How to Scale GTM with AI SDRs, Digital Twins & a Growth Mindset

Episode Transcript
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(00:00):
.999Hey crafters.
Just a reminder, this podcast is for informational entertainment purposes only and should not be considered advice. 3 00:00:08,330.0000000002 --> 00:00:18,10.001 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. 4 00:00:18,610.001 --> 00:00:20,530.001 for tuning in to the FutureCraft podcast. 5 00:00:20,800.001 --> 00:00:21,770.001 Let's get it started. 6 00:00:22,770.001 --> 00:00:23,280.001 Hey there. 7 00:00:23,310.001 --> 00:00:32,170.001 Welcome to The FutureCraft Go to Market podcast, where we're exploring how AI is changing all things go to market, from awareness to loyalty, and everything in between. 8 00:00:32,440.001 --> 00:00:35,830.001 I'm Ken Roden, one of your guides on this exciting new journey. 9 00:00:36,430.001 --> 00:00:42,970.001 And I am Erin Mills, your other co-host, and together we're here to unpack the future of AI and go to market. 10 00:00:43,330.001 --> 00:00:49,810.001 We're gonna share some best practices, how tos, interview industry leaders and pioneers who are paving the way in AI and GTM. 11 00:00:50,140.001 --> 00:01:04,775.001 So Ken, how are you paving the way? How's it going? Yeah, Erin, I feel like a lot of where you're hearing conversations about how AI is helping go to market is around content creation and I have this new use case that I've been playing around with, which is pretty cool. 12 00:01:04,775.001 --> 00:01:09,605.001 It's not actually creating content, but it's really teeing up to the strategic impact of your content. 13 00:01:09,995.001 --> 00:01:11,825.001 Simple little GPT that I've been using. 14 00:01:11,825.001 --> 00:01:12,875.001 I'll share it with you later. 15 00:01:13,680.001 --> 00:01:34,200.001 And I'll put it in the link of the show notes for our listeners, but what I've been doing is I've taken a company's website, particularly their resource center and blog, which includes their blog posts, and then I'm comparing it to a key competitor and having a deep research analysis done by ChatGPT to understand what are the content gaps of. 16 00:01:34,545.001 --> 00:01:44,685.001 The competitor on their website, what don't they have? And then I'm putting in some information about our ICP and what interests them in 2025 and understanding where we could build out our content strategy. 17 00:01:44,955.001 --> 00:01:53,655.001 So rather than having ChatGPT write some blog posts or create some white papers, I'm actually getting ideas for our content team to actually make things that will speak to our buyers. 18 00:01:53,925.001 --> 00:01:58,575.001 And one of the cool things was I shared it with a sales leader and they were like. 19 00:01:58,965.001 --> 00:02:00,615.001 This is exactly what we need. 20 00:02:00,615.001 --> 00:02:04,245.001 It's, it really hits on a key gap that we haven't addressed that I've been wanting to talk about. 21 00:02:04,575.001 --> 00:02:08,725.001 And just positive feedback on something little like that made me feel really good. 22 00:02:08,905.001 --> 00:02:13,495.001 But really when you think about the strategic impact is you're not creating content for content's sake. 23 00:02:13,555.001 --> 00:02:18,325.001 You're creating value add using AI while still leveraging humans for their expertise. 24 00:02:18,895.001 --> 00:02:20,755.001 I think that's such a cool idea. 25 00:02:21,55.001 --> 00:02:25,165.001 And building on what we did last episode, building that competitive battle card. 26 00:02:25,285.001 --> 00:02:29,425.001 You could even load that in as part of the how to create the content. 27 00:02:29,830.001 --> 00:02:30,850.001 That's a really good idea. 28 00:02:30,850.001 --> 00:02:31,930.001 I'm gonna do that next. 29 00:02:32,210.001 --> 00:02:39,210.001 What about you? What have you been up to with ai? As usual, it feels like everything in my life is AI related. 30 00:02:39,310.001 --> 00:02:41,490.001 I've been doing a lot of home improvement lately, Yeah. 31 00:02:41,500.001 --> 00:02:47,240.001 one of the things was I couldn't really decide what color to paint this mantle in my house. 32 00:02:47,600.001 --> 00:02:53,150.001 And, normally you get these little paint samples and you test it and you could just see this little swatch does that work. 33 00:02:53,540.001 --> 00:02:57,620.001 And with ai I was able to take a picture of it and said, match my door. 34 00:02:57,990.001 --> 00:03:02,25.001 And paint it that color It looked so realistic. 35 00:03:02,55.001 --> 00:03:04,260.001 I could not tell the difference, Wow. 36 00:03:04,365.001 --> 00:03:14,355.001 had it try some other colors because let's see the difference And similar outcome where it really gave me a visual of what it actually could look like if I painted it. 37 00:03:14,565.001 --> 00:03:19,395.001 So I feel like I made a really good color choice without having to do all the extra effort. 38 00:03:19,695.001 --> 00:03:23,655.001 And it gave me feedback what colors would work and which ones weren't wouldn't, which is also cool. 39 00:03:24,360.001 --> 00:03:25,500.001 Yeah, I really like that. 40 00:03:25,600.001 --> 00:03:33,40.001 You've got me thinking that I could probably use something like that to think about how to brand things better, to align to a certain. 41 00:03:33,620.001 --> 00:03:37,580.001 Company that I'm working with, or maybe a certain initiative that we're working on. 42 00:03:37,720.001 --> 00:03:44,310.001 What is, how does this color scheme work? What, how do I match it up for our existing branding and where to go from there? That's really cool. 43 00:03:44,770.001 --> 00:03:48,730.001 You gave me the inspiration last season when you started doing your outfits. 44 00:03:49,730.001 --> 00:03:50,870.001 I still use it. 45 00:03:50,990.001 --> 00:03:54,280.001 My partner thinks it's really weird that I get advice, but I don't know. 46 00:03:54,970.001 --> 00:03:56,140.001 I know my colors now. 47 00:03:56,650.001 --> 00:03:57,310.001 I love it. 48 00:03:57,640.001 --> 00:04:00,700.001 Alright, Ken, so we've got our colors lined up, we're ready to go. 49 00:04:01,360.001 --> 00:04:05,755.001 do we have on the podcast today? We have Rachel Tru. 50 00:04:06,115.001 --> 00:04:09,595.001 She's the CMO of a company called simPRO and. 51 00:04:09,925.001 --> 00:04:22,495.001 I think you are going to love talking to her because one of the things that really amazed me when I met her a couple months ago is how she is leading her team through this AI go-to market transformation. 52 00:04:22,765.001 --> 00:04:32,915.001 And she's got them thinking not just about how to improve efficiency, but actually how to evolve their strategy from a go-to market perspective and accelerate as a team. 53 00:04:33,95.001 --> 00:04:34,625.001 And so I think we're gonna learn a lot from her. 54 00:04:34,625.001 --> 00:04:35,585.001 I think you're gonna love her. 55 00:04:35,585.001 --> 00:04:36,815.001 And I'm really excited for her. 56 00:04:36,815.001 --> 00:04:37,355.001 If you can't tell. 57 00:04:38,60.001 --> 00:04:38,540.001 Awesome. 58 00:04:38,540.001 --> 00:04:39,410.001 Let's get into it. 59 00:04:40,750.001 --> 00:04:45,370.001 we're thrilled to have Rachel Tru Air, CMO at simPRO Group. 60 00:04:45,640.001 --> 00:04:53,710.001 Rachel has a diverse background in B2B leadership with experience in both Fortune 100 companies and startups, including cart.com, 61 00:04:53,800.001 --> 00:04:58,240.001 Adobe Magento, confluent, and Oracle, quite the resume. 62 00:04:58,540.001 --> 00:05:07,550.001 Her success in technology marketing and leading teams through global expansion proves invaluable as simPRO Group continues to empower tradespeople worldwide. 63 00:05:08,420.001 --> 00:05:17,180.001 Rachel's expertise lies in utilizing AI powered marketing automation, customer insights, and personalized strategies to drive engagement and business. 64 00:05:17,180.001 --> 00:05:19,70.001 Super excited to talk to you, Rachel. 65 00:05:19,160.001 --> 00:05:20,210.001 Thanks for joining us. 66 00:05:20,510.001 --> 00:05:21,50.001 Likewise. 67 00:05:21,50.001 --> 00:05:22,160.001 Thank you so much for having me. 68 00:05:22,895.001 --> 00:05:23,225.001 !Great. 69 00:05:23,225.001 --> 00:05:24,65.001 Let's dive in. 70 00:05:24,115.001 --> 00:05:30,685.001 Rachel, there's still a myth out there that AI isn't good enough or it's really only for marketers creating content. 71 00:05:30,955.001 --> 00:05:47,180.001 How do you think about AI supporting go to market teams? Is it really ready for prime time? I think the real question is are go-to market teams ready for ai? I've seen myself just the rapid advancement of AI for both marketing and sales. 72 00:05:47,610.001 --> 00:05:50,370.001 Pretty much all, all across the CXO stack. 73 00:05:50,370.001 --> 00:05:51,120.001 At this point. 74 00:05:51,390.001 --> 00:05:57,540.001 If you're not thinking about how you're leveraging ai, then you're probably falling behind and on the go to market side. 75 00:05:57,805.001 --> 00:05:59,35.001 It's absolutely ready. 76 00:05:59,95.001 --> 00:06:06,855.001 There's a lot of different ways to use it, whether you are just getting started or you are starting to scale and grow your AI efforts. 77 00:06:07,275.001 --> 00:06:08,755.001 But go to market. 78 00:06:08,755.001 --> 00:06:11,485.001 Teams really need to start thinking about AI before it's too late. 79 00:06:13,845.001 --> 00:06:15,75.001 That's something we talk about often. 80 00:06:15,75.001 --> 00:06:25,725.001 Like we're still in this window where people can still jump on and not fall behind, but if they wait too much longer, they're gonna run the risk of losing out to their competitors and potentially losing business. 81 00:06:26,25.001 --> 00:06:31,455.001 One of the things that I have really been interested in your background is working with leaner marketing teams. 82 00:06:31,785.001 --> 00:06:44,585.001 Where should smaller resource constrained teams focus on for their AI strategy to get some of those quick wins going? Yeah, that's a great question and thanks for calling out some of the background I have with smaller, leaner teams. 83 00:06:44,865.001 --> 00:06:56,175.001 At the end of the day, a lot of the kind of foundational elements re remain the same whether you are in a huge organization or in a really small, couple of people in marketing type of setting. 84 00:06:56,455.001 --> 00:07:01,860.001 At simPRO what I put together was essentially a maturity model for how we're using AI and marketing. 