[00:00:00] Phil: what's one thing leaders can do tomorrow to make AI adoption more inclusive [00:00:04] Pam: I think women might be uniquely suited to this approach and acknowledging that we're leading in a time of ambiguity. [00:00:10] So I guess my top advice would be, make the learning the metric, not the output, right? We're still in this point where we have to celebrate people who will experiment, who share prompts, who teach others what they've learned, AI is a team sport. you need to be bouncing ideas off of each other, when curiosity is rewarded, I think inclusion follows more naturally because the quieter people feel like they can maybe speak up and, and they can do something. AI adoption isn't about a race to mastery, it's about a culture of shared learnings. And the companies that get that right will move faster and bring more people with them. [00:00:46] ​ [00:01:13] In This Episode --- [00:01:13] Phil: What's up everyone? Today we have the pleasure of sitting down with Pam Boris, fractional, CMO, and the co-founder of Women Applying ai. In this up episode, we cover 10 ways to support women and build more inclusive ai, like how to audit data, fingerprints for AI bias, how to go from ai curious. [00:01:29] To AI confident how visible role models build permission structures, how leadership shapes the learning environment for ai, and how community storytelling shapes, how women build confidence. All that, and a bunch more stuff after a quick word from two of our awesome partners. [00:01:44] ​ [00:03:40] Phil: Pam, thank you so much for your time today. I'm really excited to chat with you. [00:03:43] Pam: I've been so excited for our conversation. I'm so to join you from Boston today. [00:03:48] Phil: Awesome. Uh, yeah. One of the books that I've read in preparation for our chat today, Pam, is Invisible Women by Caroline Accredo Perez. It was a fascinating read and completely eye-opening for me [00:04:00] as a white male in tech. Um, I would love to start by some of the insights I got from that book. Couple of things stuck with me. [00:04:07] 1. How To Audit Data Fingerprints For AI Bias In Marketing --- [00:04:07] Phil: One of the core ideas in invisible women is that what we often call gender neutral is really just male by default. The author in the book shows how almost every modern system from like city planning to smartphone design was built around a male body or male behavioral pattern. The scary part for me reading that was that, you know, like hardware bias is kind of visible. [00:04:28] You can see like a seatbelt that doesn't fit or a phone that's way too big. But this idea in marketing of ai, like algorithmic bias hides inside of outputs that look objective. And the danger with AI in marketing is that bias scales quietly repeating itself thousands of times before anyone notices. So maybe we can start there. [00:04:47] Like I would love to ask you, how do you spot these invisible biases before they snowball? How do you, like, are there checkpoints or habits that help you like figure out these skewed patterns early on? How do we [00:05:00] audit these systems? What are your thoughts there, Pam? [00:05:02] Pam: Yeah, so first kudo for Reading Invisible. I think that's a really great touch point, not only for our conversation today, but in general. You. You know, you raise a great point. You know the scary part of bias, it doesn't show up as necessarily a bad outcome. It shows up as a pattern and these patterns, because they're ingrained in us, they just mirror what we see and what we've already seen. [00:05:24] So we can't always kind of detect 'em. So the first thing is that awareness, right? You have to assume that there's some bias exists and not wonder. If it does, if we think about how AI was trained, AI was trained on every word throughout human history, and there's been a lot of male bias in that, right? So it's just in kind of inheriting what we have. [00:05:44] So, I mean, I think about it like this. You know, every data set has a fingerprint and you can't see it at first glance, but if you zoom in. You might see who's overrepresented, who's missing, who's underrepresented, or who's misrepresented. That's a piece of it too. [00:06:00] So I ask, you know, not what's in the data. [00:06:02] Um, you know, who's being treated as edge cases when you know, in the case of women, half the population. So it is a three part habit. It's a always on kind of habit where before you build, you've gotta audit and see where your data is coming from and deeply understand it. Like you said, it's a little bit of a black box. [00:06:19] It's not so visible the way that it is with, um, hardware and physical devices. And then while you build, you've gotta stress test it for different groups. Make sure you build that into your testing. And then after you launch, you've gotta monitor the results, just like you'd monitor a performance. So, you know, bias isn't a one-time check. [00:06:36] It's definitely continuous maintenance. [00:06:38] Phil: Yeah. Yeah. Such a good point. Um, I, I. Had a couple episodes earlier on, I think like a couple years ago now. Brittany Mueller came on the show and she's like an SEO scientist that went all in on AI and ml like 10 years before A lot of marketers woke up to ai and she talks a lot about like what's in the hotdog, what's in the ai, and talks about all [00:07:00] the, the data sets that are in there and the biases that are in there. [00:07:03] And she uses, um, Wikipedia as like one of the main sources for, uh, the clean data corpus data set and. Wikipedia is like incredibly biased for like white male in their like mid thirties that don't have any kids and they're like higher income. And so yeah, like the, these are things that are so easy to miss when you're just like a user and all you see is the UI in front of you. [00:07:27] You just ask a question, you get the output, you copy paste that into your Instagram post for your corporate company, and then there's not really like a balance check on some of that stuff. So it's, it's a good reminder of continuously. Auditing it. And I like your point about like, it's not, it's not a one time thing. [00:07:44] Like, um, these things