How AI Is Rewriting Go-To-Market Playbooks

We're back. In the first episode of Season 2 of the FutureCraft Podcast, Ken Roden and Erin Mills dig into how AI is shifting the entire go-to-market motion. Not just content, but sales enablement, market strategy, and execution.

They share how they're using tools like ChatGPT, Gamma, and structured research to build battle cards that actually move deals. They also break down what’s working—and what’s not—when it comes to driving adoption on lean teams.

This episode covers practical ways AI can support speed and clarity without adding complexity. It closes with personal updates and an open call for listener ideas as the show evolves.

 

Unpacking the AI Toolbox

• Digital Focus Groups and AI-Assisted Research: Our hosts take us through an intricate process of leveraging AI-driven focus groups and deep research tools to gain valuable insights about customer behavior and preferences.
• The Power of Synthesis and Presentation Tools: With the help of platforms like Gamma, they illustrate how to transform comprehensive research into polished, actionable battle cards for sales teams, effectively bridging the gap from data gathering to strategic execution.

00:00 Introduction and Disclaimer

00:22 Welcome to Season Two

01:28 Hosts' Personal and Professional Updates

03:58 AI's Rapid Advancements

06:30 Deep Dive into AI Tools and Techniques

09:06 Building a Digital Focus Group

17:34 Leveraging Deep Research for Competitive Intelligence

25:31 Leveraging AI for Content Strategy

26:02 Impact of AI-Driven Traffic on User Engagement

27:20 Credibility and Organic Traffic in AI Models

28:21 Deep Research and Customization in AI

32:50 Creating Consumable Content with AI

34:16 Optimizing Competitive Analysis with AI Tools

40:00 Enhancing Presentation and Design with Gamma

47:49 Integrating AI Tools for Efficient Workflows

51:09 Season 2 Overview and Future Directions

Episode Transcript

(00:00):
.999Hey crafters.
Just a reminder, this podcast is for informational entertainment purposes only and should not be considered advice. 3 00:00:08,330.0000000003 --> 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 future craft podcast. 5 00:00:20,800.001 --> 00:00:21,770.001 Let's get it started. 6 00:00:22,770.001 --> 00:00:23,460.001 Hey there. 7 00:00:23,490.001 --> 00:00:33,480.001 Welcome to the Future Craft Marketing Podcast, and we're back for season two, where we're exploring how AI is changing everything from brand to demand. 8 00:00:33,630.001 --> 00:00:44,490.001 I'm Ken Roden one of your guides on this exciting journey, and I'm Erin Mills, your co-host, and together we're here to unpack the future of AI and go to market. 9 00:00:45,820.001 --> 00:00:46,270.001 Wait. 10 00:00:46,485.001 --> 00:00:46,605.001 I. 11 00:00:46,660.001 --> 00:00:47,390.001 Hold up, Erin. 12 00:00:47,410.001 --> 00:00:52,0.001 Did you just say Go to market? I thought this was a marketing podcast. 13 00:00:52,750.001 --> 00:00:53,290.001 Yeah. 14 00:00:53,290.001 --> 00:00:54,310.001 Good catch, Ken. 15 00:00:54,550.001 --> 00:00:56,650.001 But AI doesn't care about silos. 16 00:00:56,860.001 --> 00:01:01,900.001 It's transforming not just marketing, but sales enablement strategy. 17 00:01:01,900.001 --> 00:01:03,640.001 Basically all things go to market. 18 00:01:03,820.001 --> 00:01:07,240.001 So we're expanding the conversation to cover all of it. 19 00:01:09,10.001 --> 00:01:09,460.001 Okay. 20 00:01:09,460.001 --> 00:01:10,450.001 I love it. 21 00:01:10,690.001 --> 00:01:13,840.001 So we're evolving right alongside AI and our listeners. 22 00:01:13,900.001 --> 00:01:20,830.001 We're still exploring practical insights, testing the latest tech, and talking to the innovators who are shaping the future of go to market in ai. 23 00:01:21,820.001 --> 00:01:22,570.001 That sounds great. 24 00:01:23,560.001 --> 00:01:24,340.001 Let's do it. 25 00:01:25,150.001 --> 00:01:26,170.001 Let's get into it. 26 00:01:26,350.001 --> 00:01:26,860.001 Awesome. 27 00:01:27,280.001 --> 00:01:27,970.001 Hey Erin. 28 00:01:28,70.001 --> 00:01:33,360.001 People might be wondering where have we been? We've been all over the place. 29 00:01:33,720.001 --> 00:01:36,570.001 A lot has happened since last time we talked to you guys. 30 00:01:37,20.001 --> 00:01:43,560.001 Probably as much that has changed with AI has changed with Erin and I, whether it's personally or professionally. 31 00:01:43,810.001 --> 00:01:45,670.001 It's been quiet for a little bit. 32 00:01:45,670.001 --> 00:01:45,730.001 I. 33 00:01:46,510.001 --> 00:01:47,140.001 Yeah, I know. 34 00:01:47,150.001 --> 00:01:51,680.001 After our last episode, I felt like we had a good handle on ai. 35 00:01:51,780.001 --> 00:01:53,70.001 We were semi caught up. 36 00:01:53,70.001 --> 00:01:59,500.001 And and I think now we're in this place of, holy crap, a lot has happened in the last six months. 37 00:01:59,550.001 --> 00:02:07,420.001 Ken, what do you think, what have you been up to the last six months? Probably as, as much that has changed with AI has changed with us. 38 00:02:07,520.001 --> 00:02:17,240.001 So since then I actually started a new job, still focused on the work that I'm passionate about, but more in operations and strategy role, but thinking about how AI is going impact that. 39 00:02:17,450.001 --> 00:02:19,10.001 And I also started my doctorate. 40 00:02:19,230.001 --> 00:02:24,670.001 The same energy and excitement that I have around the work that we do at Future Craft is extending into my research. 41 00:02:24,670.001 --> 00:02:32,650.001 So I'm focused on ai, not surprising, as well as the impact on how it's going to affect leaders, and I think that actually fits well into this. 42 00:02:33,10.001 --> 00:02:41,960.001 Along with that, I've been traveling a lot been talking to people all over the world about AI and what they're experiencing, and did some traveling with you actually as well. 43 00:02:41,960.001 --> 00:02:42,830.001 So I've been busy. 44 00:02:42,830.001 --> 00:02:45,355.001 How about you? What have you been up to? Yeah. 45 00:02:45,355.001 --> 00:02:48,325.001 Similarly, personally I got married, which is super exciting. 46 00:02:48,645.001 --> 00:02:51,705.001 And you were along for that trip? No, we did not marry each other. 47 00:02:53,995.001 --> 00:02:59,305.001 And work-wise still A CMO still pushing the boundaries of ai. 48 00:02:59,305.001 --> 00:03:11,505.001 I think one of the things that got me excited about season two is we're doing a lot with workflows, which was a little bit more, on the edge of our expertise six months ago. 49 00:03:11,585.001 --> 00:03:21,255.001 It's more about how do you make the whole machine work better together, and AI does help to enable that, bottom to top and top to bottom of the funnel. 50 00:03:21,475.001 --> 00:03:23,995.001 So I'm excited to get into the conversation. 51 00:03:24,635.001 --> 00:03:25,25.001 Yeah. 52 00:03:25,25.001 --> 00:03:44,185.001 I think for everyone to know, like typically we bring in a guest, but after some conversations that we've actually had with some practitioners, some listeners we realized that we're just going do a episode focused on tools and tech that we've been implementing and using sharing what we've learned and how they can help you. 53 00:03:44,415.001 --> 00:03:46,725.001 And that's where we're going focus today's episode. 54 00:03:46,725.001 --> 00:03:50,445.001 And then you'll see a regular format moving forward where we'll still bring in. 55 00:03:51,60.001 --> 00:03:57,120.001 Some of the great thinkers and doers around AI right now, but we wanted to share with you some of the cool stuff we're working on. 56 00:03:57,945.001 --> 00:03:58,455.001 Yeah. 57 00:03:58,455.001 --> 00:04:06,725.001 And know, if you think about where AI was a year ago compared to where we are today, we have new models that we've been using a ton of. 58 00:04:06,815.001 --> 00:04:13,305.001 We've got things like Claude Artifacts, shout out to Lisa Adams for inspiring some of the work there. 59 00:04:13,455.001 --> 00:04:18,15.001 We've got sophisticated chatbots that do more voice. 60 00:04:18,15.001 --> 00:04:20,55.001 We've got video Canva's. 61 00:04:20,55.001 --> 00:04:21,915.001 Dream Lab has gotten way better. 62 00:04:22,135.001 --> 00:04:33,65.001 So the overall advancements, it's pretty incredible what we've seen and even some of the tools where we said not so much, we'll talk about one of them today has gotten way, way better as well. 63 00:04:33,65.001 --> 00:04:37,985.001 And so I think that's the other thing that's exciting is historically. 64 00:04:38,795.001 --> 00:04:48,965.001 We had to wait for software to get better and it would be, you put in a product feature request or something along those lines, and maybe a year or two down the road you might get that feature included. 65 00:04:49,355.001 --> 00:04:52,565.001 And we're seeing that happen so much faster now. 66 00:04:53,405.001 --> 00:04:59,85.001 Yeah, I've been surprised you mentioned some of the tools that we've used previously and tried the. 67 00:05:00,225.001 --> 00:05:02,805.001 Pace that they've moved and how effective it is. 68 00:05:02,865.001 --> 00:05:07,665.001 And we've gone from this stage of kind of experimenting with AI and oh, like it might work for this. 69 00:05:07,665.001 --> 00:05:12,315.001 Or how could it look like for this to saying, no, actually, this is how I use AI as part of my job. 70 00:05:12,585.001 --> 00:05:14,745.001 And we're going show you guys some of that today. 