85 00:07:02,250.001 --> 00:07:03,960.001 And I really look at it as three different stages. 86 00:07:03,960.001 --> 00:07:07,30.001 One is around foundational aI efforts. 87 00:07:07,30.001 --> 00:07:12,790.001 One is more around operational AI efforts, which is where I would say the simPRO group marketing organization is today. 88 00:07:13,150.001 --> 00:07:17,260.001 And I think the future vision is really around autonomous AI integration. 89 00:07:17,260.001 --> 00:07:21,400.001 And so with smaller teams, one of the first things to think about is. 90 00:07:21,715.001 --> 00:07:25,435.001 Looking for a way to get started on that foundational aspect. 91 00:07:25,465.001 --> 00:07:29,575.001 And I would say, again, this really goes across whether you're in marketing or finance. 92 00:07:29,575.001 --> 00:07:32,275.001 I was just talking today with my CFO. 93 00:07:32,275.001 --> 00:07:48,710.001 She had just gone to a Deloitte AI conference and learning a lot about AI for finance, and she was telling me that, the financial teams are really falling behind a little bit other functions because of regulatory challenges and some of the rules that they have to follow. 94 00:07:49,10.001 --> 00:07:56,90.001 But what my guidance to her was really around looking for kind of repetitive data rich and low empathy tasks that. 95 00:07:56,665.001 --> 00:07:58,675.001 She and her team can start to deploy. 96 00:07:58,735.001 --> 00:08:05,460.001 And that's really where we started in our marketing organization around ai, was just those kind of foundational efforts. 97 00:08:05,460.001 --> 00:08:13,520.001 Not everything's going to work, not everything's going to scale, but once you can start to build that muscle and a key area there is not boiling the ocean. 98 00:08:13,980.001 --> 00:08:15,180.001 It can be daunting. 99 00:08:15,420.001 --> 00:08:23,500.001 And so if you can not boil the ocean and really focus on what can we do today? What's some quick wins with, high degree of feasibility, that's a great place to start. 100 00:08:23,500.001 --> 00:08:33,490.001 So I would say, look for the low hanging fruit and look for things that can be repetitive and free you up for more time and energy to actually start to focus on those bigger heavier lift type of AI efforts. 101 00:08:34,250.001 --> 00:08:37,730.001 Yeah, I really like what you just said, low empathy tasks. 102 00:08:37,730.001 --> 00:08:49,470.001 I think I hadn't heard it structured like that, but when we hear marketers sometimes talk about, is AI gonna replace my job? These are tasks that we don't really require much human effort and are tasks that people don't really want to do sometimes. 103 00:08:49,470.001 --> 00:08:50,490.001 I like the framing of that. 104 00:08:50,490.001 --> 00:08:51,180.001 That was really cool. 105 00:08:51,570.001 --> 00:08:51,900.001 Yeah. 106 00:08:51,900.001 --> 00:08:55,730.001 One of the things that I'm have been intrigued by, especially in this AI space. 107 00:08:56,105.001 --> 00:09:07,235.001 Is when you talk about, job killers and ai, often those are more, I would say, entry level or earlier career type of jobs that, that people see as being, at risk or threatened. 108 00:09:07,235.001 --> 00:09:12,405.001 And you think about the BDR function which is one of the first places where we started innovating with ai. 109 00:09:12,825.001 --> 00:09:18,65.001 And because it's such an early career function, a lot of the people that work in that function tend to be. 110 00:09:18,755.001 --> 00:09:19,715.001 Digital natives. 111 00:09:19,715.001 --> 00:09:24,620.001 They're people that have grown up with technology from the day they were born and. 112 00:09:25,580.001 --> 00:09:33,190.001 What I found is that the resistance doesn't necessarily come from within the ranks of these kind of digital native early career employees. 113 00:09:33,520.001 --> 00:09:42,890.001 They're actually really accustomed to leaning on and leveraging technology to do things that they don't either want to do or they think are below their level of work quality. 114 00:09:43,220.001 --> 00:09:49,970.001 And so I view it as really a force extender when you're talking with the BDR team about, Hey, we're gonna start leveraging ai. 115 00:09:50,390.001 --> 00:09:53,115.001 This is why it matters oftentimes, the team's yeah. 116 00:09:53,305.001 --> 00:09:54,85.001 Welcome to the party. 117 00:09:54,205.001 --> 00:10:03,400.001 We've all been using it for a while now, so I've found that there's not as much resistance as maybe the culture or the kind of the overall like conversation around ai. 118 00:10:04,50.001 --> 00:10:12,360.001 Creates because a lot of the people that are starting to see AI affect their roles are really from a digital kind of native generation. 119 00:10:12,420.001 --> 00:10:16,190.001 And they're embracing it faster oftentimes than some of the people running the company. 120 00:10:16,190.001 --> 00:10:29,320.001 I view it as really a forced extender as something that can help, handle all the repetitive stuff so that our reps can actually do what they love, which is really around building relationships, high value conversations being able to achieve, their goals and move up in their careers. 121 00:10:31,270.001 --> 00:10:31,420.001 Okay. 122 00:10:31,420.001 --> 00:10:33,190.001 You brought it up, so now I have to dive in. 123 00:10:34,210.001 --> 00:10:40,210.001 I think every marketing leader I talk to is curious about AI and the BDR/SDR function. 124 00:10:40,450.001 --> 00:10:43,180.001 So we gotta dive in and ask you some questions about it. 125 00:10:43,390.001 --> 00:10:49,240.001 It's one of the most talked about and misunderstood use cases, I think, in the marketing and go to market space. 126 00:10:49,430.001 --> 00:11:00,835.001 To start us off, when someone hears an AI SDR, what does that actually mean? In terms? Yeah, I think one of the things to understand about it's not a robot dialing on the phone. 127 00:11:00,925.001 --> 00:11:05,215.001 It's a trained kind of always on digital teammate. 128 00:11:05,725.001 --> 00:11:11,715.001 We actually have names for our ai teams and the first one we started with was Daniella. 129 00:11:11,925.001 --> 00:11:19,125.001 We've now onboarded about four others across the globe in different regions focused on different parts of our business and. 130 00:11:19,650.001 --> 00:11:26,580.001 They're someone who we are coaching and training just as much as we might coach and train a new employee, just in sometimes different ways. 131 00:11:26,970.001 --> 00:11:31,730.001 We're, building segments, helping understand, where should we focus on their efforts. 132 00:11:32,60.001 --> 00:11:37,440.001 We are giving them content to start with and learn and adapt based on what they see working. 133 00:11:37,710.001 --> 00:11:39,720.001 We give them an objective and a goal. 134 00:11:39,900.001 --> 00:11:44,480.001 Many of these things are not that different than what you might do when you ramp a human onto a team. 135 00:11:45,155.001 --> 00:11:52,15.001 The big difference obviously is that they can scale much more quickly and you can really start to see results almost instantaneously. 136 00:11:52,375.001 --> 00:11:58,175.001 And as we look at someone like Daniella in our organization, having the ability to say. 137 00:11:58,565.001 --> 00:12:13,235.001 This can handle those kind of repetitive conversations, but also do it in a way, even though we're talking about low empathy tasks, do it in a way that actually has a lot of context around it and make decisions based on what they're getting back from the prospect they're reaching out to. 138 00:12:13,525.001 --> 00:12:19,175.001 When they're doing that, they eventually do end up with a warm lead or a human that's, that they're engaging with. 139 00:12:19,505.001 --> 00:12:22,685.001 And from there, the very first thing they're going to do is pass it to. 140 00:12:23,285.001 --> 00:12:25,115.001 To one of our actual bdr. 141 00:12:25,145.001 --> 00:12:28,415.001 And so when that starts to happen, that's where the humans take over. 142 00:12:28,415.001 --> 00:12:33,25.001 That's where, you start to reach the point of, okay, now it's time to have a real conversation. 143 00:12:33,145.001 --> 00:12:36,915.001 Someone wants to get on the phone, see the product, understand, who are they working with. 144 00:12:36,915.001 --> 00:12:40,425.001 Especially in a business like ours, relationships and reputation are really important. 145 00:12:40,425.001 --> 00:12:48,495.001 And so some of those things are, I would say AI proof, right? They're going to be something that's around for a very long time, regardless of how many AI BDRs we scale. 146 00:12:50,710.001 --> 00:12:54,320.001 I always think that being a BDR is the hardest job in an organization. 147 00:12:54,560.001 --> 00:12:56,905.001 You're usually the most junior person having to have. 148 00:12:57,130.001 --> 00:12:58,780.001 Pretty sophisticated conversations. 149 00:12:58,780.001 --> 00:13:37,955.001 And so I can see something like where you've got Daniella, who's really trained up on all your content and the value props could benefit the customer lifecycle, but also giving the BDR lot of intelligence that they can use when they do have that human handoff where do you really think the line is right now, and how do you see it evolving between where somebody like A BDR, an entry level person is today interacting with Daniella or one of these new models that you're coming out with? And how does that change over the next year or two? Yeah, I think for me the, there's a lot of impact to how I think about workforce planning and organizational resource allocation. 150 00:13:38,540.001 --> 00:13:56,730.001 And while we still have BDRs and will need BDRs to take over from someone like Daniella to be able to engage with a prospect and take them through a demo or a discovery call, what I see in terms of the impact and what's really going to change over the next couple of years is on the marketing side least. 151 00:13:57,690.001 --> 00:14:12,610.001 I couldn't have predicted when we launched Daniella in Q4 of last year and went into planning this year that I would need, a certain number of headcount to really support the, not just the strategy, but really the technical side of. 152 00:14:13,30.001 --> 00:14:15,610.001 Launching something like several Daniel's. 153 00:14:15,610.001 --> 00:14:28,330.001 And so for every time we say we want to run a certain use case leveraging, an AI BDR agent, and there are many use cases, if you can imagine it, we probably have been asked about whether, we can spin it up. 154 00:14:28,810.001 --> 00:14:34,500.001 And there's the classic thing of it sounds easy because in general it, is once you get it running it's pretty autonomous. 