can snowball and you don't just do it once. You have to be constantly aware of it. I feel like that's half the battle, being aware of it. Right. [00:07:52] Pam: Absolutely percent. [00:07:54] Phil: So, um, I wanted to also ask you like, [00:07:57] 2. Why Emotional Intelligence Improves AI Prompting Quality --- [00:07:57] Phil: one of the interesting elements of, you know, talking about this with you today, like women in ai, uh, a lot of the research that I did before this episode is like well-documented that women score way better than men when it comes to like emotional intelligence. [00:08:11] Things like empathy, self-awareness, social intuition. These are the exact skills that machines can't fake at least today. Um, you know, in this world that's like obsessed with automation and optimization, those human superpowers matter more than ever right now. Um, when you look at how AI is reshaping marketing, where do you think women's natural strengths give them the edge? [00:08:34] Like obviously empathy matters, it's easy to say empathy matters, but inside a marketing work, where does that actually show up? Like is it in. How we brief creative, how we prompt AI tools, how we interpret data. What are your thoughts there, Pam? [00:08:47] Pam: Yeah, it's, it's such a, a multifaceted area. You know, you're right. Women do score higher on emotional intelligence, and as machines get better doing what they do, the humans are gonna have to be, [00:09:00] get, get better at doing what we uniquely can do. And while machines can, uh, give you foe empathy or they can seem really empathetic, there's no genuine empathy behind that. [00:09:10] And again, something else that folks need to be really aware of. Um, but you know, the biggest place I see women's strength show up is how we prompt and interpret. You know, prompting is basically briefing the AI and women are natural context givers, right? We think about tone and we think about audience, we think about nuance, and that's what makes AI output more human, more usable, more aligned with the brand. [00:09:33] We have that kind of intuitive sense. When we interpret the data, we don't always just ask what performed best, right? It's the why. And not only the why, but the right, because it could be a subset of your audience that is skewing the whole result because that it's, you know, that's the audience for whom it's perform performed. [00:09:54] So that's where emotional intelligence meets analytics. And um, you know, a lot of your guests on this podcast have talked [00:10:00] about how. Critical curiosity is for success in the MarTech world. And curiosity, I think, is one of the most important, uh, skill sets, uh, traits that you can have in AI as well, right? [00:10:15] So, you know, if we think about women, AI gives us maybe more leverage for women, what women have always done, which is to understand people. And when you build empathy outward, your marketing doesn't just perform and really resonates. I, I really, truly believe that. [00:10:31] Phil: Yeah, such a good point. [00:10:32] 3. Why So Many Women Hesitate --- [00:10:32] Phil: E, even though women dominate. Eight marketing as a profession. Um, like I've worked with, uh, countless of amazing women in marketing and technical roles. Uh, studies still show that adopting AI tools for women is much slower than men. And a lot of the studies are saying that it's not like ability, it's not a thing about skill. [00:10:52] It's often comfort. Confidence or trust even. So I'm curious to get your take on this, like what do you think is [00:11:00] driving that hesitation for women in ai? Is it how the tools are built, how they're represented, or maybe the culture around like who's supposed to use them and how do we kind of turn that around? [00:11:11] Pam: You've hit on all of the high points. So in addition to being a fractional CMO and my role with women applying ai, I'm also an AI trainer, so I train B2B marketing teams on AI adoption. So I work with a lot of women in marketing teams, and I see everything you just talked about. So it's not a skill gap, it's a confidence gap. [00:11:31] Sometimes it's a culture gap. And you know, most AI tools were built by men for early adopters who love to tinker. And a lot of women just don't see themselves as tinkerers. They think tinkerers are. Again, I think this goes back to really early days sometimes of how girls are raised, right? So, you know, and we're doing a lot better than with this generation than we did with my generation. [00:11:53] But you know, Legos weren't for girls and tinker toys weren't for girls, right? Those are boys tools. So you get taught [00:12:00] that like tinkering in building and learning to fail. And even when your Lego thinkink falls down, like that's a boys thing, right? Women just may not see themselves reflected in the culture. [00:12:12] So they tend to hesitate, right? And you hesitate when something is moving as quickly as AI is, you can be left behind. So it's not because we can't use the tools, but maybe we don't feel as welcomed into. And part of the gap that I think we have in AI is we've been inherited the tech, the technology, gender gap. [00:12:31] When truth is you don't need to be technical to be great at ai. And that's one of the bar. Really try to break down in my training, and it's a big tent of what we're trying to do with women applying ai. So in my training, I've heard many women say to me, I feel like I'm cheating at my job when I use ai. [00:12:48] And I could just tell you, Phil, I have never had a guy say that to me, never. And this comes again from that deep seated good girl syndrome that many of us were raised. So we need to uncover and [00:13:00] name that mindset that's holding people back and address those. Things that people don't even realize, maybe are conscious because you can't learn a new skill if you don't have a growth mindset. [00:13:10] So you've gotta establish a growth mindset first. Um, you know, and I think turning it around is the key point, right? So what can we do? And I think we have to model the great behavior. We have to point to other women in AI who are doing great things. We need to point to the teams