On AI: Replacing Recruiters, Scaling Agents, and Getting Out of the Pilot Phase

We talk with Lennard Kooy, CEO of Lleverage, about why nobody actually cares about AI—they care about outcomes. Lennard drops hard truths on why most companies are moving too slow, how to accelerate adoption by assisting before replacing, and where agentic workflows are creating real ROI. He also demos a live “gladiator challenge” of building a cold outreach AI agent from scratch, and outlines what every GTM leader needs to do right now to stay relevant.

Whether you're a RevOps pro, a BDR sick of cold calls, or a CMO trying not to get fired—this is your wake-up call.

 

04:43 Interview with Lennard Kooy

09:36 AI-Powered Recruitment and Sales Automation

14:29 Adopting AI in Business Processes

21:29 Practical AI Workflow Demonstration

23:40 Generating Company Lists and Lead Data

24:24 Simplifying Automation for Users

24:47 User Experience and Customer Support

25:39 Quick Wins for New Users

28:10 Potential of Agentic AI in Go-to-Market

30:59 Guardrails for Adopting AI

32:32 The Power of MCP in AI Integration

35:25 Mid-Market Focus and ROI

37:34 Future of AI in Professional Roles

39:41 Advice for Go-to-Market Leaders

42:29 Quick Hits: Practical AI Tips

44:57 Final Thoughts and Takeaways

Key Topics

• Reality Check: Why most businesses don’t care about AI—and what they do care about
• The Trust Layer: How “assist before replace” is the cheat code for adoption
• Recruiting Reinvented: How Lleverage AI automated 70% of their hiring pipeline
• Agentic GTM: Where agent workflows are replacing cold calls, research, and lead scoring
• Demo Time: Watch Lennard build an AI agent live, in under 5 minutes
• MCP Advantage: Why this new spec removes dev bottlenecks and boosts AI usability
• Speed > Perfection: Why going slow will kill your competitive edge
• Hard Truths for Leaders: You will get replaced if you don’t move faster
• Future of Work: What GTM roles look like in a near-agentic future

About our Guest:

Lennard Kooy is a seasoned tech entrepreneur focused on how emerging technologies can transform business operations. As CEO of AI platform Lleverage, he helps companies automate complex processes without requiring technical expertise, drawing from his experience building and selling martech company Storyteq to ITG.

Known for his pragmatic approach to AI adoption, Lennard regularly shares insights on making advanced automation accessible to everyday business teams. He's passionate about strengthening Europe's position in the global AI landscape and frequently writes about the practical realities of implementing AI in enterprise settings.

🔨 Practical Takeaways

3 Quick Wins for New AI Users

1. Start with something visual, with visible output, not backend automations
2. Keep workflows small (≤5 blocks) to understand what’s happening
3. Build in areas where you have subject-matter expertise—test what you know

Stay tuned for more insightful episodes from the FutureCraft podcast, where we continue to explore the evolving intersection of AI and GTM. Take advantage of the full episode for in-depth discussions and much more.

To listen to the full episode and stay updated on future episodes, visit the FutureCraft GTM website.

Disclaimer: This podcast is for informational and entertainment purposes only and should not be considered advice. The views and opinions expressed in this podcast are our own and do not represent those of any company or business we currently work for/with or have worked for/with in the past.