155 00:14:34,920.001 --> 00:14:37,680.001 But to get it set up, there's segmentation has to be done. 156 00:14:37,680.001 --> 00:14:39,180.001 There's content has to be loaded. 157 00:14:39,720.001 --> 00:14:46,800.001 So I didn't expect or even plan this year that I might need, five or six marketing ops people to really support this program. 158 00:14:46,800.001 --> 00:14:48,840.001 And so for me, what's been interesting to see is. 159 00:14:49,620.001 --> 00:15:13,80.001 Where previously we might be thinking about how many more BDRs do we need to scale up or do we need to go and outsource some outbound lead gen to an agency? Instead, I'm thinking about, oh, do I need to set some budget aside for the second half to be able to support hiring some additional technical people that can scale up this AI motion? And that's just one motion within the organization. 160 00:15:13,130.001 --> 00:15:20,40.001 I think the shifts are definitely happening and work looks different than probably it did even six months ago. 161 00:15:20,400.001 --> 00:15:24,480.001 But at the same time, there's still quite a bit of human work left to be done. 162 00:15:24,870.001 --> 00:15:32,50.001 And I think it's not full, not to the point where it's so fully automated that, we can all just go grab a margarita on the beach and call it good. 163 00:15:32,330.001 --> 00:15:34,370.001 So that's where I see the changes happening. 164 00:15:35,655.001 --> 00:15:41,315.001 I think that's it's interesting 'cause we hear a lot from folks that we talk to, oh, I'm really worried about my job. 165 00:15:41,415.001 --> 00:15:47,535.001 And I think that's really interesting that it's maybe not the SDR r job, but maybe it's more marketing ops or somebody more technical. 166 00:15:47,775.001 --> 00:15:53,55.001 So do you feel like the SDRs are feeling more empowered with having. 167 00:15:53,435.001 --> 00:15:55,965.001 This, SDR AI bot. 168 00:15:55,965.001 --> 00:16:15,795.001 And then how are they working with the marketing ops team to make sure that they're really smooth handoff? Yeah, it's, the handoff is relatively smooth because of the way we've set it up, where Daniella or the AI agent actually will just copy the BDR right into the email, and that it's intelligent enough to know which BDR needs to be copied in, which is a cool part of how it's built. 169 00:16:16,255.001 --> 00:16:26,545.001 What I have noticed with the BDRs is they're getting more engaged on some of the higher touch, higher value tactics that maybe we didn't have time to get to previously. 170 00:16:26,545.001 --> 00:16:28,15.001 So writing. 171 00:16:28,525.001 --> 00:16:45,160.001 Custom copy for a big account that they might be prospecting into, or spending more time on kind of the pre-work before a call to that prospect that raised their hand on the inbound side through ai, we also are seeing a lot more efforts being done around direct mail. 172 00:16:45,680.001 --> 00:16:55,480.001 We've got a lot more, I would say, integration between our high touch outbound BDR motion and this high volume outbound b AI motion that we're running. 173 00:16:55,900.001 --> 00:17:02,505.001 And what I love about that is it goes back to the fact that, these are often kind of manual repeatable tasks and. 174 00:17:03,315.001 --> 00:17:17,475.001 No one really wants to have a job like that, and I think that it's evolving the role of A BDR rapidly, but there's still certainly quite a bit of work left to be done on the BDR side in terms of bringing prospects in and, moving them through the funnel. 175 00:17:18,475.001 --> 00:17:24,805.001 A couple of weeks ago I had the chance to meet some of your team and I would say that they're beyond AI curious. 176 00:17:24,805.001 --> 00:17:32,755.001 They are dove in headfirst and are excited to have AI be part of their work, which is really exciting for me to meet them and talk to 'em about it. 177 00:17:33,85.001 --> 00:17:40,645.001 And it got me thinking about, deploying AI isn't just about the technology, it's actually a mindset and culture shift for teams. 178 00:17:40,885.001 --> 00:17:49,165.001 How did you approach the human side of this rollout? And get the results you have for the engagement as well as the, business impact? Yeah. 179 00:17:49,805.001 --> 00:17:56,375.001 I'm fortunate in that one of simPRO groups core values is growth mindset, and. 180 00:17:57,105.001 --> 00:18:13,805.001 The growth mindset essentially is that you don't believe that anything is fixed or unchanging, and that potential is unlimited and that we're always looking for a different or better way to do something and we recruit off of that. 181 00:18:13,985.001 --> 00:18:23,815.001 It certainly is reflective of the executive leadership team and how we run the our teams, and so I think it started with having a really strong team of talent. 182 00:18:25,45.001 --> 00:18:34,285.001 With no specific plan of, oh, we're gonna implement ai, but knowing that's, these are the kind of things that disrupt businesses and you can never really predict what those things might be. 183 00:18:34,555.001 --> 00:18:40,695.001 But when you have a growth mindset coming into an organization, you're going to be ready to take on any challenge. 184 00:18:40,695.001 --> 00:18:51,225.001 And if that challenge happens to be this incredibly disruptive technology that's moving quickly and unpredictable in terms of where it's headed next. 185 00:18:52,230.001 --> 00:18:57,480.001 I've got an amazing team around me who, like you noticed, they're not afraid and they're willing to dive in head first. 186 00:18:57,480.001 --> 00:19:04,710.001 And so I would say if you're not thinking about that in your recruiting process, that it's gotta be table stakes at this point. 187 00:19:04,760.001 --> 00:19:13,420.001 What is being done around innovation? How is someone thinking about ai? I was just talking with my CEO today about how we're starting to ask in our interviews. 188 00:19:13,945.001 --> 00:19:35,575.001 Just, when was the last time you used chat gt? How are you using chat gt? Do you use chat gt even just asking about something as simple as just, are you using, did you research this interview or this company? You can really start to get a sense of what someone's learning curve is around ai and it's going to tell you a lot about how fast you're gonna have to accelerate that learning curve. 189 00:19:35,605.001 --> 00:19:51,275.001 Think it's really important to have people that are in the organization that are comfortable being very experimental in their roles and make sure they're thinking through like, how can I use this? What can I do differently? Surfacing those things to yourself, leaders, other people on the team. 190 00:19:51,675.001 --> 00:19:55,695.001 So you need someone that can analyze, see how it's working, and then think about scaling it. 191 00:19:56,95.001 --> 00:19:59,180.001 And so you've gotta have the kind of two parts of the two sides of the coin. 192 00:19:59,180.001 --> 00:20:00,200.001 You've gotta have someone that's. 193 00:20:00,740.001 --> 00:20:02,480.001 That's has a growth mindset. 194 00:20:02,600.001 --> 00:20:06,20.001 That's a creative problem solver, willing to experiment. 195 00:20:06,20.001 --> 00:20:09,200.001 And then you've gotta have people that are willing to say, okay, we've done the experiments. 196 00:20:09,200.001 --> 00:20:16,430.001 Now what are we gonna do? How are we gonna model this? How are we gonna scale it? It could be the same person, it could be a team of people depending on the function. 197 00:20:16,460.001 --> 00:20:23,870.001 But where you start to get to in that kind of operational phase is your strategy starts to get more and more informed by ai. 198 00:20:24,740.001 --> 00:20:28,40.001 There's still the, high level corporate decisions that might get made. 199 00:20:28,40.001 --> 00:20:31,580.001 Maybe not everything's being run through ai, but a lot of strategy is being informed by it. 200 00:20:31,940.001 --> 00:20:36,270.001 And the execution is very much like underpinned by AI in many parts of the team. 201 00:20:36,570.001 --> 00:20:47,520.001 So it's starting to influence on all sides and having someone that can both experiment and bring in new ideas and then someone that can scale and predict become really important. 202 00:20:49,125.001 --> 00:21:04,565.001 I think that's a great mindset to help leaders think about right now when they probably have been experimenting with AI and over the past year, but now they, or their sponsors or CEO or CXO is okay, but now what are you gonna do with this? Yeah. 203 00:21:04,825.001 --> 00:21:08,125.001 It's a really valid point and I really like how you're thinking about it. 204 00:21:08,840.001 --> 00:21:12,20.001 The other piece that you mentioned, Rachel, is the strategy piece. 205 00:21:12,20.001 --> 00:21:15,440.001 I feel like when people started it was that let me just execute something. 206 00:21:15,440.001 --> 00:21:15,500.001 Yeah. 207 00:21:15,830.001 --> 00:21:20,930.001 And now I think it's underpinning the strategy, but you're also then tying it to the execution. 208 00:21:21,120.001 --> 00:21:22,410.001 How are they thinking about. 209 00:21:22,645.001 --> 00:21:24,535.001 The strategy and execution together. 210 00:21:25,135.001 --> 00:21:35,745.001 To me one of the most critical pieces of moving through this like maturity phase, because I think that in that early foundational phase, the strategy is very disconnected from AI altogether. 211 00:21:35,745.001 --> 00:21:44,345.001 You're just using it for tactical execution once you're running some of your strategy through and in, informing your strategy through ai. 212 00:21:45,15.001 --> 00:21:52,125.001 It starts, you start to realize like how the non-linear scale AI can give you, could potentially work. 213 00:21:52,155.001 --> 00:21:56,235.001 And so that's where I see right now, we were in this kind of operational phase. 