it.
494
00:42:25,585.6200476 --> 00:42:37,800.6200476
One of the things thinking ahead and building community I'm curious what you are most optimistic about, where is AI headed in the next couple of years? What are you personally excited? I think a couple of things.
495
00:42:37,860.6200476 --> 00:42:41,400.6200476
Just watching Women Build has been amazing.
496
00:42:41,400.6200476 --> 00:42:43,440.6200476
I've been in women defining AI for a year.
497
00:42:43,470.6200476 --> 00:42:45,690.6200476
We've had women win competitions.
498
00:42:45,690.6200476 --> 00:42:48,320.6200476
one lady entered her alumni competition.
499
00:42:49,110.6200476 --> 00:42:53,820.6200476
And won a hundred thousand dollars for funding now I see her on LinkedIn posting about her product.
500
00:42:53,820.6200476 --> 00:42:55,800.6200476
It's called potluck if anybody wants to check it out.
501
00:42:56,170.6200476 --> 00:43:01,870.6200476
Another lady just started and built an app that shows where in New York.
502
00:43:02,265.6200476 --> 00:43:06,475.6200476
You can bring your dog and work it's the coolest thing, people love their dogs.
503
00:43:06,475.6200476 --> 00:43:08,35.6200476
So I was like, you need to monetize this.
504
00:43:08,725.6200476 --> 00:43:13,915.6200476
And so just watching these ideas come up and I'm gonna throw it out there and I'm gonna fix it.
505
00:43:13,915.6200476 --> 00:43:15,295.6200476
So I'm really excited about that.
506
00:43:15,685.6200476 --> 00:43:23,195.6200476
And then another thing is really being able to close the economic gap in underserved communities.
507
00:43:23,195.6200476 --> 00:43:33,645.6200476
So whether that's women, people of color, people who traditionally haven't gotten opportunities or haven't had access to the education because of the money that it requires.
508
00:43:33,675.6200476 --> 00:43:34,965.6200476
It's like an equalizer.
509
00:43:34,965.6200476 --> 00:43:36,795.6200476
And yeah, there's still things you need.
510
00:43:36,795.6200476 --> 00:43:37,725.6200476
You need a computer.
511
00:43:37,945.6200476 --> 00:43:42,955.6200476
You need to be able sometimes to buy a pro version of something, but That's.
512
00:43:43,940.6200476 --> 00:43:54,440.6200476
A much smaller barrier than a 50,000 a year education, I'm really excited about the possibility of it closing that economic gap as well.
513
00:43:54,950.6200476 --> 00:43:55,240.6200476
Yeah.
514
00:43:55,245.6200476 --> 00:43:55,335.6200476
Yeah.
515
00:43:55,465.6200476 --> 00:43:56,5.6200476
I like that.
516
00:43:56,260.6200476 --> 00:43:58,990.6200476
Access, accessibility and democratization of it all.
517
00:43:58,990.6200476 --> 00:44:03,680.6200476
it could really change what people can do and empower them to change their lives.
518
00:44:04,295.6200476 --> 00:44:05,705.6200476
Yeah, I'm a perfect example.
519
00:44:05,705.6200476 --> 00:44:07,505.6200476
I got my master's in coding.
520
00:44:07,940.6200476 --> 00:44:13,250.6200476
There was some crying, a lot of it, oh my God, it was so bad.
521
00:44:13,470.6200476 --> 00:44:20,370.6200476
this was 2019, before Gen ai and I was like that was fun, but I guess I'm just gonna go back into marketing because this is not something I'm gonna do.
522
00:44:20,760.6200476 --> 00:44:23,130.6200476
And here comes Chad, GBC and Gen ai.
523
00:44:23,130.6200476 --> 00:44:27,960.6200476
so I understood the concepts and it was an easier transition for me, but without this.
524
00:44:28,120.6200476 --> 00:44:29,170.6200476
I would still be in marketing.
525
00:44:29,200.6200476 --> 00:44:30,400.6200476
Not that's the worst thing.
526
00:44:30,430.6200476 --> 00:44:35,890.6200476
I may go back into it, but it has opened so many doors and I've had the access that I needed.
527
00:44:35,920.6200476 --> 00:44:39,140.6200476
But it definitely, brought this closer to home for me.
528
00:44:39,845.6200476 --> 00:44:42,635.6200476
Erin, we have to figure out where we can work with our dogs.
529
00:44:44,380.6200476 --> 00:44:49,240.6200476
I'll send you a link to the app if it, I don't even know if she's made it public yet, but she posted it on LinkedIn.
530
00:44:49,240.6200476 --> 00:44:50,830.6200476
I'll send her LinkedIn posts to you.
531
00:44:50,995.6200476 --> 00:44:59,95.6200476
Hey, Sheena, to wrap things up, we like to provide our listeners quick bits of knowledge so that they can implement AI in their day-to-day.
532
00:44:59,95.6200476 --> 00:45:01,690.6200476
So are you up for a lightning round? Let's do it.
533
00:45:02,230.6200476 --> 00:45:03,100.6200476
I'll keep it short.