Music: Far Away - MK2

Episode Transcript

(00:00):
.999Hey crafters.
Just a reminder, this podcast is for informational entertainment purposes only and should not be considered advice. 3 00:00:08,330.0000000002 --> 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 00:00:21,770.001 --> 00:00:30,622.81052381 uh, uh, uh, um, uh, Hey there. 7 00:00:30,652.81052381 --> 00:00:39,802.81052381 Welcome to The Future Craft Go To Market podcast, where we're exploring how AI is changing all things go to market, from awareness to loyalty and everything in between. 8 00:00:40,192.81052381 --> 00:00:51,352.81052381 I'm Ken Rodin, one of your guides on this exciting new journey And I am Erin Mills, your co-host, and here with Auggie, our mascot, who you might hear in the background today. 9 00:00:52,942.81052381 --> 00:00:54,162.81052381 Another episodes Auggie. 10 00:00:54,502.81052381 --> 00:00:54,682.81052381 I give. 11 00:00:54,682.81052381 --> 00:00:55,522.81052381 We love you. 12 00:00:55,817.81052381 --> 00:01:04,267.81052381 Auggie Oggie, the Frenchie for those of you that are listening and we're here to unpack the future of go-to-market and AI will share some best practices. 13 00:01:04,417.81052381 --> 00:01:08,137.81052381 Talk to industry pioneers who are paving the way in AI and go to market. 14 00:01:08,437.81052381 --> 00:01:17,272.81052381 So Ken, how are you paving the way? Yeah, the thing I wanted to share today was something that I'm helping a friend of mine do. 15 00:01:17,512.81052381 --> 00:01:28,792.81052381 They've recently come into a new role and it's a sales role, and the account information on their territory is a little sparse. 16 00:01:28,922.81052381 --> 00:01:30,742.81052381 I've built a workflow that. 17 00:01:31,222.81052381 --> 00:01:47,502.81052381 takes whatever they have and creates a deep analysis on the target account, and then actually fills in the information that they need by researching everything from LinkedIn profiles to previous speaking events. 18 00:01:47,747.81052381 --> 00:02:01,137.81052381 The next step is actually creating a dashboard in Gemini so that you can interact with it and share with VP of sales that might be jumping on the call or a solution engineer to give them an overview on where the account is. 19 00:02:01,377.81052381 --> 00:02:07,297.81052381 And it's outside of CRM, which some people might, say doesn't work, but this is really effective for them. 20 00:02:07,297.81052381 --> 00:02:11,997.81052381 They're already using it in their workflows and it's helping them prep their internal teams to go faster. 21 00:02:12,157.81052381 --> 00:02:16,357.81052381 If you have bad data, you have options to get around it. 22 00:02:16,567.81052381 --> 00:02:17,937.81052381 And it's saved them hours. 23 00:02:17,937.81052381 --> 00:02:19,207.81052381 They wouldn't even be able to do this before. 24 00:02:19,207.81052381 --> 00:02:20,432.81052381 So I'm really excited about that one. 25 00:02:21,212.81052381 --> 00:02:22,352.81052381 That's really cool. 26 00:02:22,352.81052381 --> 00:02:25,947.81052381 Folks interested in that should reach out to you on LinkedIn and get more details. 27 00:02:26,352.81052381 --> 00:02:34,407.81052381 How are you using ai? One of the things that I've been really obsessed with is EO and GEO, like all of the acronyms. 28 00:02:34,407.81052381 --> 00:02:40,57.81052381 Let's talk about getting in LLMs and one of the things that we've learned is that. 29 00:02:40,402.81052381 --> 00:02:46,132.81052381 These LLMs really like the sort of question and answer, so they don't wanna have to look that hard for the answer. 30 00:02:46,132.81052381 --> 00:02:49,402.81052381 One of the things I've been trying to do is incorporate more FAQs. 31 00:02:49,812.81052381 --> 00:03:05,12.81052381 In an agentic platform that I've been using, I built a scraper to look at different sites to do research for me and then also to create an FAQ on different topics to go along with some of the things I've been writing about. 32 00:03:05,62.81052381 --> 00:03:05,812.81052381 it's pretty slick. 33 00:03:06,302.81052381 --> 00:03:07,892.81052381 That's really cool. 34 00:03:08,382.81052381 --> 00:03:12,332.81052381 how much time do you think this would've taken you before, I don't think it would've happened. 35 00:03:12,742.81052381 --> 00:03:15,677.81052381 I think that's one of the things that I would say I'm really loving about. 36 00:03:15,727.81052381 --> 00:03:26,17.81052381 AI right now, because deep research has gotten so good, and even regular research within ChatGPT or Claude, like you're able to do things that you only dreamed about and said oh, I'll do that someday. 37 00:03:26,17.81052381 --> 00:03:28,297.81052381 And it actually allows you to get started on it. 38 00:03:28,297.81052381 --> 00:03:29,837.81052381 And I'm loving this right now. 39 00:03:30,377.81052381 --> 00:03:31,487.81052381 Yeah, I totally agree. 40 00:03:31,487.81052381 --> 00:03:40,67.81052381 unleashing creativity and being able to do things that just, we all have this laundry list of things to do and it's like how many things can you actually tick off in a given day. 41 00:03:40,407.81052381 --> 00:03:44,57.81052381 some of these like projects that are just interesting, but just don't have the time to they. 42 00:03:44,922.81052381 --> 00:03:49,32.81052381 Are not the most important thing because you're spending time on the most important things. 43 00:03:49,32.81052381 --> 00:03:51,282.81052381 And so they keep falling to the end of the list. 44 00:03:51,322.81052381 --> 00:04:07,832.81052381 And now with automation can create a lot more of those types of assets that would've been really challenging to create before When people are asking how AI is changing B2B marketing, one of those things is taking things that might be on the bottom of your list and they can take it to the top without you having to do much. 45 00:04:08,292.81052381 --> 00:04:19,942.81052381 Yeah, without losing the things that are most important, I think that's the key, right? You're still able to do the things that are most meaningful for your business, and now you could add these additional things because you're a lot more efficient. 