214 00:21:56,235.001 --> 00:22:00,405.001 My kind of highest level is this autonomous ai and. 215 00:22:00,780.001 --> 00:22:04,950.001 One of the kind of pieces of that is that strategy is led by, by ai. 216 00:22:05,280.001 --> 00:22:10,470.001 So rather than someone coming in and saying, we should go to market in Peru, I'm totally making that up. 217 00:22:10,750.001 --> 00:22:22,130.001 You have insights that are being brought forward through AI in real time around where your opportunities are, what the messages need to be what kind of segmentation might look like and. 218 00:22:22,385.001 --> 00:22:29,215.001 It's most blown out state that you could pretty much run all of that very quickly through, through AI and have, marketing. 219 00:22:29,575.001 --> 00:22:34,515.001 My team, I would say is still in that kind of operational phase and as am I. 220 00:22:35,370.001 --> 00:22:42,390.001 I, if I am asked to go to a meeting and it's some kind of strategic conversation, I probably will consult AI in some way or another. 221 00:22:42,670.001 --> 00:22:50,0.001 I think that's where, we get into things like digital twins and being able to understand other people's or other cohorts. 222 00:22:50,675.001 --> 00:22:53,45.001 Of thinking and ways of making decisions. 223 00:22:53,435.001 --> 00:22:58,595.001 But I think where we're headed is that strategy will be mostly led by ai. 224 00:22:58,595.001 --> 00:23:02,675.001 It's gonna, it's going to take a while, but I think there will start to be small tweaks again. 225 00:23:02,775.001 --> 00:23:05,775.001 Like in the early days where you start to do some smaller. 226 00:23:05,810.001 --> 00:23:06,650.001 Low hanging fruit. 227 00:23:06,650.001 --> 00:23:09,920.001 Let's try a little mini campaign that AI has recommended. 228 00:23:09,920.001 --> 00:23:10,970.001 Let's see how it performs. 229 00:23:10,970.001 --> 00:23:12,380.001 Suddenly it performs really well. 230 00:23:12,380.001 --> 00:23:14,390.001 Then you start seeing that kind of get blown out. 231 00:23:14,730.001 --> 00:23:19,200.001 I think we're a ways off on it fully being that way, especially at the corporate level. 232 00:23:19,410.001 --> 00:23:22,802.001 But I think within the marketing organization I actually don't think we're very far off at all. 233 00:23:25,225.001 --> 00:23:32,605.001 Hey you just mentioned a digital twin, and I remember when I met you, you mentioned that you actually had one. 234 00:23:32,865.001 --> 00:23:38,695.001 What inspired that idea and then what are you doing with it? I cannot remember. 235 00:23:38,695.001 --> 00:23:44,35.001 I've tried, I wish I could tell me where I first learned about this concept of making a digital twin. 236 00:23:44,65.001 --> 00:23:52,995.001 I've heard about creating your own custom GPTs, I heard about it and I just found it really interesting as an exercise really. 237 00:23:53,45.001 --> 00:23:55,620.001 I'm always trying to understand other human beings. 238 00:23:55,740.001 --> 00:23:58,560.001 One of my Gallup strengths is being a relator. 239 00:23:58,590.001 --> 00:24:04,280.001 And so I tend to do better in one-to-one or smaller, more, deeper relationships. 240 00:24:04,610.001 --> 00:24:09,530.001 And so I like to really understand all the angles of a person and how they think and what they might ask me. 241 00:24:09,530.001 --> 00:24:09,740.001 And. 242 00:24:10,425.001 --> 00:24:11,475.001 What motivates them. 243 00:24:11,475.001 --> 00:24:17,505.001 And so for me, I thought of it initially as oh, this would be a great thing to use for other digital twins. 244 00:24:17,835.001 --> 00:24:20,775.001 But before I did that, I was like, I'll just make one of myself and see how it does. 245 00:24:21,105.001 --> 00:24:25,815.001 So I I started it because I wanted to experiment and see what happened. 246 00:24:26,355.001 --> 00:24:30,795.001 And then suddenly it was like, oh wow, this is super useful to me in so many different ways. 247 00:24:30,855.001 --> 00:24:35,295.001 And what I would say is that it's helped me scale. 248 00:24:36,500.001 --> 00:24:51,30.001 The energy and the my ability to be in a lot of places and show my face in a lot of ways across the marketing organization or even online, because one of the challenges I have is that, I work in a global organization. 249 00:24:51,100.001 --> 00:24:56,590.001 I have teams in Australia, New Zealand, the uk, all over the world, and it's hard to. 250 00:24:57,355.001 --> 00:25:03,205.001 Especially for an executive leader, you can never talk or communicate enough, and you can never get in front of people often enough. 251 00:25:03,325.001 --> 00:25:08,605.001 And the world we live in has made it very challenging to, be everywhere you need to be all the time. 252 00:25:09,55.001 --> 00:25:16,870.001 And so I had actually been going through with, an executive coach, trying to think about ways that I could increase people's knowledge of who I am. 253 00:25:17,225.001 --> 00:25:25,165.001 What motivates me and what what I like and what I'm thinking and how to work with me and really make myself a lot more visible across the organization. 254 00:25:25,165.001 --> 00:25:27,385.001 Because we had a ton of change management going on. 255 00:25:27,595.001 --> 00:25:45,780.001 We had a lot of new people coming into the organization through acquisitions and hiring, and one of the things that I wanted to do was I love to write, I wanted to start sending out a monthly newsletter, email, motivational type email to my team just with my thoughts and it wasn't meant to be super business oriented. 256 00:25:45,780.001 --> 00:25:52,105.001 It was meant to be a storytelling opportunity, sharing more about my background and my who I am and how I got started. 257 00:25:52,585.001 --> 00:25:54,385.001 And I wrote a few of them. 258 00:25:54,385.001 --> 00:25:58,885.001 And then I had this digital twin idea and I thought, I wonder if I could teach this to write. 259 00:25:59,305.001 --> 00:26:02,515.001 My, my emails for me for this month by email. 260 00:26:02,995.001 --> 00:26:12,145.001 And what's crazy is I fed it, things I wrote in high school, I fed it a my, in my MBA application essay. 261 00:26:12,595.001 --> 00:26:16,615.001 I fed it all sorts of stuff from the internet that I've written or podcast. 262 00:26:16,615.001 --> 00:26:24,235.001 I've been on my LinkedIn profile, my Gallup strengths, random emails that I've written that I thought were worded or whatever. 263 00:26:24,685.001 --> 00:26:25,285.001 And. 264 00:26:26,80.001 --> 00:26:32,610.001 Now I go in there and I basically say, I wanna write, and each of my monthly emails is tied to one of our company core values. 265 00:26:32,610.001 --> 00:26:33,690.001 And I rotate through them. 266 00:26:33,690.001 --> 00:26:34,620.001 We have five. 267 00:26:35,220.001 --> 00:26:37,550.001 And I say, I wanna write about growth mindset. 268 00:26:38,645.001 --> 00:26:43,925.001 And I'll say, write me a 500 word email, drawing from my story and who I am about growth mindset. 269 00:26:44,495.001 --> 00:26:46,475.001 And sometimes I'll sprinkle new stuff in. 270 00:26:46,475.001 --> 00:26:52,565.001 I'll be like, based on the fact that this happened in Q1 or based on this going on, in the world or whatever. 271 00:26:52,825.001 --> 00:26:56,665.001 And I'll read it and I'll review it and I'll tweak it and adjust it, and I send it out. 272 00:26:56,665.001 --> 00:27:01,765.001 And I think it was in your session that my team had no idea that I, for the last, I've been doing it for almost a year now. 273 00:27:02,120.001 --> 00:27:10,140.001 And only probably the first two or three I actually wrote myself and the rest were largely Jen and I with a little bit of sprinkling of my own writing. 274 00:27:10,150.001 --> 00:27:11,680.001 it's helped me stick with it. 275 00:27:11,680.001 --> 00:27:17,30.001 It's helped me have more time to, scale that and also do all the other things I have to do each day. 276 00:27:17,360.001 --> 00:27:19,370.001 Sometimes I'll use it to write a slack post. 277 00:27:19,370.001 --> 00:27:21,20.001 I'll use it to write a LinkedIn post. 278 00:27:21,590.001 --> 00:27:24,770.001 I use it for smaller format communications now as well. 279 00:27:25,100.001 --> 00:27:26,500.001 And I use it for a lot of 'em. 280 00:27:26,500.001 --> 00:27:31,80.001 I just one-on-one emails that I do, so it's been really valuable to me. 281 00:27:31,120.001 --> 00:27:40,50.001 And I've started to do other digital twins, which I'm happy to talk more about, but I think, it's been something I've really enjoyed building, I guess I would say. 282 00:27:41,460.001 --> 00:27:43,690.001 I was gonna say maybe you heard about it on FutureCraft. 283 00:27:43,720.001 --> 00:27:47,210.001 I love your ideas, around creating the monthly content and using it. 284 00:27:47,460.001 --> 00:27:55,25.001 With mine I definitely have used it more to give to my team to say, Hey, brainstorm with her, I think using it myself, I could get a lot of benefit. 285 00:27:55,435.001 --> 00:28:01,565.001 I'm curious to think about how you balance some of the automation with maintaining the originality. 286 00:28:01,565.001 --> 00:28:06,815.001 'cause that's something I really struggle with, is I get so excited with what AI can do and the speed. 287 00:28:07,25.001 --> 00:28:07,85.001 Yeah. 288 00:28:07,295.001 --> 00:28:10,415.001 But then it's like you're almost over reliant on it. 289 00:28:10,415.001 --> 00:28:13,545.001 And so how do you balance the two? Yeah. 290 00:28:13,585.001 --> 00:28:15,565.001 I used to work with a woman named Lonnie Stark. 291 00:28:15,565.001 --> 00:28:20,735.001 She's a VP at Adobe, and she is, a, an incredibly talented. 292 00:28:21,715.001 --> 00:28:22,855.001 Product marketer. 293 00:28:22,915.001 --> 00:28:23,905.001 Product manager. 294 00:28:24,5.001 --> 00:28:33,365.001 But she's also an amazing artist and she recently had a LinkedIn post about her kind of relationship with ai, I thought was really beautifully written. 295 00:28:33,875.001 --> 00:28:39,935.001 But one of the things that I wrote on the comments on her LinkedIn was that, to me when I write, because I do love to write. 296 00:28:41,180.001 --> 00:28:46,320.001 It's a amalgamation of everything I've seen or heard and read and experienced in my life. 297 00:28:46,320.001 --> 00:28:48,300.001 Maybe in my past lives, I have no idea. 298 00:28:48,300.001 --> 00:28:53,740.001 But what comes out when I write is it's my own, but it's a lot of things and. 299 00:28:54,190.001 --> 00:29:01,540.001 Whether it's symbolism that was inspired by something I read, or a style of writing when growing up that I loved, that I try to emulate. 