71 00:05:15,35.001 --> 00:05:16,565.001 So we're going break it down. 72 00:05:16,565.001 --> 00:05:17,735.001 We're going cover four. 73 00:05:18,515.001 --> 00:05:23,985.001 Topics for tools that we've used and share a little bit about the use case and the impact it's had. 74 00:05:24,285.001 --> 00:05:29,275.001 And we're excited to share it with you We're in season two, episode one. 75 00:05:29,485.001 --> 00:05:36,445.001 Our second part of this episode is going dive into some of the tools we've been using across the go to go-to-Market Motion. 76 00:05:36,635.001 --> 00:05:49,265.001 Ken, there are probably some listeners that are still, just getting their feet wet, maybe just using Chachi, BT, or Gemini to have some conversations, maybe a little content creation and thinking about, use cases and starting there. 77 00:05:49,715.001 --> 00:05:57,660.001 But what are you seeing with folks you were talking to? Yeah, so first of all, wherever you're at right now is still good. 78 00:05:57,710.001 --> 00:06:06,800.001 If you're engaging and learning what makes a good prompt or how to get a good response or output from Gemini, Claude, or chat bt. 79 00:06:07,235.001 --> 00:06:07,805.001 You're good. 80 00:06:07,855.001 --> 00:06:08,905.001 So please keep doing that. 81 00:06:09,205.001 --> 00:06:16,285.001 But I think what we're finding now is again, less of that experimentation and more of like the exploration of like the art of the possible. 82 00:06:16,495.001 --> 00:06:26,65.001 So like maybe people thinking like, what is this big idea I have that AI could do? And then trying to push the functionality so that you can achieve that. 83 00:06:26,65.001 --> 00:06:30,115.001 And you might not be able to get a hundred percent on the way there, but I think you'd be surprised how far you can get. 84 00:06:30,275.001 --> 00:06:39,185.001 And I think that this first use case around building a buyer's group, a digital buyer's group is a great example of pushing things beyond it, like what I thought it could originally do. 85 00:06:40,85.001 --> 00:06:45,455.001 Yeah, I think it's a good point in that it's not just use case by use case, right? Yeah. 86 00:06:45,485.001 --> 00:06:50,205.001 When you were thinking about the buyer's group, it wasn't just I think I might want some feedback. 87 00:06:50,205.001 --> 00:06:52,125.001 How did you come up with it? Yeah. 88 00:06:52,125.001 --> 00:06:55,265.001 So I, it actually came from a real challenge that I was having. 89 00:06:55,565.001 --> 00:07:06,455.001 I was trying to understand a broad breadth of industries and buyers from various industries and trying to have an understanding and be able to talk to that many customers. 90 00:07:06,635.001 --> 00:07:07,715.001 It was just impossible. 91 00:07:07,895.001 --> 00:07:16,295.001 And it would, it was challenging and I felt like it was holding me back from being able to execute on things like messaging or assets or thought leadership topics. 92 00:07:16,535.001 --> 00:07:27,65.001 So what I was wondering was like, is there a way that I could leverage AI to help me answer questions about my customer? And that's where the whole focus group actually came from. 93 00:07:27,65.001 --> 00:07:31,815.001 So it wasn't oh yeah let me create a digital focus group so I don't have to do customer research anymore. 94 00:07:31,875.001 --> 00:07:32,895.001 That's not the thing. 95 00:07:33,75.001 --> 00:07:41,285.001 It's all about that problem that you have in your day-to-day with something that you do in your job and trying to connect it to something that's possible or think of what's possible. 96 00:07:42,515.001 --> 00:07:50,15.001 I think that's what's interesting is it's less about this one unique unique problem and hey, I'm just going solve this pain point. 97 00:07:50,495.001 --> 00:08:02,35.001 And it puts the power of imagination in our hands where, a lot of times, like founders would have these big ideas and then the successful ones would find a way to execute it with a lot of folks. 98 00:08:02,35.001 --> 00:08:09,925.001 And now I feel like with ai we've got this opportunity and this window to say, I want it to look this way. 99 00:08:10,105.001 --> 00:08:15,655.001 And then what are all the things that are possible and I think that's also why we chose the tools today. 100 00:08:15,835.001 --> 00:08:21,225.001 So do you wanna give a little preview of how we're going weave tools into what we're going showcase? Yeah, sure. 101 00:08:21,290.001 --> 00:08:28,790.001 We are going be looking at good old faithful chat, GPT to start, and that's where we built this digital focus group. 102 00:08:29,120.001 --> 00:08:37,550.001 And from there we're going to take that and leverage, I think Erin in my favorite feature functionality right now, which is deep research. 103 00:08:37,800.001 --> 00:08:44,40.001 And then we're going show you how you can create a templated designed battle card from that insight. 104 00:08:44,250.001 --> 00:08:52,920.001 So we're going show you how to use three different tools in one workflow to help you achieve several different outcomes. 105 00:08:53,80.001 --> 00:09:01,960.001 And this was all based on some work we did about exploring the possible for how do you do competitive intelligence and get it out to your sellers and your marketing team in the right function and way. 106 00:09:01,960.001 --> 00:09:03,300.001 So I'm excited to show you this. 107 00:09:03,300.001 --> 00:09:05,820.001 Do you mind if I dive in? Let's do it. 108 00:09:05,880.001 --> 00:09:06,180.001 Great. 109 00:09:06,600.001 --> 00:09:14,650.001 So for those of you watching you're going be able to see how I demo this and I'll work, do my best to talk you all through what we're doing here. 110 00:09:14,760.001 --> 00:09:17,950.001 I'm going show you first is this custom GPT. 111 00:09:18,340.001 --> 00:09:26,370.001 So this is something I made using just standard functionality within the chat GPT. 112 00:09:27,465.001 --> 00:09:36,165.001 Feature set and I built a buyer's group for a test company that's in the recruiting software space. 113 00:09:36,415.001 --> 00:09:44,965.001 Those people who would buy that software would be like heads of recruiting, VPs of talent acquisitions, chief human resources officers. 114 00:09:45,145.001 --> 00:09:52,225.001 But then there's also probably buyers that are like CFO or COO and in some cases maybe A-C-C-E-O. 115 00:09:52,615.001 --> 00:09:54,325.001 But I created this focus group. 116 00:09:54,415.001 --> 00:09:57,265.001 It was pretty easy from the backend to do. 117 00:09:57,625.001 --> 00:09:58,495.001 You put in. 118 00:09:58,930.001 --> 00:10:02,740.001 Your name, and then you can create a description for people to access it. 119 00:10:03,10.001 --> 00:10:05,890.001 Keep in mind this is something that's shareable to your team. 120 00:10:05,890.001 --> 00:10:12,820.001 So after you build it, you can make it accessible for whoever you want by creating a custom link and make it just for your group. 121 00:10:13,450.001 --> 00:10:26,80.001 And then what I did was actually just gave it some pretty clear instructions and we'll go into the instructions in a little bit, but just, it's very similar to a prompt trying to set up what the output to look like, what you're expecting, and then some considerations. 122 00:10:26,380.001 --> 00:10:29,20.001 And then the other cool thing is you can do these conversation starters. 123 00:10:29,110.001 --> 00:10:39,945.001 And what's cool about that is when you are sharing this with other users, maybe it's your product marketing team made this, but you shared it with your content marketing team, or you shared it with your sales team or your BDRs. 124 00:10:40,195.001 --> 00:10:42,835.001 They get some ideas of some questions they can go ahead and ask. 125 00:10:43,75.001 --> 00:10:45,355.001 Let's start with some of the pre-populated questions. 126 00:10:45,535.001 --> 00:10:59,455.001 What this great one is first one is what's the latest trend impacting buyers of recruiting software this year compared to last year? Why someone would find value in that is, could be some interesting topics for a BDR to use in part of their outreach sequence. 127 00:10:59,455.001 --> 00:11:02,695.001 Or it could be some great content ideas for content marketing team. 128 00:11:02,745.001 --> 00:11:07,935.001 So you can see here the output based on this question will tell me like how it's been set up. 129 00:11:07,935.001 --> 00:11:18,855.001 So I have a panel audience, so it's my advisory board or my focus group, and then these different types of buyers, different sizes of organizations giving me feedback on what these tools are. 130 00:11:19,65.001 --> 00:11:21,885.001 And it produces it in a well organized setup. 131 00:11:22,255.001 --> 00:11:26,95.001 So I might ask something though that's a little bit less canned, I might say. 132 00:11:26,365.001 --> 00:11:26,725.001 What. 133 00:11:27,310.001 --> 00:11:37,430.001 Is your impression of the main recruiting software tools? And I'll just list a few out that I know. 134 00:11:37,670.001 --> 00:11:41,710.001 Lever, iCIMS and then I would say Greenhouse. 135 00:11:42,235.001 --> 00:11:42,655.001 Greenhouse. 136 00:11:42,655.001 --> 00:11:43,615.001 That's a great one. 137 00:11:44,85.001 --> 00:11:47,475.001 And I'll put in Workday 'cause I think they have a recruiting tool too. 138 00:11:48,165.001 --> 00:11:55,995.001 And I'll say let's focus on B2B SaaS as the industry. 139 00:11:56,695.001 --> 00:12:01,675.001 And I'm looking for executive level buyers. 140 00:12:02,440.001 --> 00:12:05,490.001 So I like what you're doing here with giving it a lot more instruction. 141 00:12:05,700.001 --> 00:12:15,100.001 I think a lot of times when people get frustrated with ChatGPT or any of these other tools, it's this very broad general prompt that they're putting in. 142 00:12:15,250.001 --> 00:12:21,310.001 And so then they get something that's like general coming out and ultimately not satisfying the requirement of what they're looking for. 143 00:12:21,520.001 --> 00:12:28,90.001 And so I think this helps in that you're narrowing down what the recruiting software tools are that you want it to evaluate. 