534
00:45:03,355.6200476 --> 00:45:13,55.6200476
What is an AI tip for marketing or sales teams that others might not think about? I would say you can build a GPT.
535
00:45:13,475.6200476 --> 00:45:15,935.6200476
And have voice conversations with it.
536
00:45:15,935.6200476 --> 00:45:19,325.6200476
like training and practicing your sales pitches.
537
00:45:19,605.6200476 --> 00:45:25,305.6200476
So you give that GPCA role and you say, and you can give it all the information you wanna give it.
538
00:45:25,305.6200476 --> 00:45:30,435.6200476
So obviously it can correct you, but having it ask questions over and over in different ways.
539
00:45:30,715.6200476 --> 00:45:35,305.6200476
And I will say I tried this with a different tool, not A GPT, but this is an easy way to do it.
540
00:45:35,635.6200476 --> 00:45:38,305.6200476
I was am not a salesperson, I failed.
541
00:45:38,615.6200476 --> 00:45:46,745.6200476
Measurably, but to get people like to be able to do that quickly in practice, I think that's a great use of AI for go-to-market teams.
542
00:45:47,675.6200476 --> 00:45:52,270.6200476
What's a GTM or AI Transformation Hill you will die on.
543
00:45:53,805.6200476 --> 00:45:56,145.6200476
This is probably trauma induced, but.
544
00:45:57,280.6200476 --> 00:45:57,570.6200476
Okay.
545
00:45:58,10.6200476 --> 00:46:00,860.6200476
It's anybody's job, it is tied to NRR.
546
00:46:00,890.6200476 --> 00:46:10,885.6200476
So net retention, revenue, making sure customers don't churn, usually it is only the customer success team that gets this metric, but.
547
00:46:12,400.6200476 --> 00:46:17,560.6200476
can only do so much if the sales team sold the wrong product or told them they could do something that we can't.
548
00:46:17,560.6200476 --> 00:46:21,970.6200476
Or if products like, I'm taking this off the roadmap, there's nothing I can do about this.
549
00:46:22,30.6200476 --> 00:46:26,500.6200476
So if anybody has that metric, everybody should have that metric.
550
00:46:26,975.6200476 --> 00:46:27,500.6200476
I like that.
551
00:46:27,795.6200476 --> 00:46:41,385.6200476
What's overrated in AI adoption for business right now? Expecting to hire one person that can do it all from go to market, to coding, to data, to knowing everything that happens in ai.
552
00:46:41,385.6200476 --> 00:46:47,985.6200476
I saw a job posting and the guy was like, I am hiring for someone who every day is up to date on everything happening in ai.
553
00:46:48,15.6200476 --> 00:46:51,345.6200476
And I'm like I wouldn't set myself up for failure in that job.
554
00:46:51,345.6200476 --> 00:46:53,990.6200476
So yeah, that is definitely overrated right now.
555
00:46:54,715.6200476 --> 00:47:02,665.6200476
What's the best professional advice you've ever received? Invest in your own brand, hence me being here right now.
556
00:47:02,945.6200476 --> 00:47:03,875.6200476
I love my job.
557
00:47:03,875.6200476 --> 00:47:11,625.6200476
I love what I do, but putting myself out there in an authentic way and my own brand has opened so many doors and I've met so many cool people.
558
00:47:12,220.6200476 --> 00:47:24,205.6200476
What's one thing you wish you could DM to every go-to-market professional right now? If you haven't started on ai, you're behind let your employees use external tools, just be smart about it.
559
00:47:24,205.6200476 --> 00:47:31,105.6200476
But let, we're not gonna pretend your internal chat bot is better than chat GPC for the most part.
560
00:47:33,505.6200476 --> 00:47:33,805.6200476
That's it.
561
00:47:37,60.6200476 --> 00:47:38,380.6200476
Sheena, this has been great.
562
00:47:38,440.6200476 --> 00:47:41,260.6200476
I've learned so much and I think our listeners have too.
563
00:47:41,260.6200476 --> 00:47:41,980.6200476
Thank you for joining us.
564
00:47:42,535.6200476 --> 00:47:43,645.6200476
Thank you for having me.
565
00:47:43,645.6200476 --> 00:47:44,785.6200476
this was fabulous.
566
00:47:44,825.6200476 --> 00:47:45,335.6200476
Thanks.
567
00:47:45,655.6200476 --> 00:47:46,400.6200476
Thank you.
568
00:47:47,663.9533809 --> 00:47:58,156.7629048
uh, uh, uh, um, uh, all right, Ken, another great conversation with a fabulous guest.
569
00:47:58,156.7629048 --> 00:48:08,791.7629048
What are you gonna take away from today's talk? Yeah, I really liked what Sheena had to say around the framework around adoption.
570
00:48:09,481.7629048 --> 00:48:11,346.7629048
What's really, are struggling with it.
571
00:48:11,731.7629048 --> 00:48:14,641.7629048
it's a huge problem and we talked about it in our opening today too.