300 00:29:01,960.001 --> 00:29:09,580.001 For one long time I was in a big David Sedaris kick and I wrote tons of like humorous personal essays and narratives, right? Like about all these funny things in my life. 301 00:29:10,390.001 --> 00:29:19,60.001 So to me it's almost akin to, this idea that it's pulling from your knowledge, but it's also pulling from other places. 302 00:29:19,540.001 --> 00:29:21,40.001 And then at the end of the day. 303 00:29:21,385.001 --> 00:29:29,185.001 When you go through it and look at it like you're the one that decides what to do with it, like I could just throw it in the trash or never show it to anyone. 304 00:29:29,505.001 --> 00:29:34,65.001 I could decide actually, I don't wanna talk about the growth mindset value. 305 00:29:34,65.001 --> 00:29:35,355.001 I wanna talk about one team. 306 00:29:36,240.001 --> 00:29:38,790.001 I came up with that those decisions were mine. 307 00:29:39,210.001 --> 00:29:47,130.001 The tweaks I make to it, the fact that I send it out, the timing of when I send out the motivation of why I send it out, all those things are still me and mine. 308 00:29:47,560.001 --> 00:29:52,290.001 And so I, I'm not out here trying to get like a grade on a, college essay. 309 00:29:52,290.001 --> 00:29:54,60.001 So I think there's a little bit of a difference. 310 00:29:55,170.001 --> 00:30:01,515.001 I am simply looking for ways to build and create a story or a motivation or an emotion in someone. 311 00:30:02,235.001 --> 00:30:03,585.001 And it's helping me do that. 312 00:30:03,585.001 --> 00:30:08,200.001 So is it as rewarding to me as writing something on my own? No, for sure not. 313 00:30:08,260.001 --> 00:30:14,880.001 But is it something that I feel pretty good about, like that I can make it into something that represents me and how I think and what I'm saying. 314 00:30:14,880.001 --> 00:30:16,170.001 I actually, I think it's great. 315 00:30:16,230.001 --> 00:30:22,320.001 And and sometimes it, I learn something, reading the stuff that I come, my digital twin comes up with. 316 00:30:22,860.001 --> 00:30:24,720.001 And my last one, it came up with a quote. 317 00:30:24,720.001 --> 00:30:26,400.001 It was like, there's a quote I've always loved. 318 00:30:26,985.001 --> 00:30:30,525.001 Never seen the quote before in my life, but I actually do love it. 319 00:30:31,15.001 --> 00:30:34,595.001 And so I I'm gonna keep that quote in my back pocket. 320 00:30:34,655.001 --> 00:30:39,245.001 And I think that's where it's like I'm learning to, I'm being influenced by my own digital twin. 321 00:30:39,675.001 --> 00:30:41,175.001 But I don't have any issues with it. 322 00:30:41,175.001 --> 00:30:52,815.001 And the other thing I'll say more damaging than anything else to my writing has been just being on a computer all day and having a phone in my pocket, the last thing I wanna do when I get done working is sit at a computer and write. 323 00:30:53,470.001 --> 00:31:05,555.001 Fiction like I think someday when I'm an 85, I'll probably take up writing again, but it, I feel like I spend so much of my time communicating now that writing is at the least of the things I want to do. 324 00:31:05,555.001 --> 00:31:06,925.001 So I do mourn. 325 00:31:06,955.001 --> 00:31:15,210.001 Technology's impact on my writing, creativity in my interest, but I, it was long gone before AI got here, so I don't blame my AI for that. 326 00:31:15,210.001 --> 00:31:17,580.001 In fact, I would say it's helped me revive some of it. 327 00:31:19,5.001 --> 00:31:21,725.001 In early days of writing one pagers, it doesn't inspire. 328 00:31:23,75.001 --> 00:31:28,5.001 No, nothing's really, I remember when I finally realized that no one ever read any emails. 329 00:31:28,135.001 --> 00:31:33,465.001 I write when I was in my twenties, these long emails, and then I learned that people didn't read more than three sentences. 330 00:31:33,465.001 --> 00:31:35,115.001 And I'm like, okay, I'm done. 331 00:31:35,145.001 --> 00:31:36,555.001 I've done writing altogether. 332 00:31:37,185.001 --> 00:31:39,735.001 Just call, calling people slacking, texting. 333 00:31:40,135.001 --> 00:31:43,945.001 But the, those days of even like a well worded email are pretty much over. 334 00:31:44,765.001 --> 00:31:53,165.001 I mean it's funny thinking about people say, oh, using ai are you like cheating or there something wrong? And I'm like, if I build a house with a hammer, am I cheating? Yeah. 335 00:31:53,165.001 --> 00:31:56,55.001 Didn't use my hand and get the nails into the wood. 336 00:31:56,65.001 --> 00:31:59,965.001 It's not, to your point, like I'm still reviewing it, I'm still talking about it. 337 00:31:59,965.001 --> 00:32:01,265.001 That I'm there with you. 338 00:32:01,620.001 --> 00:32:03,990.001 Rachel, marketing and BDR teams. 339 00:32:03,990.001 --> 00:32:08,960.001 We talked a little bit about what you guys are doing at simPRO with the Daniella and her friends. 340 00:32:09,290.001 --> 00:32:17,610.001 But they're often separate in terms of, one's on sales, one's on marketing, or sometimes BDRs are on marketing and then you've got the sales handoff. 341 00:32:17,940.001 --> 00:32:21,810.001 I'm curious to think about how you intentionally brought. 342 00:32:22,155.001 --> 00:32:23,775.001 These two functions together. 343 00:32:23,775.001 --> 00:32:37,805.001 And has integrating AI helped you unify and streamline these functions? Or are you seeing more improvement on conversion? Just both from top of the funnel, but also through the handoff to sales? Sales and marketing alignment is agent tells all this time. 344 00:32:37,915.001 --> 00:32:38,965.001 Always a challenge. 345 00:32:38,995.001 --> 00:32:46,885.001 One of the challenges we implemented AI around was because we were losing a lot of deals due to speed to leap. 346 00:32:47,425.001 --> 00:32:51,210.001 And we went initially, when we first rolled out Daniella. 347 00:32:51,880.001 --> 00:32:58,370.001 We actually had a round robin happening where she would get all the leads just as a fifth, BDR. 348 00:32:58,370.001 --> 00:33:02,180.001 So we had four BDRs and she would get the fifth one and she would respond immediately. 349 00:33:02,210.001 --> 00:33:03,830.001 And we saw incredible results. 350 00:33:04,190.001 --> 00:33:08,500.001 And and then when we started rolling it out initially in another region. 351 00:33:08,875.001 --> 00:33:14,315.001 They really didn't want to give up any of their round robin to, the new Daniella. 352 00:33:14,655.001 --> 00:33:16,815.001 And so we said, fine, no worries. 353 00:33:16,815.001 --> 00:33:24,285.001 Like, how about we have Daniella follow up with all the leads after if they haven't been touched in two hours, she picks 'em up. 354 00:33:25,35.001 --> 00:33:28,95.001 So that was the kind of second use case. 355 00:33:28,155.001 --> 00:33:30,945.001 And with the first Daniella when she was in the round robin. 356 00:33:31,490.001 --> 00:33:35,240.001 We saw a conversion rate is instantly improve. 357 00:33:35,340.001 --> 00:33:45,430.001 It, at the time we had a, about 30% of our leads were lost due to could not contact, which basically was the BDRs way of saying they never responded to me after I reached out. 358 00:33:45,965.001 --> 00:33:48,815.001 That number went down by about 80%. 359 00:33:48,905.001 --> 00:33:59,375.001 It went down significantly in the first, 30 days of Danielle in the round robin because in fact, these people were responding and we were getting to them faster. 360 00:33:59,375.001 --> 00:34:00,935.001 They were responding more weekly. 361 00:34:01,295.001 --> 00:34:07,435.001 And when we launched the second AI VDR in the new region, and we did this two hour wait. 362 00:34:08,410.001 --> 00:34:09,700.001 We weren't seeing the results. 363 00:34:09,810.001 --> 00:34:21,840.001 Daniella had a 45% MQL to sales qualified lead conversion rate, and the second BDR Sam is her name, Sam, had a 25% MQL to SQL conversion rate. 364 00:34:21,960.001 --> 00:34:31,310.001 And we pointed out to the team, we said, look like this two hour thing is not helping you, we get that you wanna have all the round robins, but this isn't working either. 365 00:34:31,790.001 --> 00:34:40,290.001 And so now what we did is we essentially just started running Sam on a hundred percent of inbound in that region as soon as the lead comes in. 366 00:34:40,650.001 --> 00:34:44,280.001 Because at the end of the day, those p the BDRs can still reach out to the leads. 367 00:34:44,820.001 --> 00:34:51,240.001 They can still stay in round robin, but they've got Sam as their backup doing immediate instant email outreach. 368 00:34:51,600.001 --> 00:34:52,920.001 And we've seen the number. 369 00:34:53,400.001 --> 00:34:57,470.001 Jump back up to the healthier, kind of 40% what that region has. 370 00:34:57,470.001 --> 00:35:09,530.001 So sometimes, and this is like I learned this day one of sales and marketing alignment school is approaching a problem with data, right? It's, sometimes you need the data to be able to have the conversation. 371 00:35:10,110.001 --> 00:35:18,610.001 The art of the art of the know, which is sometimes saying, sure, we'll try that, and then if it doesn't work, we'll try something different, which is the thing we actually wanted to do. 372 00:35:18,890.001 --> 00:35:23,960.001 It's classic sales and marketing alignment challenges just with a different thing around them. 373 00:35:24,350.001 --> 00:35:35,60.001 But what I would say is that it's been really great to see how the team, the lead, the sales team, the leadership, have embraced the concept of. 374 00:35:35,765.001 --> 00:35:37,955.001 AI automation to the point where now. 375 00:35:38,775.001 --> 00:35:45,495.001 It's the new Hey, can you throw a happy hour for us? It's Hey, can we do another AI outreach on this segment? Can we do an AI outreach? Oh, that's not working. 376 00:35:45,495.001 --> 00:35:51,415.001 What about AI outreach? So there's a lot of asks coming in around AI outreach because it does work so well. 377 00:35:51,615.001 --> 00:35:56,245.001 And it's become a really great tool in our toolbox that, the sales team understands is really valuable. 378 00:35:56,245.001 --> 00:36:07,345.001 And for a marketer that's as about as good as it gets when you know you've got something that's working and the sales team's seeing the value of, so that's also helped drive a lot of, I would say, confidence and partnership between the two teams. 379 00:36:09,145.001 --> 00:36:20,740.001 I think that's a pretty incredible example of how AI can be used to address some of these challenges that have existed before ai, a twinkle in someone's eye. 380 00:36:21,130.001 --> 00:36:30,30.001 Thinking about the other go-to-market functions and relationships that, marketing teams working with each other have, or even some traditional challenges that may have with customer success or product. 