144 00:12:28,480.001 --> 00:12:45,870.001 Yeah, that's a good point because for example, if you were, a marketing ops person or you in demand gen and you uploaded a list of contacts and you only had spotty data in it and you weren't confident in what you were putting in, what are you going get? Put the, what's going be the output of it? Something not quality. 145 00:12:45,870.001 --> 00:12:53,430.001 And so I think that's a good mindset to have, Erin, of the effort and time you put into something is probably going represent the quality you get out. 146 00:12:53,610.001 --> 00:12:58,950.001 The nice thing is we've created some templates that you guys can use so you don't have to go through the building process. 147 00:12:58,950.001 --> 00:13:01,500.001 'cause we figured some of it out with you and we wanna share it with you all. 148 00:13:03,570.001 --> 00:13:16,410.001 Going back to this question, you can see the industry focus that we mentioned, and then I got a new group of stakeholders to pull from and I'm able to have a round table discussion, which is going give me an idea of these trends. 149 00:13:16,500.001 --> 00:13:24,400.001 And you're hearing about what people's perspective are on these on these specific companies and what their strengths are. 150 00:13:24,670.001 --> 00:13:32,450.001 And what's interesting about this is, you might know what your company's strengths are because you spend time doing your competitive differentiation. 151 00:13:32,810.001 --> 00:13:33,560.001 But I think what's. 152 00:13:33,965.001 --> 00:13:40,525.001 Particularly unique about these outputs is it might look at your competitor's webpage to see what they say their strengths are. 153 00:13:40,715.001 --> 00:13:49,715.001 It can look at lever's website, it can look like at reviews of Lever, it can look at Reddit comments about Lever, which we'll see in the deep research. 154 00:13:49,715.001 --> 00:13:57,395.001 So you're getting an idea of what the market thinks of the company, not just what on their website, which is going change how you think about doing competitive. 155 00:13:57,575.001 --> 00:14:00,425.001 So these are the pieces that can help you with this next step. 156 00:14:02,195.001 --> 00:14:13,695.001 The last question I think that's good to ask in something like this is why might not why might you not purchase Lever? 'cause that's who we're focusing on. 157 00:14:14,25.001 --> 00:14:20,130.001 What would change your mind? I think that's key is the what would change your mind. 158 00:14:20,460.001 --> 00:14:20,520.001 Yeah. 159 00:14:20,520.001 --> 00:14:46,760.001 And I do think one thing we learned from Nicole Effer last year is being able to give the GPTA little bit more of the frame of reference of Hey, pretend you are this, or and in this instance, it's giving that perspective of the buyer as opposed to just, generally asking a window of Chachi bt of Hey, what do you think about Lever? And would you change your mind? This is going be a lot more specific to the panel that you set up, which is cool. 160 00:14:47,120.001 --> 00:14:50,660.001 Yeah, and I'll tell you, so this specific custom. 161 00:14:51,65.001 --> 00:14:52,205.001 GPT format. 162 00:14:52,235.001 --> 00:15:01,565.001 We have run in, I think five different industries that are span outside of tech in tech manufacturing was one of them. 163 00:15:01,895.001 --> 00:15:07,5.001 And gotten feedback from the users of those that this is pretty accurate. 164 00:15:07,35.001 --> 00:15:15,575.001 For example, a manufacturing advisory board that I built someone who's a chief technology officer at a manufacturing company. 165 00:15:15,575.001 --> 00:15:17,735.001 I said, Hey, this is the output of what we're getting. 166 00:15:17,785.001 --> 00:15:19,975.001 How accurate is it? This is to what you would think. 167 00:15:19,975.001 --> 00:15:22,675.001 And he goes, it's about 80, 85% there. 168 00:15:22,855.001 --> 00:15:33,985.001 And I was like, that's pretty good, right? Because if you don't have that knowledge or your BDRs don't have that knowledge, 85% is a lot more than they could have maybe been able to uncover just on their own. 169 00:15:33,985.001 --> 00:15:37,375.001 So it's not going get you to the perfect answer. 170 00:15:37,465.001 --> 00:15:41,195.001 And I think this concept of a no prize is what we'll call it. 171 00:15:41,195.001 --> 00:15:51,155.001 So I'm a big comic book nerd, and in Marvel comics, if you find in a comic a contradiction to the story, you can write them a letter and they will mail you an empty envelope. 172 00:15:51,155.001 --> 00:16:01,815.001 And it's called a no prize because they're like, great, you found this flaw, but what are you going get from it? And I think that goes along with a little bit of people who are like, chat, GPT can't do this, or Claude can't do that. 173 00:16:01,815.001 --> 00:16:04,965.001 And I'm like, cool, but like it can do all these other things. 174 00:16:04,965.001 --> 00:16:08,295.001 So here's your no prize, but use what it can do. 175 00:16:09,300.001 --> 00:16:10,590.001 And I think it's also the speed. 176 00:16:10,920.001 --> 00:16:11,10.001 Yes. 177 00:16:11,10.001 --> 00:16:25,280.001 You think about a lot of teams now are worried about ai one, taking their jobs, but two, that it's just like more is more and it's, taking away from some of the core things that they enjoy doing or, it's just like overwhelming. 178 00:16:25,280.001 --> 00:16:26,155.001 There's so much to do. 179 00:16:26,390.001 --> 00:16:34,840.001 And I guess my response a little bit with that is, even in this instance, if it can get you 80% of the way there and what how long have we spent doing this? Five minutes. 180 00:16:35,260.001 --> 00:16:35,530.001 Exactly. 181 00:16:35,690.001 --> 00:16:36,730.001 That's a great start. 182 00:16:36,730.001 --> 00:16:51,240.001 As opposed to pre gen AI where it was, you were going through the G two reviews or you were looking at all your competitor websites, and you should still do that just to, keep yourself informed, but you can get a much deeper and faster level of analysis. 183 00:16:51,875.001 --> 00:16:58,115.001 Using these tools and then be able to leverage those for your other work, which I think is the exciting part. 184 00:16:58,115.001 --> 00:17:05,285.001 So it's a lot of the legwork that, you spent, hours and hours chasing down and if you're 80% there, not bad. 185 00:17:05,335.001 --> 00:17:19,905.001 You're still going need humans to go in and see what's accurate and not, and certainly you can compare this to some of the interviews that you may do and you should still do customer interviews and, and get that, that human to human data point. 186 00:17:20,55.001 --> 00:17:22,545.001 But 80% not bad. 187 00:17:23,205.001 --> 00:17:23,655.001 Yeah. 188 00:17:23,805.001 --> 00:17:24,285.001 Okay. 189 00:17:24,375.001 --> 00:17:25,755.001 I could do this all day. 190 00:17:25,935.001 --> 00:17:26,175.001 Yeah. 191 00:17:27,525.001 --> 00:17:27,795.001 Okay. 192 00:17:27,795.001 --> 00:17:44,215.001 So now we've talked to our digital focus group, and now Erin, what are we going do after this step? Yeah, so this is also one of the things I find most fascinating and most exciting with gen AI, is we can take the work that Ken does and I can leverage it. 193 00:17:44,335.001 --> 00:17:52,45.001 And so I think that's what's exciting especially when we get going, it's like you have this idea and then you can build on it and pass it back and forth and make something even better. 194 00:17:52,45.001 --> 00:17:58,405.001 So I'm going take the outputs from the GPT that we just showed you, and then we're going go in and show one of our. 195 00:17:59,20.001 --> 00:18:09,180.001 Favorite tools within ChatGPT, which is deep research, and I have found it to be just such a time saver for research and you'll see why in just a second. 196 00:18:09,480.001 --> 00:18:20,770.001 So I'm going go ahead and share my screen, all right, so here we have Ken's research that he did with the GPT and what I'm going show you first is this is a super. 197 00:18:21,235.001 --> 00:18:23,515.001 Simple GPT that I found. 198 00:18:23,765.001 --> 00:18:26,15.001 This one is actually prompt like Ethan Molik. 199 00:18:26,15.001 --> 00:18:33,765.001 So what's cool here is those of you that follow Ethan Molik, he is such a, thought leader in the space and has interesting ideas. 200 00:18:34,135.001 --> 00:18:36,835.001 One of the things that people struggle with is prompting. 201 00:18:37,15.001 --> 00:18:44,895.001 And so this one is mirroring some of the guidance that he's given over the years and being able to help you create a meaningful prompt. 202 00:18:44,895.001 --> 00:18:48,15.001 So we're going start with this and then we're going get into the deep research. 203 00:18:48,75.001 --> 00:18:49,275.001 You ready, Ken? Yeah. 204 00:18:49,275.001 --> 00:18:51,465.001 I'm so excited for this part. 205 00:18:51,885.001 --> 00:18:53,835.001 Okay, so here we go. 206 00:18:53,835.001 --> 00:19:06,435.001 So I'm going say, can you help me build a prompt for deep research? Now you can skip this step if you want, and you can go right to deep research if you want. 207 00:19:06,715.001 --> 00:19:09,895.001 I like this 'cause it just helps me frame the prompt a little bit better. 208 00:19:09,895.001 --> 00:19:15,415.001 And you can use this tool or this GPT for any prompt, just outside of deep research as well. 209 00:19:15,715.001 --> 00:19:21,595.001 I'm going to include all this so I can copy and paste it. 210 00:19:21,595.001 --> 00:19:25,345.001 I can also go in and add it from my Google Drive. 