around you if you're in a marketing organization, and learn from people who are doing a great job. [00:13:31] Because if you sit on the sidelines. Wait for three months to dive into ai. That's basically three years lost, and the pace of change that we're seeing in ai, things are really moving fast, so I encourage people to jump into the pool. If you don't feel comfortable jumping in the deep end of the pool, at least wait into the shallow end of the pool because there is no way to substitute for getting your hands on the tools, working on real use cases and pushing yourself forward. [00:13:58] Phil: It is so interesting [00:14:00] that, um, you've never heard of men say, uh, that they, they feel that they're cheating when they're using ai, but it's something that comes up a lot for women. I, I'm curious to like. What do you say to those women when, when you hear that is like AI is a tool, right? Like do you feel like you're cheating when you're using Zapier or when you're using email automation? [00:14:18] Pam: Yeah, exactly. It's the same thing I say to them. If you needed to do complex calculations and you'd use Excel, would you write that out on hand on a, a handwritten spreadsheet? No, of course not. Right? It's the same kind of parallel, and I think that that kind of, once people see, oh yeah, it's just a tool, then they can snap out of that kind, that kind of approach. [00:14:37] And again, that confidence and that that culture has to be built from the top because of the leadership team and the marketing group. Hasn't addressed ai, right? And it's kind of in the shadows. Um, and they're not clear. Guidelines have been given. You can see why somebody might feel that way, particularly if people are kind of using it on the side. [00:14:56] So I think that, you know, clarity that from the leadership [00:15:00] saying, these are tools we want you to use, these are skills we need you to embrace for the future of your career. That frees up that mindset, I think a lot for people too. [00:15:08] Phil: Yeah, the culture one is so interesting. It reminds me of when we were choosing a daycare provider for my daughter, and, uh, we had like a couple different, um, private daycare providers who went to visit. And one of them like invited us downstairs, like where she has her daycare, like all the toys and stuff. [00:15:25] And our daughter immediately went to like, play with the cars. Like she loves playing with cars. And the daycare provider was just like, oh, like, you know, you don't, you're not interested in cars. Like here are like the Barbies and here's like the kitchen, [00:15:38] Pam: Oh man. [00:15:39] Phil: stuff. And my wife was just like cringing. [00:15:41] Like, yeah. That was [00:15:43] Pam: oh yeah, we're not gonna be going to this daycare provider. A hundred percent. No, a hundred percent. Like so you get it right. That modeling, that that formation happened so young in girls. Um, so yeah, you just hit the nail on the. [00:15:57] Phil: Yeah, [00:15:57] 4. Why Collaborative AI Practice Builds Confidence In Marketing Ops Teams --- [00:15:57] Phil: the confidence factor is also really interesting. Um, I, I was digging into this, uh, invisible women calls this out also. Many women I know who I've worked with in Marketing Ops and Lifecycle, like they say, I'm not technical enough for ai, but these same women run incredibly complex campaigns and CRM systems, they understand data. [00:16:19] They can query databases. Like how do we shift that mindset from feeling like AI is a thread or this like. Thing that is, they're being tested on to seeing it as just another creative instrument, like something you can play with and master. What are your thoughts there? [00:16:36] Pam: Yeah. I, again, I think wading into the pool or jumping into the deep end of the pool is the answer. Right? And I also firmly believe that AI is a team sport. I think a lot of digital literacy and digital skills have kind of been communicated as an individual skill. So when you took your first Excel course, whenever that was, or whenever you sat through. [00:16:56] Marketo automation training, whatever you [00:17:00] sat through, a lot of times you're sitting through that as an individual and building individual skills. AI is unique in that you need to be bouncing ideas off of each other, ideally within your same team, um, or with other people who do a similar kind of role. Um, I do also, marketing ops folks are in perfect position to be leaders on the AI transformation because AI transformation isn't really even about tools. [00:17:26] It's about process. It's about identifying steps in workflows that can be automated in new and interesting ways, and being able to map that process out for people. Because sometimes with AI, it's not like, oh, this one tool is going to completely automate this set of tasks. It might be task to task, and it might be task to tool to task. [00:17:46] So marketing ops folks, in my experience just have that thinking of that mindset of breaking tasks down into smaller bits. Knowing when to bring it in. And there's no better confidence builder than seeing something actually [00:18:00] work. So in order to see something work, you have to be in there and you have to be, uh, playing with it and taking the initiative [00:18:08] Phil: I love it. Yeah. Shout out to the marketing ops folks for sure. We, we feel like, you know, this is our time. More people care about data quality now that we're slapping AI on top of everything, and [00:18:18] Pam: a hundred percent. [00:18:19] Phil: understand garbage and garbage out. Like, let's, let's all rally around all those like roadmap items about data quality that keeps getting pushed back. [00:18:27] Now we can finally [00:18:28] Pam: I, it's so true. Like those data hygiene projects that nobody ever has time for, they are so important now because AI can be a black box and if you're not able to see what it data, it's making decision that's gonna be existential for your company. So thank you for continuing to fight the good fight everybody on this, on this podcast. [00:18:49] 5. How to Go From AI Curious to AI Confident --- [00:18:49] Phil: So let's chat about like what AI confident