381 00:36:30,310.001 --> 00:36:51,155.001 Can you think of another example of how AI could potentially help? Solve some of those challenges or improve the situation? Yeah, we actually, we're looking at it on the post-sale side of things is how we, how can we leverage AI and email around basically after a deal is done to identify potentially, for example. 382 00:36:52,490.001 --> 00:36:55,360.001 New customers that are showing low engagement. 383 00:36:55,360.001 --> 00:37:01,130.001 So we're looking at engagement and adoption score in some of our tools on the product side. 384 00:37:01,460.001 --> 00:37:07,190.001 And if we see that they're showing low engagement, can we have AI automation and pick up and start. 385 00:37:07,445.001 --> 00:37:13,425.001 Reaching out to them, using tools like Help Guides and some of the, content that we've already created. 386 00:37:13,425.001 --> 00:37:15,805.001 It's already been, buried away in the website somewhere. 387 00:37:16,215.001 --> 00:37:21,855.001 And same process, if someone engages, oh yeah, actually I am struggling, or, oh I, I don't get this. 388 00:37:22,425.001 --> 00:37:31,235.001 That's where the goal, what we've set out the AI agent to do, or what we have on the roadmap is, Hey, loop in the CSM or loop in the support wrap. 389 00:37:31,690.001 --> 00:37:45,620.001 And so that one is obviously has huge potential for especially in a scaled business like ours where we have a lot of volume and a high number of customers coming in where we can't always do one-to-one account management. 390 00:37:45,900.001 --> 00:37:57,170.001 High touch account management on all of our accounts is really, a game changer for us in terms of being able to improve, net retention improve, our expansion and improve adoption scores on the product. 391 00:37:59,185.001 --> 00:38:06,315.001 I think that's, it's interesting you talk about scale and the folks that are like the puppet masters behind these new tools a little bit. 392 00:38:06,315.001 --> 00:38:16,265.001 I think you said something around how do we evolve alongside AI instead of trying to control it? And I'd be curious how you would answer that for go to market leaders now. 393 00:38:18,35.001 --> 00:38:22,85.001 It's a question I have a lot of conversations about and. 394 00:38:22,680.001 --> 00:38:31,60.001 I liken it to this idea that, when I was a child, I'm sure my parents could not have predicted that I would be doing this for a living. 395 00:38:31,60.001 --> 00:38:35,710.001 I know that they didn't because I told them I wanted to be a reporter or a veterinarian. 396 00:38:35,930.001 --> 00:38:40,70.001 Someone today I told me, I don't know what my, I worry about my kids in an AI future. 397 00:38:40,70.001 --> 00:38:44,840.001 What will they do? What will their work be? And it's funny because I've never, once I have two kids, I've never thought about that. 398 00:38:44,840.001 --> 00:38:45,350.001 I never even. 399 00:38:45,965.001 --> 00:38:46,685.001 Pondered it. 400 00:38:47,55.001 --> 00:38:50,385.001 They're five and four, so they got a long way to go before they entered the workforce. 401 00:38:50,385.001 --> 00:39:01,845.001 But I said to them, I said did your parents know that you would be using all this technology and working at a private equity bank software company? Did they know you'd be using chat GPT? No. 402 00:39:01,895.001 --> 00:39:03,725.001 So it is hard. 403 00:39:03,725.001 --> 00:39:06,545.001 I think probably the hardest thing is trying to predict. 404 00:39:07,550.001 --> 00:39:17,670.001 What wave you're on and where's it gonna land and what's the next wave to catch? But if you look at it on the long arc of history, I think it's very likely that, everything is evolving always. 405 00:39:17,670.001 --> 00:39:19,200.001 We just don't always realize it. 406 00:39:19,530.001 --> 00:39:19,800.001 And. 407 00:39:20,295.001 --> 00:39:23,55.001 There's going to be plenty of work for people to do. 408 00:39:23,145.001 --> 00:39:26,715.001 I if that involves more time for margaritas on the beach, sign me up. 409 00:39:27,125.001 --> 00:39:30,5.001 But I think that there will be work, plenty of work left to do. 410 00:39:30,5.001 --> 00:39:31,175.001 It's just going to look different. 411 00:39:31,175.001 --> 00:39:32,765.001 And that's the story that's always been there. 412 00:39:32,765.001 --> 00:39:35,135.001 It's happened in manufacturing, it's happened in automotive. 413 00:39:35,135.001 --> 00:39:38,315.001 Like this is a story that's happened many times before. 414 00:39:38,705.001 --> 00:39:41,75.001 And so I think it's being open to the change. 415 00:39:41,535.001 --> 00:39:48,135.001 Being open to, again, trying different things at a kind of low level and building a muscle. 416 00:39:48,135.001 --> 00:39:52,755.001 It's like a workout, it's, the more often you lift that heavy thing, the easier it gets to lift. 417 00:39:52,815.001 --> 00:40:03,305.001 And so if you can lift a little bit each day and then start lifting heavier things I learn every day about something that I can do and with AI or using AI that I didn't know that I could. 418 00:40:03,305.001 --> 00:40:14,655.001 And, it unlocks a lot of creativity and potential, and so if you can come at it with that mindset, that growth mindset, I think you'll be well positioned for the future regardless of, where you sit in any organization across any function. 419 00:40:15,505.001 --> 00:40:16,615.001 I think you're right. 420 00:40:16,675.001 --> 00:40:23,35.001 Taking on a growth mindset right now when so much is changing is just the best way to tee you up for whatever is gonna happen. 421 00:40:23,35.001 --> 00:40:24,645.001 And we are gonna have jobs. 422 00:40:24,645.001 --> 00:40:26,745.001 They just might be different than they're Yeah. 423 00:40:26,745.001 --> 00:40:42,515.001 And I know we are almost outta time, but I wanted to tell a funny anecdote about the whole BDR job killer thing, which is that at my last company we had a BDR who was really strong BDR and I remember he essentially started a Udemy course on how to useche GPT when it first came out. 424 00:40:42,950.001 --> 00:40:45,80.001 And I was like, oh, that's so cool that you're doing that. 425 00:40:45,80.001 --> 00:40:55,160.001 Can you come give a lunch and learn to the marketing organization, the VR team about chat chi pt? And it was like, wow, look at all this stuff that it can do and you can feed it all these things. 426 00:40:55,610.001 --> 00:41:00,260.001 And it was, very simple stuff, but at the time, very exciting and interesting. 427 00:41:00,320.001 --> 00:41:01,870.001 And he ended up. 428 00:41:02,595.001 --> 00:41:04,755.001 So much money on his Udemy course. 429 00:41:04,875.001 --> 00:41:14,725.001 He turned himself into an AI consultant for businesses, quit his BDR job, and now is, doing his own AI consulting and, podcasting and everything else. 430 00:41:14,725.001 --> 00:41:18,625.001 So I love that story because it's such a great example of it didn't kill his job. 431 00:41:18,625.001 --> 00:41:21,385.001 He found better opportunities leveraging ai. 432 00:41:21,635.001 --> 00:41:32,415.001 And I think that's where like the magic is in all of this, is that your job is probably going to be different, but there's a lot of opportunity and, hang on for the ride and try to, scale up as best as you can and learn. 433 00:41:33,45.001 --> 00:41:33,375.001 Yeah. 434 00:41:34,80.001 --> 00:41:34,710.001 I'd love that. 435 00:41:34,950.001 --> 00:41:35,220.001 Yeah. 436 00:41:35,470.001 --> 00:41:39,310.001 To wrap us up, we always love to do a rapid fire round where we get your quick hits on. 437 00:41:39,310.001 --> 00:41:40,660.001 A few questions. 438 00:41:40,880.001 --> 00:41:49,230.001 Rachel, let's start with what's your single best quick AI tiff for go to market teams? It's gotta be, don't boil the ocean. 439 00:41:51,265.001 --> 00:42:07,345.001 What's your favorite AI prompt or workflow that you like to use? Create a digital twin of a board member and have the digital twin do some board member scenarios in a board meeting and grill you with questions and give you feedback on how you could answer them better. 440 00:42:07,975.001 --> 00:42:08,425.001 I love that. 441 00:42:09,15.001 --> 00:42:17,310.001 What's your go-to source for staying up to speed on AI trends when there's so much out there? The simPRO Group executive leadership team. 442 00:42:17,310.001 --> 00:42:24,500.001 We have a Slack channel where we share AI trends and we're all very much engaged with AI and using different tools. 443 00:42:24,500.001 --> 00:42:25,790.001 So that's my go-to. 444 00:42:25,790.001 --> 00:42:29,180.001 Sorry, it's a private channel, but that's, I'm not gonna find anything better than that. 445 00:42:30,320.001 --> 00:42:45,450.001 And then what is that hidden GEM AI tool that GTM leaders should go check out right now? I would say aside from six sense conversational email, which is six sense a AI email agent peak ai pc.ai 446 00:42:45,450.001 --> 00:42:49,620.001 is an AI search visibility tool, and I think that's another area less so on. 447 00:42:49,680.001 --> 00:42:54,0.001 How are you using ai? More about how is AI impacting your business? That's becoming more and more important. 448 00:42:54,180.001 --> 00:42:56,880.001 So check out or take a peek at Peak ai. 449 00:42:56,880.001 --> 00:42:56,940.001 I. 450 00:42:59,100.001 --> 00:43:08,810.001 Okay, Rachel, I bet you would've been a great reporter or veterinarian, but I'm really happy that you picked marketing because we learned a lot and I think our audience is gonna learn a lot as well. 451 00:43:08,810.001 --> 00:43:10,820.001 So thank you so much for joining us today. 452 00:43:11,540.001 --> 00:43:12,650.001 Thank you so much for having me. 453 00:43:12,650.001 --> 00:43:13,430.001 It was really fun. 454 00:43:13,790.001 --> 00:43:15,590.001 Great, and we'll be right back everyone. 455 00:43:16,90.001 --> 00:43:16,960.001 Okay. 456 00:43:17,320.001 --> 00:43:20,710.001 What'd you think Erin? A lot of fun in the conversation. 457 00:43:20,760.001 --> 00:43:24,780.001 I was particularly interested by the way they're using the AI SDRs. 458 00:43:24,960.001 --> 00:43:27,900.001 Something that we're looking into at my organization. 