211 00:19:25,495.001 --> 00:19:30,15.001 And so there's a couple ways, you can connect your Google Drive, that is often an easier route. 212 00:19:30,285.001 --> 00:19:35,535.001 You can upload it from your computer or sometimes if you just wanna paste, you can also just paste. 213 00:19:35,955.001 --> 00:19:55,995.001 And so I'm going include all this information that Ken was able to glean from his GPT and then be able to then say what else do I want here? I wanna I want to create in depth battle cards based on this. 214 00:19:56,0.001 --> 00:20:04,345.001 And then what else do you think we, we were interested in? Ken? I think one thing that we've learned is adding who maybe the consumer of those battle cards would be. 215 00:20:04,345.001 --> 00:20:09,715.001 So it would be maybe BDRs account executives and maybe marketers. 216 00:20:10,285.001 --> 00:20:10,675.001 Yeah. 217 00:20:11,675.001 --> 00:20:18,315.001 And, we're also talking about go-to market motion, so I think we may also wanna include some go-to market strategy pieces. 218 00:20:18,315.001 --> 00:20:22,545.001 I'm going, I'm going push the boundaries, usually I say let's just stick to one, one thing. 219 00:20:22,545.001 --> 00:20:24,45.001 But deep research, pretty neat. 220 00:20:24,75.001 --> 00:20:25,425.001 So let's let's push it a little bit. 221 00:20:25,425.001 --> 00:20:25,605.001 I love it. 222 00:20:25,905.001 --> 00:20:38,415.001 Should we include for a comprehensive so this is just going help me build the prompt to then take that, put it into the deep research. 223 00:20:38,415.001 --> 00:20:40,905.001 And so what's cool here is you're going see it's. 224 00:20:41,205.001 --> 00:20:42,435.001 Asking me a few things. 225 00:20:42,555.001 --> 00:20:45,345.001 So it's like wanting to know what my primary goals are. 226 00:20:45,375.001 --> 00:20:47,55.001 'cause I was a little bit all over the place. 227 00:20:47,445.001 --> 00:20:56,675.001 And and then it's going just confirming like what am I most interested in? So let's start and give it those answers, and then ultimately it'll give me a prompt that I'm going use into deep research. 228 00:20:56,675.001 --> 00:21:01,815.001 So first off the battle cards are going be for, let's go all the above. 229 00:21:02,325.001 --> 00:21:02,595.001 Yeah. 230 00:21:03,405.001 --> 00:21:07,815.001 Why not? That's, that's how battle cards typically get used, right? Yeah, for sure. 231 00:21:08,25.001 --> 00:21:13,65.001 And what do we care most about? For the use cases? I think I'm thinking objection handling is always helpful. 232 00:21:13,70.001 --> 00:21:30,390.001 Messaging and positioning I think maybe understand like the strengths of the current company compared to the competitors, right where we stand out so that the seller can have a quick hits on what to highlight when they're going against the this competitor. 233 00:21:31,230.001 --> 00:21:31,530.001 Great. 234 00:21:32,200.001 --> 00:21:45,850.001 And let's also just say this is from the perspective of Lever, right? We wanna tell it what company we're actually going be building these for. 235 00:21:46,190.001 --> 00:21:57,510.001 And then let's go with a comprehensive, And let's say the tone. 236 00:21:57,590.001 --> 00:21:58,160.001 I don't know. 237 00:21:58,520.001 --> 00:21:59,150.001 You decide. 238 00:22:00,200.001 --> 00:22:00,560.001 Yeah. 239 00:22:00,890.001 --> 00:22:01,970.001 Educational. 240 00:22:02,0.001 --> 00:22:02,30.001 Okay. 241 00:22:03,710.001 --> 00:22:05,240.001 I was going have the GPT decide. 242 00:22:05,930.001 --> 00:22:06,80.001 Oh, okay. 243 00:22:06,80.001 --> 00:22:07,10.001 Yeah, do that instead. 244 00:22:07,310.001 --> 00:22:07,730.001 All right. 245 00:22:07,730.001 --> 00:22:08,390.001 All right, here we go. 246 00:22:09,390.001 --> 00:22:09,630.001 Okay. 247 00:22:10,440.001 --> 00:22:15,890.001 So we're going get this prompt and then we're going take it into deep research. 248 00:22:15,950.001 --> 00:22:20,100.001 And now one of the things that is a little bit interesting with deep research is it does take a few minutes. 249 00:22:20,100.001 --> 00:22:28,510.001 And so you'll see we'll pause for a little bit to let it run the research, but then ultimately we'll have something pretty cool to show. 250 00:22:29,50.001 --> 00:22:32,640.001 And it's also giving me feedback on this this prompt, which is pretty cool. 251 00:22:32,690.001 --> 00:22:35,940.001 'Cause it gives me a way to think about prompting for the future. 252 00:22:35,940.001 --> 00:22:41,520.001 So we're just going go ahead and say, and it's also saying, okay, great. 253 00:22:41,580.001 --> 00:22:43,470.001 Including the input variable. 254 00:22:43,710.001 --> 00:22:48,510.001 We are going go ahead and copy this just to make it easy. 255 00:22:49,80.001 --> 00:22:52,530.001 And then we're going start a new chat. 256 00:22:52,790.001 --> 00:22:56,0.001 So what model, I guess this is another thing we should talk about. 257 00:22:56,150.001 --> 00:23:01,230.001 There's been a ton of new models that have come out since the last time we did this podcast. 258 00:23:01,470.001 --> 00:23:10,200.001 What are you thinking for your best practices? Where, what models are you using? So for this use case, I think maybe us going into 4.5 259 00:23:10,200.001 --> 00:23:21,190.001 is, is the right choice, right? Because you're not going be doing the pro model, which takes a lot of time to run through scenarios, but you're going be able to get some of that comprehensive thinking. 260 00:23:21,190.001 --> 00:23:22,570.001 I love it. 261 00:23:22,780.001 --> 00:23:28,30.001 And I do think I actually just did a training on all these different models and when to use them for different use cases. 262 00:23:28,510.001 --> 00:23:29,680.001 You should always experiment. 263 00:23:29,710.001 --> 00:23:31,960.001 If you're not getting what you want out of 4.5, 264 00:23:32,290.001 --> 00:23:33,100.001 try something else. 265 00:23:33,380.001 --> 00:23:37,790.001 So you're going paste your your prompt here and then you're going hit deep research. 266 00:23:37,890.001 --> 00:23:40,670.001 And this is going tell the GPT to, Hey, I want. 267 00:23:41,180.001 --> 00:23:42,470.001 Something more. 268 00:23:42,750.001 --> 00:23:52,140.001 And what's I think part of the magic here is that it helps you to understand well, are you confirming what competitor you like? So let's do all three. 269 00:23:53,140.001 --> 00:23:57,940.001 The other thing here that's pretty cool is you can actually tell it what a credible source is. 270 00:23:58,30.001 --> 00:24:02,420.001 If you're finding that what you come up, up with is not what you were looking for. 271 00:24:02,420.001 --> 00:24:06,580.001 So this is going be it's going tell you what it's going do and it's going start the research. 272 00:24:06,650.001 --> 00:24:12,510.001 As it starts to do the research, you can ultimately then see what it's researching, yeah. 273 00:24:13,125.001 --> 00:24:33,85.001 So I think one of the points that we've alluded to throughout this conversation today is, using AI more as a thought partner and rather than just, oh, I'm going ask it this question so I can use whatever the output is, but having kind of an ongoing conversation in collaboration with the prompt. 274 00:24:33,325.001 --> 00:24:53,75.001 And I just wanna share the Ethan Molik, who you used that prompt inspiration from he just did a study at Proctor and Gamble where they looked at teams of professionals who are randomly assigned to use AI as well as having a controlled group of two who didn't, and people. 275 00:24:53,420.001 --> 00:24:58,840.001 Actually were more productive because they used AI to expand their own knowledge. 276 00:24:58,840.001 --> 00:25:06,700.001 So the more r and d product centric groups were more likely to be more commercial thinking, I think about the business and vice versa. 277 00:25:06,700.001 --> 00:25:11,20.001 So think of, this is a great example, Erin, that you're showing of being a true thought partner. 278 00:25:12,310.001 --> 00:25:14,230.001 Yeah, I think that's a good point. 279 00:25:14,230.001 --> 00:25:17,580.001 You can actually just click on where it's, starting the research. 280 00:25:17,630.001 --> 00:25:23,840.001 Sometimes it'll automatically pop open for you, but it's going show you how it's thinking through this problem that it's trying to solve for you. 281 00:25:24,80.001 --> 00:25:27,740.001 And I think the other neat thing is you could see the activity and how it's thinking. 282 00:25:27,890.001 --> 00:25:31,370.001 You can also click to see where is it getting some of its sources from. 283 00:25:31,640.001 --> 00:25:37,400.001 And a lot of times you'll see 20, 30 sources or more depending on the problem that you're trying to solve for. 284 00:25:37,400.001 --> 00:25:41,790.001 But I think this is also good for content creators out there. 285 00:25:41,790.001 --> 00:25:54,485.001 Or just when you're thinking through how do I even get into some of these AI models? It's looking at things like you mentioned Reddit, a lot of software reviews, it'll look at G two, it's going look at of course, the company websites. 286 00:25:54,485.001 --> 00:26:02,465.001 But as you think about your content strategy, these are areas that you might wanna focus in if you wanna be referenced in AI tools. 287 00:26:02,675.001 --> 00:26:16,635.001 I think the other thing that we're finding at my day job that's super interesting is that when we get traffic from these models, we actually see that those users are spending like 700% more time on our website. 