really means for you. Like how do we support women in AI and help them go from being AI curious to AI [00:19:00] confident everyone says, and, and you mentioned this a few times also, a lot of folks say this, just jump in, get your hands dirty. Like find a use case. Just build something. [00:19:09] No one really explains what that looks like in a real job with real pressure in limited time, and you know, a real life outside of work. Also, what does getting your hands dirty actually mean to you? And how do we go from curiosity to real competence with AI without feeling overwhelmed or like fake busy? [00:19:28] Pam: Yeah, no, I mean this is really the whole reason why women applying AI exists, and it's the whole ethos that underpins our approach, right. So we built this nonprofit community that is free for members to join to be a safe space where women can learn AI in a hands-on way from each other. So we welcome them at all levels of their AI journey, and the volunteers and members are really driving the momentum. [00:19:55] It's a give get. So it might be that somebody in the community has [00:20:00] mastered this small thing and they feel like it's small, right? Because for them it's not, you know, it's, it automates one particular task, but you share that with a hundred other people and you've multiplied the impact times a hundred. So that give, get, and that builds the confidence, right? [00:20:15] I mean, I find this myself. I do a lot of the training and sometimes when I go in to do a training. I'm like, oh, this stuff's pretty basic. This team is probably ahead of it. And then I get all this awesome feedback at the end, people saying, wow, you made me think about this completely differently. Or, you know, this is giving me a confidence to jump in. [00:20:32] So I think that modeling and seeing the behavior, and we understand like all women, you know, our members are definitely juggling a lot of responsibility. So we really wanted to respect their time and their other commitments. The programming we offer really is that differentiator. So we have the basic bills, skills, building sessions that are hands-on, not lectures. [00:20:51] And then we inspire women by hosting, you know, folks who are really doing stuff in leading AI transformation of organizations of [00:21:00] all sizes. Um, we have a series where we interview female founders of AI native startups. One of the things people are sometimes surprised to hear is how a non-technical founder can run an AI startup. [00:21:13] And I think a lot of why we're seeing more women in this space is they're identifying problems out in the world that ma male founders just might never have recognized as a big problem. Right? So I think we're spawning a whole generation of female AI first, you know, kind of excited about that. Um, so, you know, a big part of women applying ai, AI is an always on online community where like-minded women can connect with each other and find their people to maybe work on a project together in their own time. [00:21:44] Um, and we talked about this already, but like this whole concept of digital skills being individual skills, ai, I so believe that AI is a team sport and it really requires that collaboration. All of this is about future proofing your careers. Um, you know. [00:22:00] I don't know if this is controversial or not, but I would say job security is dead, but career security is alive and kicking. [00:22:08] Phil: So cool. I love that job. Security is dead. Career security is what you should care about. Yeah. Like being on the solopreneur journey myself, I can totally empathize with that. I've always had this idea of. Building a network for like, what if I lose my job? Because, you know, even if you have a nine to five that you really like, and it feels like stable income, it's just not stable. [00:22:30] It's never stable. You've seen all the layoffs happen over the last like half decade. Like you never have a stable income when you have an in-house role for working for someone else. And so, yeah, we, we can chat at length about that, but there's so many good points that you said in there. [00:22:47] ​ [00:24:50] 6. Joining The 'Women Applying AI' Community --- [00:24:50] Phil: Pam for the, for the women listening that, you know, are really curious to join the community. [00:24:55] We'll obviously link out to it, but like, talk about like the onboarding process or an [00:25:00] application process. Like how, how do women join, uh, women apply in ai. [00:25:05] Pam: We do have an application. It's a really lightweight application. It's mostly designed so that we can understand where people are and develop and bring programming that's appropriate to them, their industries, their roles, their level of AI mastery. So, uh, it's on our web. Site, uh, it's a pretty easy form, and then people get invited into our Slack community. [00:25:24] That's kind of the hub of where everything happens. We see new people. We had, uh, 44 people come in, new people last week alone. So this, this, this community is scaling nicely. And then in there they're able to see all of the other people. They're able to follow different interest areas depending on their industry. [00:25:43] They are, uh, given all of the programming that we offer and the opportunity to sign up for that. And many of it's available after the fact on demand because we know people have demanding work and home lives and may need to watch it at 11 o'clock at night after the kids are bed. And then we understand that too.