572
00:48:14,641.7629048 --> 00:48:16,336.7629048
There's this hype of, are these.
573
00:48:17,41.7629048 --> 00:48:19,171.7629048
Generative AI initiative is gonna succeed or not.
574
00:48:19,381.7629048 --> 00:48:22,351.7629048
And what I took away from it was she was like, let's break it down.
575
00:48:22,351.7629048 --> 00:48:25,621.7629048
Like what should people be doing right now? Prompting.
576
00:48:25,966.7629048 --> 00:48:29,266.7629048
Let's get them good at prompting first and then we can start scaling.
577
00:48:29,356.7629048 --> 00:48:35,886.7629048
And I thought, her model, which has accelerate, which means, scale it to the rest of your organization at the very end is the right thing to do.
578
00:48:35,916.7629048 --> 00:48:43,856.7629048
Versus, what we're seeing maybe some companies saying they bought ChatGPT for their whole company and now wondering why they don't have a 800% increase in pipeline.
579
00:48:44,106.7629048 --> 00:48:45,726.7629048
So that was really powerful to me.
580
00:48:45,726.7629048 --> 00:48:50,841.7629048
What about you? You, one of the things that's a great reminder that Sheena said was the don't give up.
581
00:48:51,231.7629048 --> 00:49:08,31.7629048
And I think one of the things with AI that can be really frustrating is you see people who maybe don't have a ton of experience on social or whatever, and it's like they're doing these amazing things and making you look super easy, but you go on and it's oh, it's like very squeaky wheel and you can't get something to work.
582
00:49:08,31.7629048 --> 00:49:17,71.7629048
I certainly had this experience when I started playing with an n8n and and some of these tools can be super intimidating even if they are, low code, no code tools.
583
00:49:17,251.7629048 --> 00:49:20,341.7629048
There is still this element of you have to figure it out.
584
00:49:20,461.7629048 --> 00:49:25,711.7629048
And so I think that tenacity is one thing that is a good REMI reminder for everybody.
585
00:49:26,211.7629048 --> 00:49:33,391.7629048
Nobody knows how to do anything when they first start, so you gotta just keep trying, experimenting and and so I loved that that takeaway.
586
00:49:33,956.7629048 --> 00:49:40,186.7629048
Yeah, it's a little bit of that like resilience as well as not trying to get so frustrated that it didn't work.
587
00:49:40,236.7629048 --> 00:49:44,146.7629048
No one sees Michael Jordan missing a hundred free throws.
588
00:49:44,146.7629048 --> 00:49:50,496.7629048
I only, saw him getting the slam dunks.
589
00:49:52,176.7629048 --> 00:49:52,821.7629048
I redo that one.
590
00:49:53,151.7629048 --> 00:49:56,391.7629048
Getting the slam dunks that that he, that we caught on tv.
591
00:49:56,391.7629048 --> 00:49:58,671.7629048
So I That's a really good message.
592
00:49:58,761.7629048 --> 00:50:00,831.7629048
I think appropriate for where we're at with AI right now.
593
00:50:01,326.7629048 --> 00:50:01,626.7629048
Yeah.
594
00:50:01,626.7629048 --> 00:50:09,156.7629048
And another stat I'm gonna give you that is sports related that I know you've been dying for is, Roger Federer only won 52% of his matches.
595
00:50:09,456.7629048 --> 00:50:10,326.7629048
One of the greatest 10.
596
00:50:10,476.7629048 --> 00:50:10,836.7629048
Yeah.
597
00:50:10,836.7629048 --> 00:50:14,251.7629048
One of the greatest tennis players of all time Yeah, that's a.
598
00:50:14,736.7629048 --> 00:50:16,116.7629048
maybe sets, I can't remember.
599
00:50:16,116.7629048 --> 00:50:17,196.7629048
I'm gonna have to look it up again.
600
00:50:17,196.7629048 --> 00:50:19,241.7629048
But it's it is that crazy of a stat where you're like.
601
00:50:19,551.7629048 --> 00:50:19,941.7629048
Yeah.
602
00:50:20,861.7629048 --> 00:50:21,881.7629048
That's really powerful.
603
00:50:21,961.7629048 --> 00:50:27,501.7629048
And the more you know, This has been a great episode.
604
00:50:27,551.7629048 --> 00:50:32,111.7629048
Thanks again to Sheena Miles for joining us, and thank you all for listening.
605
00:50:32,351.7629048 --> 00:50:33,161.7629048
If you liked what you heard.
606
00:50:33,731.7629048 --> 00:50:39,46.7629048
Give it a a follow, a subscribe leave a comment and share with somebody because that's how we get the word out there.
607
00:50:40,11.7629048 --> 00:50:41,931.7629048
Yeah, and let us know what you wanna hear.
608
00:50:42,201.7629048 --> 00:50:46,161.7629048
So let's keep crafting the future of AI and go to market together.
609
00:50:46,161.7629048 --> 00:50:47,391.7629048
Bye-bye.
610
00:50:48,166.7629048 --> 00:50:48,386.7629048
Bye.