288 00:26:16,635.001 --> 00:26:26,805.001 We're seeing them engage with us because they are seeing that this is a credible source and then they're, the buyers are much more likely to consume more content, which is fascinating to me. 289 00:26:27,315.001 --> 00:26:28,305.001 Wait, okay. 290 00:26:28,905.001 --> 00:26:30,435.001 I feel like that's important. 291 00:26:30,975.001 --> 00:26:37,575.001 If I heard you right, you said that when someone using, an LLM. 292 00:26:37,575.001 --> 00:26:45,15.001 So chat, PT Claude, and they are searching for information on a vendor and your vendor name pops up as a source. 293 00:26:45,15.001 --> 00:26:47,865.001 They click on your link, they come to your webpage. 294 00:26:48,15.001 --> 00:26:56,625.001 They're spending more time than those who just came to your site, maybe through Google or maybe an another advertisement or something. 295 00:26:57,105.001 --> 00:26:58,125.001 Yeah, exactly. 296 00:26:58,125.001 --> 00:27:01,695.001 And I think it's not even just they're searching for a vendor when they're looking for thought leadership. 297 00:27:01,745.001 --> 00:27:04,25.001 I wanna understand best practices about something. 298 00:27:04,25.001 --> 00:27:10,75.001 And then ultimately your company comes up that it's much more reliable as a source. 299 00:27:10,75.001 --> 00:27:15,835.001 And so I think that is also then driving the buyers to get a better understanding of what your offering is. 300 00:27:16,25.001 --> 00:27:20,505.001 Just incredible the amount of time spent and the conversions that we're getting as well. 301 00:27:20,505.001 --> 00:27:30,925.001 And you're looking at this is traffic that you're not having to, do paid search for or paid social, this is all organic, but making sure that you're in the right places that the AI is. 302 00:27:31,255.001 --> 00:27:33,865.001 Viewing what you're doing is being credible. 303 00:27:34,75.001 --> 00:27:39,775.001 And that's also a, a interesting angle because I think of B2B marketers, a lot of times we would think like Reddit. 304 00:27:40,15.001 --> 00:27:44,875.001 I don't know if that's super credible, but a lot of these AI models do look at Reddit. 305 00:27:44,875.001 --> 00:27:52,265.001 And so maybe it's worth, looking at other channels to see what is, the AI model that you're, trying to get traffic from referencing. 306 00:27:52,265.001 --> 00:27:59,505.001 And so it's interesting to see, here's G two popping up as another example, because it also looks at that as being more credible. 307 00:28:00,255.001 --> 00:28:00,615.001 Yeah. 308 00:28:00,705.001 --> 00:28:03,525.001 Looks like 17 sources in just a few minutes. 309 00:28:03,715.001 --> 00:28:04,75.001 Yeah. 310 00:28:04,255.001 --> 00:28:09,595.001 How long would that have taken a person to do? Oh, I think this could be a project onto itself. 311 00:28:09,695.001 --> 00:28:11,225.001 Just all this research. 312 00:28:11,405.001 --> 00:28:16,805.001 And I think the other thing that's cool is it's ideas that maybe you wouldn't necessarily think of. 313 00:28:16,875.001 --> 00:28:18,285.001 Smb guide.com. 314 00:28:18,360.001 --> 00:28:21,870.001 I'm not sure that everybody would go to that to find information. 315 00:28:21,930.001 --> 00:28:25,710.001 Now, the other thing that's cool with deep research is you can also guide it. 316 00:28:25,710.001 --> 00:28:37,50.001 If you think that, Hey, you know what, I don't think that G two is super relevant for this exercise, or, I don't think SMB Guide is going be the one that I'm most interested in basing my research on. 317 00:28:37,815.001 --> 00:28:45,415.001 Tell it, when you're in the initial prompt, you can give it those instructions of what do you view as being the most credible. 318 00:28:45,535.001 --> 00:29:00,825.001 You could tell it to look for analysts more specifically, or influencers or different types of thought leaders or, Hey, I wanna mirror this thought leader and what they're saying, or find lookalike thought leaders and then it will find those as being your research points. 319 00:29:00,825.001 --> 00:29:02,595.001 And so you can be very specific. 320 00:29:02,595.001 --> 00:29:16,65.001 Obviously in this example, we're doing a little, something a little broader just because we wanted to be able to illustrate how deep research works, but you can get cool data and very targeted and very specific to what you're trying to solve for. 321 00:29:16,575.001 --> 00:29:16,845.001 Yeah. 322 00:29:16,845.001 --> 00:29:23,185.001 Erin, I just wanna call out like I'm seeing things like I'm pulling from Reddit snippets, tech blogs, G two reviews. 323 00:29:23,515.001 --> 00:29:27,955.001 Then it's telling me that the G two reviews might provide more insights than we would've had before. 324 00:29:28,75.001 --> 00:29:32,545.001 This is thinking, this is logical thinking and thinking about things that we didn't put into the prompt. 325 00:29:33,265.001 --> 00:29:33,535.001 Yeah. 326 00:29:33,535.001 --> 00:29:41,725.001 I think the reasoning is what's interesting in this, and again, this is coming back to that what model should I use? And you can try this with different models. 327 00:29:41,815.001 --> 00:29:43,345.001 And that's also the cool thing too. 328 00:29:43,345.001 --> 00:29:56,255.001 So it's do you want advanced reasoning? Do you want a surface scrape? And so now what it's also doing is it's giving me the next sort of steps it's thinking through, right? I'm crafting a markdown document to compare Lever, greenhouse, iSense, and Workday. 329 00:29:56,435.001 --> 00:29:59,375.001 And that is pretty cool, right? Because it's yes, I'm waiting. 330 00:29:59,595.001 --> 00:30:00,915.001 I could also be doing other work. 331 00:30:00,915.001 --> 00:30:03,95.001 I don't need to be in this window the whole time. 332 00:30:03,95.001 --> 00:30:06,595.001 I think some people get a little confused with that, but and it'll, alert me when it's done. 333 00:30:06,625.001 --> 00:30:23,870.001 But I love seeing, what is it that it's thinking through, and then how does it get the output that I'm actually looking for? So in this, in this instance it's weaknesses, versus strengths and, and looking through creating meaningful battle cards that are going help our sellers. 334 00:30:24,530.001 --> 00:30:30,710.001 Or our marketers or BDRs or our CSMs understand where to punch and block. 335 00:30:31,40.001 --> 00:30:39,280.001 And even going through, simplified jobs is interesting because it's going be like an a TS is going be pulling in from that type of job board. 336 00:30:39,280.001 --> 00:30:41,140.001 And so you may not even think about that. 337 00:30:41,140.001 --> 00:30:50,620.001 What are the ratings and what are the other things that people are saying on these other types of job boards that might impact? Now again, this is just an a TS example. 338 00:30:50,620.001 --> 00:30:52,180.001 You can use this for any industry. 339 00:30:52,540.001 --> 00:31:10,230.001 And I think that is what is like remarkable, right? So if you get a new employee and they're not familiar with your industry, it used to be, oh gosh, it's going take six months or however long for somebody to get up to speed and know the ins and outs of an industry, this can help them to get up to speed faster. 340 00:31:10,230.001 --> 00:31:11,700.001 So if you think about onboarding, I. 341 00:31:12,105.001 --> 00:31:19,335.001 Great tool here to be able to help them identify different sources that they should be considering and then just generally learn about the market. 342 00:31:21,575.001 --> 00:31:40,885.001 That's a interesting point then, when you start thinking about hiring what talent you need, maybe industry specificity isn't as critical as it has been in the past, especially as buyers have been expecting more subject matter expertise from vendors, while some of this gap can be closed through using deep research. 343 00:31:41,455.001 --> 00:31:42,655.001 Yeah, totally. 344 00:31:42,655.001 --> 00:31:59,405.001 And coming back to the 80% thing, Ken, because I think that's also a meaningful metric to think about is, if you think about, chat, GBT and sort of a, even some of the GPTs out there, it's like a high schooler who's very generalist and not necessarily versed in the ways of the world, if you will. 345 00:31:59,795.001 --> 00:32:03,755.001 What deep research is going do is give you more of that PhD level. 346 00:32:03,755.001 --> 00:32:16,85.001 So almost like a, you can, although, obviously not as good, but it's going give you a much greater level of sophistication than what you're seeing at just putting this prompt in a sort of regular window. 347 00:32:16,355.001 --> 00:32:22,195.001 Yeah, and I think there's also a little bit today said are there things that are going be not relevant? Yeah. 348 00:32:22,375.001 --> 00:32:33,505.001 But I also think some of the research that we do as people, you're like should I include this? Should I not, TBD, but there's certainly things where, you know, the different images that it's looking at. 349 00:32:33,635.001 --> 00:32:36,795.001 Is that going be relevant to our battle card? It may or may not. 350 00:32:36,855.001 --> 00:32:40,575.001 Probably not, but it's going give us an output that's a little more summarized. 351 00:32:40,575.001 --> 00:32:41,955.001 We're going continue to let this run. 352 00:32:42,495.001 --> 00:32:44,325.001 We were up to 40 sources now. 353 00:32:44,705.001 --> 00:32:46,400.001 And it's going run for a few more minutes. 354 00:32:46,460.001 --> 00:32:51,960.001 So we're going take a quick break and we'll come back with the results​ And we're back and hydrated. 355 00:32:54,630.001 --> 00:33:02,40.001 So one of the things I think that is also so remarkable about these tools is you can walk away and come back and have something meaningful and cool. 356 00:33:02,40.001 --> 00:33:07,310.001 In this example we've got where the deep research looked at 42 sources. 