[00:26:00] [00:26:00] Um, so that's really the kind of the main way it works. There's also a ton, this is a volunteer run organization, so we have a lot of volunteer opportunities. We're actually building AI into our process as much as possible for a couple of reasons. One, we wanna drink our own champagne and make sure we're using AI as much as possible to automate our processes. [00:26:20] We are a volunteer run organization on a pretty small, you know, budget. So we need to have those efficiencies in everything from member onboarding to all of our internal processes. Then we're building that together so that women have an opportunity to get their hands dirty on real projects that are helping move the community forward. [00:26:40] So three different ways that we're doing that, but we also have plenty of people who are just kind of lurk and check in and, and maybe you're here for the networking. We try to have live networking as much as possible today. That's been mostly in the Boston geography 'cause we have such a. Critical mass of folks here in Boston. [00:26:56] But our dream and our, our vision is that people, members will [00:27:00] pick up the, uh, pick up the charge in their local geography and take our kit that we gave them, give them and put an event on in their own, in their own local market. Because it's that combination of virtual and in person, I think that really drives the connection forward. [00:27:17] Phil: I love it. We, we'll, we'll link out to the site, uh, women listening can, can go and, and check it out whether they wanna lurk or whether they wanna like, check out and how to open up a local chapter. Um, there's so many cool things and important things that the, the, the community is, is doing and, and you're being mindful about. [00:27:35] And [00:27:35] 7. Other Ways to Support Women in AI --- [00:27:35] Phil: I want to give you a chance to just like double down in, in some of those, so like other ways to support women in ai. Um, you know, specifically like really passionate about this idea of like ways we can build more inclusive ai, I think that like, you know, surfacing up. Leadership, women, um, like roles and visibility, uh, mentorship, like just a bunch of different things we chatted about today. [00:28:00] So I wanna give you a chance to dive into three of them specifically. So one of them is just like supporting and showcasing the work of women in the field. The second one is championing inclusive education, and also this idea of holding teams accountable for process internally. And then the third one is just making space to elevate underrepresented voices and perspectives. [00:28:21] So let, let's chat about the first one. We can do each, uh, individually. [00:28:24] 7.1 Role Models and Visibility --- [00:28:24] Phil: So role models and visibility. Support and showcase the work of women in the field. You talked about, um, we interview cool AI native founders. Um, representation kind of changes everything with this, right? Like when women see other women confidently using and leading and building with ai, it normalizes it. [00:28:43] Right now you look at who's building like AI startups and it's all men, like, it's all white men. Um, who are some examples of women using AI in inspiring grounded ways? Um, maybe some folks are like, uh, incognito and you can't name names. Um, I'm sure some [00:29:00] folks are, are public and you've interviewed already, but like, how can organizations make those examples more visible to their teams? [00:29:06] Pam: Yeah, exactly. So a couple of names come to mind of people who are doing this work in a very public way in the marketing area that, um, I recommend that people follow and, and, and check out. One is Liza Adams. So Liza is very well known in the marketing space. Um, she has done so much interesting work in terms of upgrading teams, building teams of agents that can augment the work of marketers, and she shares very generously and very publicly on LinkedIn and environments. [00:29:38] She does webinars and all that kind of stuff. Liza Adams is one. Um, I'll give you her information so you could put it in the show. And then Nicole Leffer is also somebody who's giving back so much to the marketing community, which she has mastered ChatGPT and prompting. For, um, uh, for marketers in ways. I just, [00:30:00] I've just never seen anybody kind of just manipulate the platforms quite the way she does. [00:30:05] Soon as there's a new model drop, she's out there. Um, so those are two people that I can name, but I talk to a lot of women who have been given the opportunity to be lead AI transformation in their company. And maybe they weren't given it, but maybe they took it. You know, they've seen this kind of stuff happen. [00:30:23] There are some that I've talked to are in very traditional organizations, large organizations, male dominated organizations, and they have been able to bring AI transformation because they're speaking the language of business. They make it safe. And again, this maybe goes back to the EQ stuff we were talking about. [00:30:40] They recognize that these older white men who have been extremely successful in this organization, maybe been there 30, 40 years. Are not necessarily going to recognize that what got you here won't get you there. So she makes it very safe. This particular woman I'm thinking of who works for a large Fortune 500, she makes it very safe for them.[00:31:00] [00:31:00] She'll say things like, well, some people may not understand that blah, blah, blah, blah, blah. She doesn't say You don't understand, but she makes it safe for them. To, you know, learn from her in that way. And just the way that she positions it and just the way that she listens to them both in groups and recognizes those power structures, I think has helped her be extremely successful at leading AI transformation in that organization. [00:31:25] So, just one example there. Another woman, I'm thinking of he who's here in the Boston area, a more of a, you know, next gen company, an internet company. She was, uh, you know, a machine learning engineer. This same kind of opportunity came up where they wanted somebody to lead AI transformation. She'd never done it before, but she stuck her hand up and said, I'll do it now. [00:31:46] Created did. She's wearing both hats at the same time, essentially doing two jobs, which is often what happens with women. But she's learning so much and bringing such value, and I've seen her also speak in a variety of different forums. So she's building her own [00:32:00] personal brand. Even as she brings that transformation forward. [00:32:03] So I would say in a time of. Volatility, uncertainty, ambiguity. If you can step in and say, I wanna be part of this change, even if you don't have the experience, but you have the curiosity and the interest and you're willing to do a