459 00:43:28,170.001 --> 00:43:45,0.001 Beyond just how it's being used, the idea of resource planning, so thinking about different types of roles how do I think about adding more marketing operations folks? Which I think is really smart because it's anticipating that this is gonna grow over time and then you're hiring for the right skillset. 460 00:43:45,360.001 --> 00:43:53,505.001 What about you, Ken? What'd you learn? I was impressed by Rachel's approach to working with her team on the culture shift around implementing ai. 461 00:43:53,805.001 --> 00:43:55,575.001 But what I found really, I. 462 00:43:56,340.001 --> 00:44:00,60.001 Promising and exciting was her view on using AI in general. 463 00:44:00,60.001 --> 00:44:08,150.001 There's a bit of a stigma about, is using AI cheating and she, really hit on this point that said these are my ideas. 464 00:44:08,600.001 --> 00:44:11,30.001 I'm the one contributing to it and I'm the one building it. 465 00:44:11,30.001 --> 00:44:18,250.001 So this is me, the stuff that she's doing with her digital twin, making an extension of herself, she's leveraging that to drive greater impact. 466 00:44:18,625.001 --> 00:44:23,65.001 With her voice and her ideas and her thoughts, and I just think that we're gonna see that more and more. 467 00:44:23,65.001 --> 00:44:31,175.001 Right now she's really leading the way on those discussions around why, using AI is actually a tool to help people rather than doing something to replace you. 468 00:44:31,970.001 --> 00:44:36,950.001 Yeah, I just think it's still so wild that we're having conversations around whether AI is cheating or not. 469 00:44:37,475.001 --> 00:44:37,715.001 Yeah. 470 00:44:37,970.001 --> 00:44:40,375.001 post with Christopher Penn the other day, and. 471 00:44:41,180.001 --> 00:44:52,225.001 He was talking about, he went and audited a site that was very clearly written by humans, and he was this is slop like, but GPT, like ChatGPT slop is gonna be a little bit better than that slop. 472 00:44:52,225.001 --> 00:45:07,705.001 So what are we talking about here? And I think that's what's really interesting to me is that are we overselling some of human like created things and underselling our ability to interact with AI to make something that's really meaningful. 473 00:45:08,350.001 --> 00:45:08,740.001 Yeah. 474 00:45:08,770.001 --> 00:45:09,765.001 It I agree with you. 475 00:45:09,815.001 --> 00:45:16,75.001 I would wonder if anyone listening to us, if they are in this camp of not using ai, reach out to us. 476 00:45:16,75.001 --> 00:45:16,715.001 'cause we wanna talk. 477 00:45:16,715.001 --> 00:45:18,455.001 We'd love to understand your perspective. 478 00:45:18,515.001 --> 00:45:20,885.001 We definitely don't know everything and we would, I. 479 00:45:20,885.001 --> 00:45:25,595.001 I'd love to have you come on the show maybe and talk to us about your perspective, but I see the same thing. 480 00:45:25,655.001 --> 00:45:32,285.001 A 2023 study came out that said that people view users of AI as lazy. 481 00:45:32,735.001 --> 00:45:37,295.001 And when I think about the people I know using ai, they're anything but lazy. 482 00:45:37,515.001 --> 00:45:39,295.001 They're, hyper productive. 483 00:45:39,295.001 --> 00:45:41,155.001 They're looking for efficiencies to gain. 484 00:45:41,545.001 --> 00:45:48,755.001 And I do think, like I said, that study was in 2021, so I'm curious if that has evolved, but there's still a lot of resistance What I would, here's what I would say. 485 00:45:48,755.001 --> 00:46:00,460.001 If I, someone told me that they thought AI was cheating and they weren't gonna use it I would try to talk to them about the idea of the internet, right? The internet's coming, and if even if you aren't using it like you're still, you. 486 00:46:01,30.001 --> 00:46:02,410.001 You're still part of this world. 487 00:46:02,410.001 --> 00:46:05,590.001 And so give it a try and at least understand why you don't like it. 488 00:46:05,810.001 --> 00:46:09,490.001 And I, I call this the no prize because okay, so the prompt didn't work. 489 00:46:09,520.001 --> 00:46:10,390.001 Here's your no prize. 490 00:46:10,390.001 --> 00:46:11,230.001 Congratulations. 491 00:46:11,230.001 --> 00:46:16,710.001 But really give it a good try and figure out what's actually blocking you from wanting to leverage the tool. 492 00:46:16,710.001 --> 00:46:18,850.001 And, maybe that can actually help make the tool better. 493 00:46:19,405.001 --> 00:46:20,725.001 Yeah, totally agree. 494 00:46:21,65.001 --> 00:46:27,245.001 We have a fun tool today that we're gonna show, and I have been really leveraging this technology. 495 00:46:27,615.001 --> 00:46:30,775.001 This is one I think that a lot of folks listening can benefit from. 496 00:46:30,775.001 --> 00:46:32,890.001 So do you mind if I jump in? Yes, please. 497 00:46:32,890.001 --> 00:46:33,465.001 I can't wait to see. 498 00:46:33,955.001 --> 00:46:42,535.001 Okay, so today we're covering 11 labs, and for those of you that don't know, 11 Labs is a voice interface that you can interact with. 499 00:46:42,595.001 --> 00:46:45,355.001 You can create podcasts, you can dub videos. 500 00:46:45,625.001 --> 00:46:46,555.001 There's a whole slew of things. 501 00:46:46,555.001 --> 00:46:56,295.001 But I'm gonna show you my favorite thing to play with and use for a bunch of different use cases All right, and we have a pretty cool new tool to check out. 502 00:46:56,295.001 --> 00:47:05,595.001 Now we talk a lot about AI bloat and obviously you don't wanna overload your tech stack, but this one I think a lot of our listeners can get benefit from. 503 00:47:05,595.001 --> 00:47:09,595.001 So, Ken, do you mind if we start diving into 11 labs? Let's do it. 504 00:47:10,0.001 --> 00:47:10,420.001 Awesome. 505 00:47:10,750.001 --> 00:47:14,440.001 So for those of you who aren't familiar, 11 Labs is a voice tool. 506 00:47:14,440.001 --> 00:47:21,10.001 So think about it as being able to do text to speech or change your voice, or a lot of folks use it for podcasts. 507 00:47:21,370.001 --> 00:47:26,950.001 And I actually think one of the most impactful bits of functionality it has is the conversational ai. 508 00:47:27,220.001 --> 00:47:32,890.001 So that's what we're gonna cover today and how to build an agent that there's a ton of use cases for. 509 00:47:32,890.001 --> 00:47:34,390.001 But we're gonna use one for training. 510 00:47:34,900.001 --> 00:47:36,640.001 So this is the homepage of 11 Labs. 511 00:47:36,640.001 --> 00:47:43,730.001 What's cool is there is a free offering that they have, and actually,, my workspace today is on that free tool. 512 00:47:43,730.001 --> 00:47:47,660.001 So you have access to a wide breadth of their functionality. 513 00:47:47,840.001 --> 00:47:52,125.001 It's just limiting some of the credits that you can use you know, if you find it useful, you can upgrade. 514 00:47:52,345.001 --> 00:47:59,65.001 One of the things I think that's really valuable here is there's a lot of opportunity to take your text, turn it into a different format. 515 00:47:59,65.001 --> 00:48:05,585.001 So a lot of folks will use it for podcasts and you can also take and make different sound effects for videos and things of that nature. 516 00:48:05,795.001 --> 00:48:15,65.001 What I find the most interesting with 11 Labs is their conversational ai, and so we're actually gonna go into that tool today because there's a ton of applications from it, from. 517 00:48:15,280.001 --> 00:48:22,130.001 Training your team, your sales team, your SDRs to being able to create thought leadership. 518 00:48:22,130.001 --> 00:48:34,750.001 So I created one where I can talk to it and, it's basically a journalist that's interviewing me and so I can really get to the meat of what I wanna talk about and then get an input that I'm pretty happy with. 519 00:48:34,750.001 --> 00:48:39,610.001 That can be then taken by AI and turned into blogs or other types of content. 520 00:48:39,760.001 --> 00:48:45,145.001 Ken, what do you think? What should we create today so here's something I run into often. 521 00:48:46,165.001 --> 00:48:55,235.001 A lot of busy executives and subject matter experts, I'm always trying to track down to do interviews with so I can create content, thought leadership and they just don't have the time. 522 00:48:55,295.001 --> 00:49:02,885.001 And so I would love something that could capture their expertise so that I could lift and leverage it for content. 523 00:49:03,500.001 --> 00:49:03,980.001 Amazing. 524 00:49:04,40.001 --> 00:49:04,580.001 Let's do it. 525 00:49:05,570.001 --> 00:49:09,110.001 And so you can do a couple of different things when you're creating an AI agent. 526 00:49:09,110.001 --> 00:49:26,730.001 So one is, and this is what we're gonna do today, is using a blank template, but it also has options to do a support agent or a sales agent, or even a mindfulness coach, which is super helpful for all the stress that we're under kinda nice to be able to do a guided meditation if you want, but in this instance we're gonna go blank template. 527 00:49:26,820.001 --> 00:49:29,550.001 And so creating an agent here is super simple. 528 00:49:29,760.001 --> 00:49:36,520.001 It would be helpful if I added a name, but we're gonna go with future Craft GTM. 529 00:49:37,865.001 --> 00:49:38,495.001 Expert. 530 00:49:40,325.001 --> 00:49:40,565.001 All right. 531 00:49:41,15.001 --> 00:49:42,245.001 And now we're gonna create the agent. 532 00:49:42,545.001 --> 00:49:43,385.001 Simple as that. 533 00:49:43,655.001 --> 00:49:45,905.001 Now you've got a ton of options. 534 00:49:45,905.001 --> 00:49:50,435.001 So think about creating this agent a bit like when you're creating A GPT. 535 00:49:50,495.001 --> 00:49:57,135.001 So you wanna give it instructions in terms of what you want the what you want the conversation to be like. 536 00:49:57,135.001 --> 00:49:59,715.001 So you're gonna of course have a first message. 537 00:49:59,765.001 --> 00:50:14,85.001 Um, in this case, Ken, what are you thinking? How would you, how would you open this up with an expert You know, I would love it to start with something like, Hey there, tell me about something interesting you're working on right now? All right. 538 00:50:14,110.001 --> 00:50:16,720.001 I'm gonna, I'm gonna take some liberties and say hay crafter. 