357 00:33:07,400.001 --> 00:33:09,140.001 Pretty incredible for, I don't know. 358 00:33:09,920.001 --> 00:33:14,510.001 Let's see how many minutes? 11 minutes actually will tell you how long and how many sources you have here. 359 00:33:14,600.001 --> 00:33:17,360.001 And this is actually what it came up with, which is pretty cool. 360 00:33:17,360.001 --> 00:33:18,980.001 It's going give us an overview. 361 00:33:18,980.001 --> 00:33:23,330.001 It gives us value prop key objections, which is something that we asked for. 362 00:33:23,600.001 --> 00:33:33,20.001 And I think the other thing that's fun here is it, and it's a little different than some of the other GPTs of earlier where you don't know where the source is. 363 00:33:33,230.001 --> 00:33:44,460.001 What's cool with deep research is I can go right to the source, like, where did you find this? So it does seem to reduce some of the hallucinations that you might've seen in, a year ago when we were experimenting. 364 00:33:44,460.001 --> 00:33:54,190.001 And so I think this is very cool in the sense of, hey, here are the traps and it's going give you those clear examples and then it's going give you the proof points and so on and so forth. 365 00:33:54,190.001 --> 00:33:59,560.001 But, we have probably, I don't know, 25 pages of of content here. 366 00:33:59,560.001 --> 00:34:08,735.001 How long do you think this would've taken you to write, Ken? Man, back in my compete days, this would've taken me weeks to do. 367 00:34:09,105.001 --> 00:34:14,195.001 And honestly during that time, probably frustrating and disappointing a lot of my stakeholders. 368 00:34:15,725.001 --> 00:34:16,415.001 I hear that. 369 00:34:16,775.001 --> 00:34:22,825.001 Alright next I think what we're going do is go ahead and take this into Gamma and show you guys how to make something consumable. 370 00:34:22,825.001 --> 00:34:26,205.001 Now, you can share this with your teams however you'd like. 371 00:34:26,205.001 --> 00:34:29,705.001 So you can create a Google doc, you can, put this in something else. 372 00:34:30,25.001 --> 00:34:38,110.001 But I think what we wanna do is showcase how do you make it super consumable? A lot of the folks on the various teams, like they don't wanna read a 20 page document. 373 00:34:38,130.001 --> 00:34:44,15.001 Oh, who does? So we're going make it super, super simple, but as comprehensive as possible. 374 00:34:44,165.001 --> 00:34:48,785.001 So Ken I'm going go ahead and share this chat with you, which is another feature that's pretty cool. 375 00:34:49,35.001 --> 00:34:58,305.001 But what's pretty awesome is instead of having to just copy this whole thing, I can create a link and I can share this with Ken and he can go right to the conversation. 376 00:34:58,305.001 --> 00:35:04,15.001 And I'm going go ahead and stop sharing and turn it over to you, Ken, and give you the link to continue the conversation. 377 00:35:05,905.001 --> 00:35:06,925.001 Thanks Erin. 378 00:35:07,795.001 --> 00:35:08,125.001 Okay. 379 00:35:08,125.001 --> 00:35:15,385.001 So as Erin said I'm now able to actually access the link right where she left off. 380 00:35:15,665.001 --> 00:35:23,685.001 You can see here these are, this is exactly what she shared and she did mention this is probably a couple. 381 00:35:24,555.001 --> 00:35:27,255.001 10, 12, 20 pages of information. 382 00:35:27,435.001 --> 00:35:36,75.001 So one of the things that I recommend doing is asking the prompt to shorten it down so that it is digestible, but you're not losing the information as gathered. 383 00:35:36,125.001 --> 00:35:40,415.001 I'm an optimistic person, so I'm big, a big believer on positive feedback. 384 00:35:40,415.001 --> 00:35:42,35.001 So I'd say this is great. 385 00:35:42,335.001 --> 00:35:43,265.001 Thank you. 386 00:35:43,545.001 --> 00:35:55,355.001 And then I'll say please condense to format for a format that is more digestible to a sales. 387 00:35:55,385.001 --> 00:35:58,115.001 I'm going learn how to spell one day. 388 00:35:59,45.001 --> 00:35:59,255.001 Yeah. 389 00:35:59,255.001 --> 00:36:03,35.001 With ai you barely need to, I know, right? It'll still know what you're talking about. 390 00:36:03,185.001 --> 00:36:12,995.001 And BDR audience, rather than full paragraphs, please bullet the insights. 391 00:36:13,730.001 --> 00:36:16,910.001 Be sure to include the sources though. 392 00:36:16,910.001 --> 00:36:18,800.001 'cause that's, I know that's important for sellers. 393 00:36:18,800.001 --> 00:36:20,480.001 They wanna know where the source came from. 394 00:36:20,530.001 --> 00:36:44,820.001 And then the other thing I think it's important to call it is don't remove any vital information that could be particularly, actually, I'm going leave that misspelling in and see what happens helpful for a seller so what you're seeing is it's thinking and it's creating a condensed version. 395 00:36:44,820.001 --> 00:36:53,350.001 And what, 30 seconds and this is so much more consumable for, 90% of the population. 396 00:36:53,590.001 --> 00:36:59,220.001 That being said, like you could also say, Hey, I wanna focus on just one of these competitors. 397 00:36:59,280.001 --> 00:37:00,660.001 In this case we've got three. 398 00:37:00,900.001 --> 00:37:04,530.001 But if you wanna take like a deeper dive, you could do that. 399 00:37:04,830.001 --> 00:37:09,720.001 There's a lot of flexibility with what you can do beyond the deep research and then how to be able to use it. 400 00:37:09,720.001 --> 00:37:19,700.001 And so I think in this instance, we're just giving one example, but again, I, I challenge our listeners and everyone out there to think about what's possible. 401 00:37:19,700.001 --> 00:37:28,610.001 What is the big hairy problem that you're trying to solve? And then back into it with all of these different opportunities to create meaningful content. 402 00:37:29,675.001 --> 00:37:30,635.001 Yeah, I agree. 403 00:37:30,635.001 --> 00:37:34,985.001 And so I wanna call it something that you mentioned earlier, this 80% that we're focusing in on. 404 00:37:35,315.001 --> 00:37:45,815.001 Now, I asked for it to condense things down and it did maybe a little too much for my liking, but the nice thing is I can work with the GPT to have this improved. 405 00:37:45,815.001 --> 00:37:47,825.001 So I said, this is a little too tight. 406 00:37:48,95.001 --> 00:38:06,615.001 Please condense the analysis down to no more than six pages and include more depth around objection handling because we know that's important and traps. 407 00:38:07,645.001 --> 00:38:18,985.001 You're so nice to the GPTI will actually typically tell it that I don't like it, so if it gives me an output that I'm not interested in, I'll say, this isn't for me, or this isn't great. 408 00:38:19,315.001 --> 00:38:24,985.001 And then try to give them a more concrete example of what I'm looking for. 409 00:38:25,195.001 --> 00:38:26,665.001 Just another little strategy. 410 00:38:26,665.001 --> 00:38:36,870.001 I think that Ken will probably survive when the AI overlords come and they may not like the way that I'm responding to them, but that's just another strategy you can also use to get to what you're looking for. 411 00:38:37,200.001 --> 00:38:37,470.001 Yeah. 412 00:38:37,470.001 --> 00:38:38,310.001 Very valid. 413 00:38:38,310.001 --> 00:38:40,620.001 And the call out is two things. 414 00:38:40,620.001 --> 00:38:48,100.001 One, uploading an example of what good looks like or the style, the format, the tone I think is helpful in any scenario. 415 00:38:48,250.001 --> 00:38:54,40.001 Second, I will survive when the AI bots take over and you all are more mean to them. 416 00:38:54,40.001 --> 00:38:55,620.001 So I'm feeling good right now. 417 00:38:55,620.001 --> 00:39:09,450.001 Sometimes it'll try to put things in a word doc, but this is good enough for the example we're trying to use what I'm going do here is I'm going copy this input, and I'm going do this the real way. 418 00:39:09,510.001 --> 00:39:13,380.001 I would do it if none of you were here watching me right now, do this. 419 00:39:13,620.001 --> 00:39:23,350.001 I'm literally just going copy all of this information as it's producing it and command C. 420 00:39:24,70.001 --> 00:39:25,300.001 You're going old school. 421 00:39:25,420.001 --> 00:39:25,630.001 I'm not. 422 00:39:25,630.001 --> 00:39:30,910.001 Now, another option you have here is also clicking on the where it says right below. 423 00:39:30,910.001 --> 00:39:32,710.001 Let me know if you want the iSense Workday version. 424 00:39:32,710.001 --> 00:39:33,700.001 You can also copy that. 425 00:39:33,700.001 --> 00:39:45,780.001 Now the reason sometimes folks don't do it that way is if you take that copy and then copy that into, say, a Google Doc or Word doc or something like that, it does tend to lose the formatting and look a little bit funky. 426 00:39:46,20.001 --> 00:39:54,910.001 And so when you do what Ken's doing, which is highlighting and copying, and then if you were to paste that into a doc, it would actually keep or maintain the formatting as you're seeing it on the screen. 427 00:39:54,910.001 --> 00:39:59,230.001 Depending on what your use case is, definitely the copy button can be faster and easier. 428 00:39:59,280.001 --> 00:40:00,570.001 Yeah, very valid point. 429 00:40:00,910.001 --> 00:40:05,770.001 Okay, so now we have this content, right? But we want it in a more presentable way. 430 00:40:06,10.001 --> 00:40:15,610.001 I have heard sellers say like, when I'm presenting competitive analysis, they're saying like, in a word doc it just, it doesn't present well. 431 00:40:15,850.001 --> 00:40:19,450.001 And a word doc's fine, especially when internal tools. 