little bit of extra work, I think that can pay incredible dividends. So those are the kind of role models that I look at. [00:32:24] The people who are building very publicly, and the people who are kind of quietly doing the work in their organizations. I think we can learn from both of them. [00:32:33] Phil: Very cool. Um, yeah, I'm gonna write those down for, uh, folks in the show notes and, uh, I'll have to ask. Or we record here, uh, ask you for some intros. Uh, my favorite way of getting people on the show is like a recommendation from someone who already had an awesome conversation with me, is how we got connected. [00:32:51] Uh, shout out to Angela, uh, episode 1 24. Um, so yeah, I, I, yeah, excited to, to learn more about some of these [00:33:00] women. Um, it's awesome that, you know, they just came so quick to you, like there's so many of them that you can point out. So, yeah, shout out to, to some cool people doing some, some really interesting things out there. [00:33:12] Um, [00:33:12] 7.2 Leadership’s Role in Inclusion --- [00:33:12] Phil: so the second one was this idea of leadership's role in inclusion. We kinda teased this out a bit earlier also. Um, but like this idea of championing inclusive education and holding teams accountable for progress. Culture starts at the top. It starts earlier on in our like upbringing also. But when you're in a company, if leadership treats AI like a competition or like a status badge, it right away alienates a lot of people who maybe just wanna learn or are just AI curious. [00:33:43] What's one thing leaders can do tomorrow to make AI adoption more inclusive in their companies? And not just in words, but like how they structure teams around, like rewards or education. What are your thoughts there? [00:33:55] Pam: Yeah, so, so important. I mean, the role of leaders is really critical in this AI transformation, [00:34:00] and it can be tricky. I mean, I talk to a lot of CMOs, a lot of CMOs particularly, and probably leaders. And other functions as well. They don't personally understand AI as well as they should or that they want to. [00:34:10] It's just a matter of time, right? When you're doing one of those busy C-level jobs, you don't have a lot of time to kind of like tinker with tools even as much as people want to. So sometimes they feel really funny about being the champion of ai and it's sometimes where I get brought in to do that process. [00:34:26] If you want inclusive adoption, you have to make it safe for people to ask so-called stupid questions. You know, you always hear there's no such thing as stupid questions. There's really no such thing as stupid questions in ai. 'cause there's so much to know. It's changing so fast. And you know, most people don't fear AI necessarily, but they might fear that judgment of like making a mistake. [00:34:45] So being able to try things out in public and be rewarded for that, whether that's by. Being able to demo or being, you know, given some kind of other kind of recognition. And when leaders, you know, model that learning out loud, they, they [00:35:00] normalize curiosity over perfection, right? So this is a journey for absolutely everybody. [00:35:05] I always encourage leaders to be transparent and vulnerable about their own owning learning. Again, sometimes that could be really hard, and it depends on the culture of the organization. It's easier said than done. I think women might be uniquely suited to this approach and acknowledging that we're leading in a time of ambiguity. [00:35:20] So I guess my top advice would be, would be to make the learning the metric, not the output, right? We're still in this point where we have to celebrate people who will experiment, who share prompts, who teach others what they've learned, encourage and reward this collaboration. I keep coming back to AI as a team sport, something I say all the time. [00:35:43] And when curiosity is rewarded, I think inclusion follows more naturally because the quieter people or the people on the sidelines feel like they can maybe speak up and, and they can do something. So again, AI adoption isn't about a race to mastery, it's about a culture of shared [00:36:00] learnings. And the companies that get that right will move faster and bring more people with them. [00:36:05] Phil: I love your point about AI as a team sport. It, uh, flows naturally into the third one I had here around mentorship for. The AI era, right? Like [00:36:14] 7.3 Mentorship for the AI Era --- [00:36:14] Phil: we talk about mentoring women into leadership roles, but we need mentorship for adoption as well. Sometimes the most effective push comes from peer to peer and not necessarily the C level. [00:36:28] Thinking about this, you know, like what? What would an AI mentorship model look like? Maybe you've thought about this already in the community, women apply in ai, but for women specifically in marketing, like who teaches who, like what does success kinda look like for you there? [00:36:43] Pam: Yeah, absolutely. I think the mentoring is super important and what I've seen is that for a lot of AI, early adopters. They might feel alone in their organization. So they need to find connection and mentorship in other spaces. And again, women applying AI community. But there are other communities where they might find that, [00:37:00] um, you know, our model at women applying AI is pretty simple, which is learn something and share it forward. [00:37:04] So when one woman gets comfortable, she becomes a spark for five more people. And that gets, that really kind of builds a flywheel effect, and that's what inclusive adoption looks like. A ripple effect of confidence. It doesn't have to be textbook mentoring. I think when we think about mentoring, that's traditionally been defined as a kind of like one-to-one long-term relationship and in a lot of organizations because of that, it was only offered to high potentials or people in a particular program, and I think we need to democratize. [00:37:36] That concept of mentorship, and it can happen in micro moments. You know, a five minute slack tip, a quick prompt share, little bursts of learning that are baked into the flow of real