539 00:50:17,720.001 --> 00:50:20,95.001 Tell me something that you are interested in. 540 00:50:21,410.001 --> 00:50:22,310.001 Right now. 541 00:50:22,380.001 --> 00:50:26,910.001 Every single conversation now that this conversational AI is gonna open with that. 542 00:50:27,525.001 --> 00:50:28,5.001 In mind. 543 00:50:28,485.001 --> 00:50:38,375.001 Now, one of the things that you'll wanna make sure to do is anytime you're changing or if like you do a lot of work in this system prompt, you do wanna make sure to save, it's not gonna auto necessarily save bore you. 544 00:50:38,555.001 --> 00:50:41,375.001 And so it's really important to, save as you go. 545 00:50:41,745.001 --> 00:50:46,965.001 From the system prompt perspective, this is where the real meat of what you want this agent to talk about. 546 00:50:47,175.001 --> 00:50:52,405.001 And so in this instance we're gonna say let's go with a journalist. 547 00:50:52,505.001 --> 00:50:55,825.001 And you can also generate this with ai, which is fun. 548 00:50:55,825.001 --> 00:51:04,75.001 So if you're like, maybe it would be better for 11 labs to write the instructions than me, or even being able to just edit instructions. 549 00:51:04,165.001 --> 00:51:05,755.001 It gives you that option, which is pretty cool. 550 00:51:06,215.001 --> 00:51:08,255.001 And so this, gives you a lot of. 551 00:51:08,515.001 --> 00:51:19,85.001 Information about, what are we gonna be talking to this person about? And then ultimately how do we want the tone? How do we want the goal? Et cetera, et cetera. 552 00:51:19,115.001 --> 00:51:22,715.001 You can very much personalize it to whoever you're expecting to talk to. 553 00:51:22,765.001 --> 00:51:23,155.001 Great. 554 00:51:23,575.001 --> 00:51:32,545.001 Now the goal is really gonna be what's the outcome? So understanding what is it that we're trying to get, this conversational AI to get from the person that we're talking to. 555 00:51:32,545.001 --> 00:51:43,15.001 So if you had to think a little bit about the goals of what you're trying to get from our thought leaders, what do you think? Does this work? Does this align? Is there anything you'd add or change? Yeah. 556 00:51:43,15.001 --> 00:51:44,305.001 You know, I like how it. 557 00:51:44,695.001 --> 00:51:49,225.001 Really states the goal around, Hey, you need to uncover newsworthy details and insights. 558 00:51:49,285.001 --> 00:51:54,725.001 I think there's something that I notice when I'm interviewing people that, I need one more step further. 559 00:51:54,725.001 --> 00:51:55,955.001 I need to understand like, why. 560 00:51:55,955.001 --> 00:52:03,705.001 So if they have a big idea why is it that they think that or why are they experiencing that? So maybe we could ask it to make sure they dig deeper, one level deeper. 561 00:52:04,705.001 --> 00:52:16,105.001 So we're gonna say, you are gonna dig deeper than surface level questions to get to the non-marketing speak, let's say. 562 00:52:16,885.001 --> 00:52:19,690.001 And what else do we wanna say? Anything else for this one? I. 563 00:52:20,630.001 --> 00:52:23,310.001 I think the other thing is if it can ask. 564 00:52:23,730.001 --> 00:52:27,300.001 Follow up questions that might be about related topics. 565 00:52:27,300.001 --> 00:52:34,330.001 So maybe we're talking about ai, but maybe that also leads to a conversation about cloud computing or uh, security. 566 00:52:35,590.001 --> 00:52:35,950.001 Great. 567 00:52:37,0.001 --> 00:52:40,240.001 Now it is gonna provide some guardrails here too. 568 00:52:40,240.001 --> 00:52:47,340.001 So this is super important to make sure that it's, not doing things that you wouldn't want the conversational bot to do. 569 00:52:47,340.001 --> 00:52:50,820.001 And so if you think about the baseline for a lot of these. 570 00:52:51,390.001 --> 00:52:52,170.001 LLMs. 571 00:52:52,170.001 --> 00:52:53,730.001 It's really your helpful assistant. 572 00:52:53,940.001 --> 00:53:04,310.001 So oftentimes the type of responses, whether you get in chat GBT or in Claude or here at 11 Labs, it's gonna try to be helpful, basically to be help you along. 573 00:53:04,460.001 --> 00:53:05,870.001 In this case, we don't really want that. 574 00:53:05,900.001 --> 00:53:11,980.001 We really want the conversational AI to have the expert be the expert. 575 00:53:12,190.001 --> 00:53:15,625.001 And so, so this is really helpful in creating a. 576 00:53:15,675.001 --> 00:53:22,105.001 Guardrails And so, you know, I think one in here, that's really important is don't engage in personal attacks or bias reporting. 577 00:53:22,315.001 --> 00:53:33,935.001 We certainly don't want our experts to be into something that's the Claude example a couple weeks ago where we saw that it was trying to blackmail the the engineer who was coding it. 578 00:53:34,365.001 --> 00:53:40,305.001 In this case, we're enabling this AI agent to have these tools, which is looking at public red records and news articles. 579 00:53:40,455.001 --> 00:53:43,335.001 This way it's really helping to build on what. 580 00:53:43,705.001 --> 00:53:45,85.001 They can talk to this expert about. 581 00:53:45,175.001 --> 00:53:49,705.001 And this is gonna make sure that it's fact checking and using transcription, et cetera, et cetera. 582 00:53:49,915.001 --> 00:53:50,845.001 And so that really helps us. 583 00:53:50,845.001 --> 00:53:54,325.001 I'm gonna save my changes again it's gonna ask me for a dynamic name. 584 00:53:54,325.001 --> 00:54:07,655.001 So in this case we'll what would you like your name, the name to be of this interviewer? It was like something like subject matter expert interviewer? Just keep it really descriptive so the person knows what they're getting. 585 00:54:08,15.001 --> 00:54:13,505.001 Okay, let's make that the description and then we'll make this one the, interviewer. 586 00:54:14,155.001 --> 00:54:18,745.001 You can actually select which of the LLMs you wanna use. 587 00:54:18,745.001 --> 00:54:19,975.001 And there's a lot of options here. 588 00:54:20,185.001 --> 00:54:24,575.001 Now, what they are doing at, at 11 Labs is covering the cost of the LLM today. 589 00:54:24,725.001 --> 00:54:32,735.001 You can see it's like a fraction of a penny for most of these, and I think at some point they'll probably pass those costs along, but it gives you a sense of. 590 00:54:32,930.001 --> 00:54:36,570.001 You know, if I want to use Claude 3.7, 591 00:54:36,600.001 --> 00:54:39,180.001 it's gonna be a little bit more expensive than some of these others. 592 00:54:39,360.001 --> 00:54:45,830.001 So is what I'm getting out of say, Gemini good enough? And I think for this test, we'll assume it is. 593 00:54:45,830.001 --> 00:54:48,260.001 And so let's try a Gemini 2.5 594 00:54:48,260.001 --> 00:54:48,920.001 flash. 595 00:54:49,250.001 --> 00:54:53,130.001 And then this also gives you the opportunity to control the randomness. 596 00:54:53,340.001 --> 00:54:56,600.001 Now the other thing with this,, is you can create a knowledge base. 597 00:54:56,600.001 --> 00:55:02,0.001 And so if you think about, you could be using this to interview your customers. 598 00:55:02,0.001 --> 00:55:05,60.001 You could be, you know, using this to train your SDRs and sales. 599 00:55:05,240.001 --> 00:55:09,410.001 And so you could add things like personas and have it role play a persona for you. 600 00:55:09,500.001 --> 00:55:19,840.001 And so you can add a knowledge base that for it to really use, or you could use even a rag based system where it can access a lot more documents and give you something very tailored to your organization. 601 00:55:20,530.001 --> 00:55:20,860.001 All right. 602 00:55:21,220.001 --> 00:55:26,245.001 Anything else that we wanna include here? I'm really curious what this is gonna look like. 603 00:55:26,245.001 --> 00:55:29,545.001 So I think we should just send it off to, to start building this. 604 00:55:30,115.001 --> 00:55:31,795.001 Alright, well we're gonna test it on you. 605 00:55:31,795.001 --> 00:55:36,445.001 So we're gonna test the agent and you're gonna talk to it, and then we'll see what we come up with. 606 00:55:36,445.001 --> 00:55:38,915.001 How does that sound? that sounds great. 607 00:55:39,225.001 --> 00:55:44,325.001 Typically when I'm building these agents, I want to make sure it's giving me the conversation that I'm really looking for. 608 00:55:44,475.001 --> 00:55:45,345.001 So test it. 609 00:55:45,550.001 --> 00:55:48,750.001 You're ready, then you share the shareable link that is really cool. 610 00:55:48,840.001 --> 00:55:50,340.001 So here's what I really liked about it. 611 00:55:50,340.001 --> 00:55:57,145.001 One, it was very conversational, pun intended, but two it knew enough about the topic that I. 612 00:55:57,405.001 --> 00:56:01,965.001 I didn't feel like I was just talking to a robot, but I was still able to give my own answers. 613 00:56:01,965.001 --> 00:56:03,405.001 I didn't feel like the witness was being led. 614 00:56:03,595.001 --> 00:56:11,775.001 I know that went into a lot of how you programmed and set it up, so I would think receiving this as a subject matter expert would be pretty helpful and friendly. 615 00:56:11,805.001 --> 00:56:13,335.001 'cause I could go do it while I was on a walk. 616 00:56:13,865.001 --> 00:56:17,395.001 Totally and if you go more into the testing, you're gonna refine it. 617 00:56:17,395.001 --> 00:56:18,535.001 So it's a lot more powerful. 618 00:56:18,715.001 --> 00:56:25,645.001 So obviously we just did a really quick example, but you can see how as you iterate, it's gonna be much more valuable to you. 619 00:56:25,995.001 --> 00:56:27,465.001 But thanks for testing it out for me. 620 00:56:28,140.001 --> 00:56:28,800.001 That was really cool. 621 00:56:28,800.001 --> 00:56:29,910.001 Thanks for showing me it. 622 00:56:29,910.001 --> 00:56:31,500.001 And I'm going to go build one right now. 623 00:56:31,770.001 --> 00:56:32,370.001 I love it. 624 00:56:32,850.001 --> 00:56:40,50.001 Alright, Ken, well I guess now we should probably give you that time so you can go create your own conversational AI agent. 625 00:56:40,870.001 --> 00:56:41,560.001 Thank you. 626 00:56:41,560.001 --> 00:56:46,0.001 And for our listeners, thank you so much for listening today or watching. 627 00:56:46,60.001 --> 00:56:55,480.001 And, don't forget to sub subscribe and give us a rating that really helps us get our voice out there to other people who are curious about AI and go to market. 628 00:56:55,820.001 --> 00:57:00,170.001 Thanks for watching and let's keep crafting the future of GTM together. 629 00:57:00,725.001 --> 00:57:01,355.001 Thank you. 630 00:57:01,685.001 --> 00:57:02,195.001 Bye. 631 00:57:03,125.001 --> 00:57:03,345.001 Bye.

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