432 00:40:19,660.001 --> 00:40:27,790.001 But when you're able to polish something and make it a little more finished, it does change the perception of the audience who's reviewing the content. 433 00:40:28,60.001 --> 00:40:38,150.001 And so I'm going go back to, I'm going call them our frenemy potential long-term friend vendor, which is Gamma because we did review them and give them a little bit of a critical review. 434 00:40:38,150.001 --> 00:40:43,970.001 But I am a convert because Gamma is my now go-to tool that I think I'm in every day. 435 00:40:44,550.001 --> 00:40:45,210.001 Wow. 436 00:40:45,300.001 --> 00:40:45,655.001 Every day. 437 00:40:46,455.001 --> 00:40:51,855.001 So you did also convert me, and I will say I did buy the paid version based on what you were able to do. 438 00:40:52,75.001 --> 00:40:55,835.001 I think we talked about beautiful AI versus gamma still love. 439 00:40:55,835.001 --> 00:40:56,495.001 Beautiful. 440 00:40:56,525.001 --> 00:40:57,155.001 It's great. 441 00:40:57,395.001 --> 00:40:57,965.001 This is. 442 00:40:58,335.001 --> 00:41:03,945.001 A great tool for if you have specific content that you want it to preserve. 443 00:41:04,155.001 --> 00:41:12,195.001 And so I think one of the things that can be helpful and you're going see can do it, is you've got multiple options about how to bring that content in. 444 00:41:12,195.001 --> 00:41:17,505.001 And you have the ability to decide how close you want it to match your original content. 445 00:41:17,775.001 --> 00:41:21,135.001 And that I actually do think is super helpful and thank you Gamma for. 446 00:41:21,600.001 --> 00:41:25,680.001 Taking the feedback and also being able to upload custom templates. 447 00:41:25,790.001 --> 00:41:30,200.001 Which, so if you need to preserve the brand of your company, you can actually do that now. 448 00:41:30,200.001 --> 00:41:35,720.001 Whereas when we were, we gave it a fair review before, in my opinion, you couldn't do it before. 449 00:41:35,720.001 --> 00:41:40,420.001 So just, shout out for the the engineering work that was done to improve the tool. 450 00:41:40,600.001 --> 00:41:43,420.001 Yeah, it is definitely a tool that I'm a fan of. 451 00:41:43,550.001 --> 00:41:45,860.001 Like I said, I'm just going do a copy and paste. 452 00:41:45,860.001 --> 00:41:47,480.001 You could put this in a Google doc. 453 00:41:47,820.001 --> 00:41:52,230.001 You could put this in a Word doc and upload it, but this is just the way that I'm looking at it. 454 00:41:52,560.001 --> 00:41:55,50.001 Then you'll see here, there's a few different options. 455 00:41:55,50.001 --> 00:41:56,250.001 There's presentation. 456 00:41:56,515.001 --> 00:41:59,5.001 Webpage document and social. 457 00:41:59,155.001 --> 00:42:01,855.001 Now presentation was what we reviewed before. 458 00:42:01,855.001 --> 00:42:12,775.001 I'd say it's pretty much the same, but what I have realized is I actually do use it to create some just quick hit PowerPoints where I just need things structured and laid out and I don't wanna spend that time doing it. 459 00:42:13,75.001 --> 00:42:23,935.001 But where I think Gamma is standing out right now is documents, eBooks, white papers, internal training docs that you just wanna look more polished gamma can do. 460 00:42:24,205.001 --> 00:42:35,225.001 So I just dropped in that content we created and as Erin mentioned, you can see up here, you can have the text content be generated based off what was said, condensed or preserved. 461 00:42:35,405.001 --> 00:42:41,145.001 For this example, I'm going say keep the text because we've already done this rich analysis and we vetted it to be quality. 462 00:42:41,535.001 --> 00:42:42,405.001 The other thing is. 463 00:42:42,720.001 --> 00:42:46,260.001 I like to view card by card view, so it can help me create pages. 464 00:42:46,530.001 --> 00:42:48,120.001 And this is super easy. 465 00:42:48,120.001 --> 00:42:49,590.001 It's going do its best. 466 00:42:49,590.001 --> 00:42:56,70.001 Go to have you split it up and I like to have it try and it usually does a pretty good job. 467 00:42:56,460.001 --> 00:43:03,0.001 So now I've got a battle card cover page, and then the content broken down into specific portions. 468 00:43:03,210.001 --> 00:43:05,100.001 Again, this is our condensed battle card. 469 00:43:05,310.001 --> 00:43:12,210.001 Also, you can pull in from different places so you can say, Hey, do AI created images or use stock photos or use web images. 470 00:43:12,270.001 --> 00:43:14,820.001 And the format is what we're going keep it on. 471 00:43:15,90.001 --> 00:43:22,370.001 So the other thing is it does give you little tips and tricks on how to use the solution while you're in it. 472 00:43:22,790.001 --> 00:43:24,860.001 As Erin mentioned, you can do custom themes. 473 00:43:25,100.001 --> 00:43:28,580.001 I have a custom theme for us, so I'm going go ahead and use that. 474 00:43:28,580.001 --> 00:43:29,860.001 For this example, yeah. 475 00:43:29,910.001 --> 00:43:33,720.001 If you wanna customize what your content is, you can give it additional instruction. 476 00:43:33,720.001 --> 00:43:46,650.001 So in this case, we are building it for sales, but if you want it to, be more for marketers or more for SDRs, or whatever it is, and then you want it to do the AI generation a bit more, it's going do that for you right in the tool, which is pretty cool. 477 00:43:46,810.001 --> 00:43:54,800.001 And definitely saves time without having to like, create, multiple versions or enlist your graphic designer and have to go back and change and all the things. 478 00:43:54,800.001 --> 00:44:08,400.001 So I think that's the other thing that I appreciate about it is, our graphic designer can focus on more sophisticated projects versus just creating, PowerPoints or eBooks or things like that can be done a little more simply through a tool like this. 479 00:44:08,925.001 --> 00:44:10,95.001 Yeah, that's a great point. 480 00:44:10,95.001 --> 00:44:17,55.001 It's the time savings and efficiency that allows time for your designers to do other things while you're still able to get a quality output. 481 00:44:17,335.001 --> 00:44:29,145.001 So what, in 10 or 15 seconds we were able to get this version and it looks pretty good, but I noticed when it was building, I think this image is like a little lame. 482 00:44:29,385.001 --> 00:44:34,65.001 And this is a long page just for this information. 483 00:44:34,245.001 --> 00:44:37,235.001 So I'm actually able to say don't, I'm going take Erin advice. 484 00:44:37,235.001 --> 00:44:38,525.001 Don't like the design. 485 00:44:39,95.001 --> 00:44:49,35.001 Could you try a more standard view without an image? It's going take the feedback and work on it. 486 00:44:49,465.001 --> 00:44:57,475.001 I don't know about you, Erin, designer's cues are pretty long and even an edit like this would've maybe taken a couple hours, if not a day plus. 487 00:44:57,535.001 --> 00:45:00,895.001 And I was able to just change the format of this in just a couple of seconds. 488 00:45:00,995.001 --> 00:45:05,105.001 It's not about replacing designers, right? It's about freeing them up to do more things. 489 00:45:05,105.001 --> 00:45:09,275.001 That's more, that would be more impactful, but you can still get the quality output you're looking for. 490 00:45:09,995.001 --> 00:45:10,985.001 Yeah, a hundred percent. 491 00:45:10,985.001 --> 00:45:16,475.001 And I think one of the things that I've found in my career is designers get frustrated with the back and forth. 492 00:45:16,895.001 --> 00:45:39,135.001 And I think that the ability to cut that down can enhance the experience for everybody, right? So it's your designers can focus on these like cool projects and even create some of the templates that you can build these types of, low hanging fruit type of things off of, but then they can focus on, doing more video or animation or things that are going add value to your brand. 493 00:45:39,135.001 --> 00:45:41,535.001 The average person isn't going be able to do just yet. 494 00:45:41,535.001 --> 00:45:52,455.001 And and I think that they can spread their creative wings, which is also something that I think is so powerful about being able to help, have others self-serve on some of these types of assets. 495 00:45:52,875.001 --> 00:45:54,195.001 Yeah, that's a good point. 496 00:45:54,615.001 --> 00:46:12,205.001 So what, in less than 30 ish minutes, we were able to get a v one of a completed competitive analysis battle card out to our sellers, and we were able to talk to our focus group, we were able to have a market analysis done, and then we were able to produce the content. 497 00:46:12,505.001 --> 00:46:18,235.001 Even if it's not perfect, you're still getting pretty good results for just a short amount of time. 498 00:46:19,15.001 --> 00:46:28,505.001 Yeah, and I think the thing to add to that is you're getting to 80% your human oversight helps you get to that a hundred percent or 95% or whatever, your goal is. 499 00:46:28,505.001 --> 00:46:34,265.001 And so if you take the work that it's been done, like you can see from Ken's example, you can pretty easily edit it as well. 500 00:46:34,265.001 --> 00:46:36,665.001 So it's Hey, this isn't right, or I don't wanna include this. 501 00:46:36,995.001 --> 00:46:38,255.001 Great, no problem. 502 00:46:38,285.001 --> 00:46:39,275.001 Easily edit it. 503 00:46:39,275.001 --> 00:46:50,415.001 Versus having to do, exhaustive research or take a ton of time or go back and forth with your designer or, the whole piece that takes a lot of extra effort. 504 00:46:50,655.001 --> 00:46:55,845.001 And know, you can streamline your workflows by being able to create something that's pretty good. 505 00:46:56,535.001 --> 00:46:56,955.001 Yeah. 506 00:46:57,165.001 --> 00:47:02,115.001 I'm just laughing who has it in their career, been working on something and been like, I think it's good enough. 