work. That's how AI competence builds, and it's drip by drip. So mentorship for the AI era is not about expertise. It's about generosity. [00:37:54] It's about sharing. Whoever is one step ahead reaches back and says, you know, here, let me show you how it's [00:38:00] done. [00:38:01] Phil: Very cool. Love that idea. Democratizing mentorship, because it does seem like such a formal, structured thing and. Like who asks who, how long does it last? Like is there a paid relationship there or, yeah, like it, it, it does seem like so formal sometimes, and like you said, sometimes it's like a five minute thing. [00:38:20] Like why isn't mentorship more micro based there, um, there's so many cool things going on with, with the community there. Um, one thing that I was curious to ask you about that kind of ties into, at the top of the conversation, [00:38:33] 8. Why Story Driven Communities Strengthen AI Adoption for Women --- [00:38:33] Phil: we talked about invisible women. A book and the author of Perez kind of shows how visibility spreads through shared stories, and I think it's such a powerful theme of the community you have going on there. [00:38:46] I wanted to ask you like, how have you seen communities or networks make a difference for women's specifically when it comes to learning or leading with ai? What are your thoughts there? [00:38:57] Pam: I mean, sharing stories are so important, [00:39:00] and one of the things we've been doing at Women Applying AI is capturing a story series that we call My Why with ai. So in our launch event, we had four of our founding members stand up and share their stories. We're now, you know, producing articles and videos because when people start to see the humanness of somebody's story and their starting point, it can really be very, very inspirational. [00:39:22] I found it very inspirational myself. Just one example of one of our members, um, mental health clinician, absolutely loved her job. You know, diagnosed with A DHD and felt buried by the note taking and the paperwork requirement that is part of that job. She had tremendous amount of shame that she was always behind on her note taking. [00:39:43] And she even said, maybe I don't belong in this field. Right? So she started, um, to experiment with purpose built AI for medical note taking, and, you know, obviously very secure and, uh, you know, PHI co, you know, compliant and all that kind of [00:40:00] stuff, but. She really wanted to be, you know, freed up from that burden. [00:40:05] And, you know, there's compliance aspect of taking notes, but also she wanted to be thinking and listening with her patients more than just thinking to make notes. So it's actually made her a better therapist. She has new joy in her role. And I mean, if that's not using AI for human-centric purposes, you know, I don't know what is. [00:40:26] There was another woman her shared her story at her launch event, and she, as ever since she was a little girl, she really, really, really wanted to be a physician. She wanted to be a doctor. She grew up in a family of doctors, got pretty far into almost going into medical school, when all of a sudden she realized she could not stand the sight of blood, like she couldn't be around blood. [00:40:46] So this, I mean, at first this was kind of devastating, right? Because this was gonna be kind of her identity and she realized that she could. Lean into data science, AI, and neuroscience and make a contribution to the medical profession that [00:41:00] way, even without, you know, being in the operating room or something, maybe like she originally visioned. [00:41:05] So I think AI is giving people a new lease on life of maybe the career path they've been on, doing it in new ways or maybe taking a complete pivot in their career to do something, you know, quite different. [00:41:18] Phil: Two awesome. Really cool stories. It's funny you mentioned, uh, A DHD 'cause I don't know if you saw the episode that dropped today. We're recording this, uh, at the end of October, episode 1 92 is with Angela Vega and she works at Expedia and she's like super courageous and open about her like later on in life diagnosis with A DHD. [00:41:38] And she talks about the chaos of marketing operations and how she, she was able to. Hone that and then create strengths out of it. And she like shares like a big A DHD tech that at Tech Stack with a bunch of tools that she uses now to like, take her what she thought were like quirks before into like strengths and her job and parenting at home. [00:41:57] So super cool kind of relevant [00:42:00] episode there and, and using AI for, you know, shared stories and, and helping other people, you know, learn from that as well. Right, [00:42:09] Pam: I mean, that's another AI for human-centric purposes, right? I mean that the inclusion and neurodiversity. I, I personally think that, you know, neuro Durris folks bring super amazing gifts to the organization. If they're leveraged in the right way. So I'm hoping that with AI we can maybe identify and unleash a lot of that value, both for individuals and their organizations. [00:42:32] Phil: I got two last questions for you, Pam. Um, [00:42:35] 9. AI’s Role in Women’s Worklife Harmony --- [00:42:35] Phil: one thing I wanted to ask you about is designing for balance. So if AI can automate a lot of the grunt work. Maybe it can finally help us also with work-life balance, but only if we design for that intention. So my question to you is, how can marketing teams use AI to create more humane work rhythms, especially for women who balance so much [00:43:00] outside of work in the office at at home? [00:43:03] What are your thoughts there? [00:43:05] Pam: Yeah, this is the million dollar question. Okay. Um, you know, and sometimes when I see AI hesitancy, part of it comes from maybe the fear or the belief that if the person automates work to become more efficient, they're just gonna get more work piled on top of them to make up for the time savings. Right. [00:43:23] We have been automating, you know, dirty, dull and dangerous work in the physical world for decades. Right. And in marketing and marketing ops, we don't have a lot of dangerous and we don't