507 00:47:02,115.001 --> 00:47:03,555.001 I don't need to go back to design. 508 00:47:03,745.001 --> 00:47:04,805.001 I'm going have to do that now. 509 00:47:05,805.001 --> 00:47:06,285.001 It's true. 510 00:47:06,285.001 --> 00:47:21,355.001 But I do think there is value to, being able to work with your designers to do meaty and meaningful, strategic projects versus again, I just feel like the designers, like how many designers have been like, I just can't wait to get into PowerPoint. 511 00:47:21,535.001 --> 00:47:23,635.001 I'm dying to make that next deck. 512 00:47:23,665.001 --> 00:47:24,955.001 Please let me do it. 513 00:47:25,265.001 --> 00:47:38,445.001 I think that that's another thing that AI can help with is just how do we make things that we don't wanna do, or more of the mundane, repetitive stuff alleviate some of that pain for ourselves and uplevel what we're doing. 514 00:47:38,445.001 --> 00:47:45,375.001 And I think that's the thing that I have found most enjoyable about AI and this journey that we're on. 515 00:47:45,375.001 --> 00:47:45,435.001 Yeah. 516 00:47:45,945.001 --> 00:47:46,935.001 I agree with you. 517 00:47:46,935.001 --> 00:47:48,45.001 It's exciting. 518 00:47:48,75.001 --> 00:47:49,95.001 So much has changed. 519 00:47:49,455.001 --> 00:47:49,695.001 Okay. 520 00:47:49,695.001 --> 00:47:59,605.001 So we just did three tools over kind of one outcome that we've seen marketers struggle with, no matter what size of company or industry. 521 00:47:59,935.001 --> 00:48:09,415.001 So today we actually solved one of the most common challenges and pains that a market has to do, which is to deliver the competitive analysis. 522 00:48:09,625.001 --> 00:48:17,645.001 And we did that with an output of a design battle card and started with that focus group that helped us understand where buyer's mindset is. 523 00:48:17,895.001 --> 00:48:19,695.001 So now that we've done that. 524 00:48:19,755.001 --> 00:48:30,555.001 Where should we go next, Erin? So there's so many different possibilities, and I think that's the thing that we're most excited about this season is that go to market and connecting people. 525 00:48:30,825.001 --> 00:48:35,445.001 You can take one tool and use the outputs in so many different ways. 526 00:48:35,445.001 --> 00:48:44,215.001 So it's not just this finite, piece of research that you did or this, user group and being able to ask a different questions and it just stays there. 527 00:48:44,455.001 --> 00:49:06,555.001 I think what's compelling about these AI tools is you could connect 'em all and I think at some point, Ken, we're probably going wanna talk about Make and Zapier and all the other tools out there that folks are using to create these automated workflows which are super fun and interesting and create a cohesive go to market engine that can support all the teams. 528 00:49:07,110.001 --> 00:49:07,560.001 Yeah. 529 00:49:07,610.001 --> 00:49:16,280.001 So if you saw my face just now, I agree with everything Erin said, but I am amazed by what Erin been able to do with make, because my head just spins. 530 00:49:16,280.001 --> 00:49:17,840.001 But it is possible to learn. 531 00:49:18,80.001 --> 00:49:18,950.001 It is doable. 532 00:49:18,950.001 --> 00:49:27,670.001 But it is crazy how much progress has been made in the past couple months around integrating workflows that actually resemble a go-to market process. 533 00:49:28,420.001 --> 00:49:31,720.001 And I think the crazy thing is you and I we're not engineers. 534 00:49:31,930.001 --> 00:49:32,20.001 No. 535 00:49:32,70.001 --> 00:49:39,930.001 And that would've taken a lot of engineering effort in the past to create, the web hooks that all fit together and it. 536 00:49:40,320.001 --> 00:49:42,540.001 It just makes it a little bit easier. 537 00:49:42,600.001 --> 00:49:50,310.001 And I think it's one of those things that you can feel dumb and challenged sometimes when you're in some of these tools. 538 00:49:50,310.001 --> 00:49:51,330.001 I don't get it. 539 00:49:51,450.001 --> 00:49:59,500.001 I know with Make, I was struggling last year and it was just I could not figure it out and finally it just clicked. 540 00:49:59,530.001 --> 00:50:02,890.001 And once I got one working, it was a lot easier to create more. 541 00:50:03,140.001 --> 00:50:06,410.001 Same thing on the Zapier side and all the other tools out there. 542 00:50:06,410.001 --> 00:50:22,710.001 It's a little bit of, there's a pretty steep learning curve, but once it clicks you can get a lot more out of these tools and have them automate a lot of the process that, would've been a copy and paste or, there's a lot of human error that can go into, transferring one from the other. 543 00:50:22,710.001 --> 00:50:24,540.001 And this creates just a lot more efficiency. 544 00:50:24,540.001 --> 00:50:27,660.001 So I'm excited to show some of that off this season. 545 00:50:27,780.001 --> 00:50:31,500.001 What else are you excited about this season, Ken? 'cause your research is super interesting. 546 00:50:32,305.001 --> 00:50:38,185.001 So I am thrilled that we are starting season two talking about go to market. 547 00:50:38,375.001 --> 00:50:46,135.001 We hit on the beginning of the episode, but go to market is now where we're seeing a lot of the energy around AI going into and. 548 00:50:46,375.001 --> 00:50:47,815.001 And people are investing in. 549 00:50:47,905.001 --> 00:50:58,615.001 And and one of the reasons why we are talking about the shift is marketing is such an integral part to go to market, that it's actually now being touched and leveraged by different groups, which it had before. 550 00:50:58,615.001 --> 00:51:01,795.001 But AI is accelerating that and expanding it. 551 00:51:02,5.001 --> 00:51:08,995.001 So I think I'm most excited about going through this journey of what a go-to-market lifecycle looks like influenced by ai. 552 00:51:09,235.001 --> 00:51:19,990.001 So we're going bring on some cool guests, but what I'm most excited about is stuff related to my research because I feel like our listeners who are, a lot of them are CMOs CXOs, VPs. 553 00:51:20,290.001 --> 00:51:26,500.001 Thinking about this leadership change and shift that needs to happen in order to get your teams onboarded is something we need to talk about. 554 00:51:26,500.001 --> 00:51:31,270.001 So we're going kinda be talking about throughout the season, but I'm hoping to spend a little bit of time sharing some of my research. 555 00:51:31,270.001 --> 00:51:34,240.001 So geek out with you guys and make it applicable to you all. 556 00:51:35,20.001 --> 00:51:37,750.001 And so one of the other things is the change management piece. 557 00:51:37,750.001 --> 00:51:39,970.001 I think a lot of folks are struggling with that. 558 00:51:40,130.001 --> 00:51:48,500.001 One of our favorite mentors out there, shout out to Cheryl, I, Noah for bringing the whole Gretzky, quote of skate to where the puck is going. 559 00:51:48,840.001 --> 00:51:50,910.001 The puck is like in the stratosphere. 560 00:51:50,960.001 --> 00:52:00,690.001 Last year when we talked to, some of our thought leaders and it was like, oh, if I squint I can see into the future and now it's not so clear. 561 00:52:00,720.001 --> 00:52:14,700.001 So I think that the change management piece is something I'm super interested in, that you've been working a lot on Penn and what does that mean for leadership and what does it mean for folks that are becoming managers, how to manage that change management through this time that is so uncertain. 562 00:52:15,120.001 --> 00:52:16,530.001 Yeah, a lot of uncertainty. 563 00:52:16,530.001 --> 00:52:19,950.001 People are looking for clarity and they're looking for their leaders to provide that clarity. 564 00:52:20,400.001 --> 00:52:38,770.001 No one else I'd rather be doing this podcast with, so I'm so excited to be starting in season two and everybody listening if you've got ideas on topics you want covered, let us know because we're building this together, right? It's still the same community that we had before, but it's just expanded a little bit and we're excited to, to explore it with you all. 565 00:52:39,295.001 --> 00:52:39,655.001 Yeah. 566 00:52:39,655.001 --> 00:52:48,585.001 And if you wanna be a guest because you are doing something innovative and you want people to know about it, our whole mission is to democratize AI for everybody. 567 00:52:48,585.001 --> 00:52:50,295.001 And so would love to hear from you. 568 00:52:50,295.001 --> 00:52:58,695.001 Or if you have ideas, as Ken said, please reach out to us on LinkedIn or Twitter or all the different ways you can connect with us. 569 00:52:58,695.001 --> 00:53:03,145.001 What signal we're all the things, we probably need a discord. 570 00:53:03,145.001 --> 00:53:09,485.001 That's what the kids are using, But we're here to take your feedback and make this podcast the best it can be. 571 00:53:10,95.001 --> 00:53:13,5.001 That's a wrap on episode one of season two. 572 00:53:13,35.001 --> 00:53:14,415.001 Thanks for being with us. 573 00:53:14,695.001 --> 00:53:21,775.001 Lots to look forward to this season and learn, especially as AI continues to transform the landscape for us. 574 00:53:21,775.001 --> 00:53:27,475.001 So be sure to smash that subscribe button, share it with your friends who may get some benefit. 575 00:53:27,695.001 --> 00:53:31,55.001 Engage with us on LinkedIn, or whatever your preferred platform is. 576 00:53:31,85.001 --> 00:53:34,535.001 We're here to listen and thank you for listening. 577 00:53:34,835.001 --> 00:53:38,285.001 Let's keep crafting the future of go to market together.