have a lot of dirty. But we have a lot of dull, right? So if we can do that and, you know, save time for people, I'm, I'm kind of all for it, right? [00:43:44] The field that all of us are in, in marketing, the to-do list never seems to get smaller. There's so much that you can do that is free and cheap and you know, it's the tyranny of the urgent. Sometimes let's just do another blog post. Let's do another email campaign. [00:44:00] Let's do another nurture sequence, right? [00:44:01] Those are some of the things. So, I mean, I would love to see this AI conversation, maybe reinvigorate discussions about the four day work week. I am not personally that optimistic. We're gonna see that right away. I think that we're gonna have a lot of disruption. You know, in the near term everybody talks about kind of this vision of the utopic vision of the future. [00:44:24] I hope we get there, but I think it's gonna be a little bit messy for now. But, you know, I think. If we can, again, get past the tyranny of the urgent and bring back the good creative game, changing ideas that we all would love to do, but we just never feel like we have time to do. I think that unleashes a lot of creativity and brings back a lot of satisfaction, hopefully reduces burnout for people. [00:44:47] I always say that we have to tie ourselves to real business metrics like pipeline creation, deal acceleration, revenue generation, customer retention. I mean, those are the metrics that your business really [00:45:00] cares about. I do think we should be part of the conversation around the value creation that manifests itself as rewards for employees, not just for shareholders. [00:45:11] Whether that means higher pay, maybe that's profit sharing or bonus structures. Maybe it's time off. Maybe it's even more flexibility than we maybe have in our organizations today with remote work and hybrid work. But I think the employees really need to be part of manifesting the new model that we wanna see here. [00:45:29] Phil: I love it. Great answer, Pam. Such an important. Conversation. Um, such a fun topic also, like you're clearly passionate about this and really well spoken. [00:45:39] 10. Why Personal History Strengthens Creative Leadership --- [00:45:39] Phil: I got one last question for you. You're a fractional CMO. You're also a marketing advisor. You help teams, uh, with their AI adoption. You're also a founding member of women in ai. [00:45:50] You're also a proud Bostonian and a yoga lover, avid reader and an ancestral detective. One question we ask everyone on the show is, how do you remain happy and successful in [00:46:00] your career, and how do you find that balance between all the things you're working on while staying happy? [00:46:05] Pam: Yeah, I love this question. It's so important, right? To stay happy and successful. I think there's a lot of conversations around success, but that happiness is really, really [00:46:14] important. Um, so as you said, you know, in addition to my professional roles, I love yoga and books and genealogy and. The irony is not lost on me, that my interests range from the cutting edge and futuristic of tech and ai, and then to things that are really deeply rooted in the past, like yoga and pop books, and you know, genealogy. [00:46:35] And I think if we bring this conversation kind of full circle, when I think about my interest in genealogy, you know, family names are inherited from our male ancestors, right? It's just another thing that we don't think about. It's just kind of historically has been done. Again, recent generations, that's changed a lot. [00:46:52] Women are keeping their maiden names in a lot of cases, but historically that has absolutely been the case. And as I dug into my own [00:47:00] genealogy, I found the women in my family tree are some of the most interesting characters. They'd show incredible strength and resilience. Um, some were immigrants who came to the US knowing they would never go back to their homeland. [00:47:12] You know, it was not as easy a hundred, 150 years ago to just bip on a flight and go back to Ireland or Italy or some other plate. You knew when you were leaving that homeland you were never coming back. And you know, some, I found on one side of my family played a really huge supporting critical role in the emergence of the US as an independent nation. [00:47:30] You know, with the, you know, the, the Revolutionary War and stuff like that. So I'm personally inspired by these women who came before me, and even if they're kind of, their names have been lost to history, we know that we can still learn from them and be inspired from them today. [00:47:44] Phil: I love it. Great answer. Yeah. It is something I don't dig into enough like my, my ancestral. Ask. Um, but yeah, it's funny you say a lot more women today keep their maiden name. My wife has kept her maiden name and I totally get it. Uh, our daughter has a nice, [00:48:00] composed last name now. But yeah, love the conversation today, Pam. [00:48:04] This is super fun. Um, hopefully, you know, the men that have stuck out for, for this long also. I've learned a lot. Um, I've learned a lot over the last couple years about how to be a better ally than just being someone that like, yeah, yeah, we support women. But like, I think, you know, a big part of it is awareness and, and understanding, and I appreciate the, the work you do for, for women, but also, um, helping men be better ally and this whole like, uh, gap that we still deal with with ai. [00:48:33] Pam: Yeah, a hundred percent. I mean, men are super important. In this, not only as allies, but really with this AI transformation, taking up so much work from everybody, we're gonna need all hands on deck, right? So we can't afford to leave half of the workforce behind. And I talk to so many men who are supportive of this and recognize that really to drive an AI driven future, we need as many brains in the game as we can get. [00:48:57] Phil: A hundred percent. Awesome. We'll, we'll link out to the community [00:49:00] and, uh, some of the other stuff you have going on there, pan, thank you so much for your time. Really appreciate it. [00:49:04] Pam: Yeah. Thank you.