46 00:04:19,952.81052381 --> 00:04:20,822.81052381 so that's been a fun one. 47 00:04:21,197.81052381 --> 00:04:26,702.81052381 Who are we talking to today? We are talking to the CEO of leverage. 48 00:04:26,702.81052381 --> 00:04:30,747.81052381 I'm real excited to have Lennard Kooy on the show and can't wait. 49 00:04:31,867.81052381 --> 00:04:32,287.81052381 Great. 50 00:04:32,287.81052381 --> 00:04:33,127.81052381 Let's pop over. 51 00:04:33,127.81052381 --> 00:04:33,607.81052381 You're right. 52 00:04:33,607.81052381 --> 00:04:34,567.81052381 I can't control it. 53 00:04:34,567.81052381 --> 00:04:35,227.81052381 I'm a fanboy. 54 00:04:35,227.81052381 --> 00:04:35,977.81052381 Let's go right now. 55 00:04:36,351.68238393 --> 00:04:43,177.31394643 um, uh, uh, uh, hi everyone, and we're back. 56 00:04:43,327.31394643 --> 00:04:54,517.31394643 Today we have Lennard Kooy, a seasoned tech entrepreneur, focused on how emerging technologies can transform business operations as CEO of AI platform leverage. 57 00:04:54,517.31394643 --> 00:04:59,677.31394643 He helps companies automate complex processes without requiring technical expertise. 58 00:05:00,382.31394643 --> 00:05:09,592.31394643 Drawing from his experience building and selling MarTech companies, story cut, story Tech and ITG, known for his pragmatic approach to AI adoption. 59 00:05:09,832.31394643 --> 00:05:15,412.31394643 Leonard regularly shares insights on making advanced automation accessible to everyday business teams. 60 00:05:15,622.31394643 --> 00:05:24,972.31394643 He's passionate about strengthening Europe's position in a global AI landscape and frequently writes about the practical realities of implementing AI in an enterprise setting. 61 00:05:25,542.31394643 --> 00:05:26,772.31394643 Leonard, thanks for joining us. 62 00:05:26,772.31394643 --> 00:05:28,62.31394643 We're so happy to have you here. 63 00:05:28,892.31394643 --> 00:05:29,722.31394643 Happy to be here. 64 00:05:31,182.31394643 --> 00:05:35,772.31394643 built a 500 person company and left it behind to start leverage from scratch. 65 00:05:35,982.31394643 --> 00:05:54,442.31394643 Can you tell us about that early stage energy and what changed in the world of go to market that made this the right time to start over? Yeah, I think if you look at the tech landscape now, and about five years ago when AI came to the scene, everyone knew, this is something that we need to do something with. 66 00:05:54,442.31394643 --> 00:05:59,902.31394643 about 18 months ago or two years ago, I really felt, okay, I can stick with my. 67 00:06:00,412.31394643 --> 00:06:02,242.31394643 Old school marketing SaaS. 68 00:06:02,272.31394643 --> 00:06:04,732.31394643 But if I wanna do something new, now is the time. 69 00:06:04,732.31394643 --> 00:06:09,602.31394643 generative AI will probably change a lot of things, especially in the tech space. 70 00:06:09,662.31394643 --> 00:06:12,742.31394643 Cloud has been you need to switch. 71 00:06:13,192.31394643 --> 00:06:18,802.31394643 So it was just looking at the technology and what it could do, it's going to be such a seismic shift. 72 00:06:19,382.31394643 --> 00:06:25,732.31394643 when you have a company like the one I had that was private equity owned, it's very hard to be agile and hop on that wave. 73 00:06:26,372.31394643 --> 00:06:30,872.31394643 it's way easier to do with a small team that can move very fast. 74 00:06:31,437.31394643 --> 00:06:35,977.31394643 I contemplated, should I do this with the companies I had? it was a portfolio of marketing tech companies. 75 00:06:36,447.31394643 --> 00:06:38,317.31394643 But that was not going to work. 76 00:06:38,437.31394643 --> 00:06:42,877.31394643 So I thought, let's start from scratch and try to ride that wave with a new group of people. 77 00:06:42,877.31394643 --> 00:06:46,417.31394643 It's a little bit of timing, luck, appetite to do something. 78 00:06:46,417.31394643 --> 00:06:47,857.31394643 It's a mix of things. 79 00:06:48,312.31394643 --> 00:06:49,932.31394643 Yeah, it's also the timing. 80 00:06:49,932.31394643 --> 00:06:52,207.31394643 my out ran out so I could leave. 81 00:06:52,207.31394643 --> 00:06:55,197.31394643 there's a puzzle that needs to fall into place, to be able to do it. 82 00:06:55,857.31394643 --> 00:06:58,977.31394643 And my coincidentally fell into place, so yeah. 83 00:06:59,142.31394643 --> 00:06:59,277.31394643 Yeah. 84 00:07:00,432.31394643 --> 00:07:11,12.31394643 When I was learning about you, I found this article where you said most businesses don't really care about AI as much as, people working in AI right now. 85 00:07:11,142.31394643 --> 00:07:13,152.31394643 Simply because, they're not thinking about it. 86 00:07:13,212.31394643 --> 00:07:14,982.31394643 That feels like a powerful insight for us. 87 00:07:14,982.31394643 --> 00:07:18,792.31394643 People who are talking about AI living and breathing AI products every day. 88 00:07:19,637.31394643 --> 00:07:38,287.31394643 Was there a single experience that made you see that as a human block and not a technical block? I think in general, in tech we live in this bubble and think, AI why doesn't the entire world shift towards it, But if you look at 99% of businesses they care about, I sell wood. 89 00:07:39,517.31394643 --> 00:07:43,247.31394643 So why would I care about ai? I wanna sell more wood. 90 00:07:43,737.31394643 --> 00:07:48,437.31394643 There's a huge gap between, yeah, you need to do AI But I wanna sell more wood. 91 00:07:49,157.31394643 --> 00:08:10,7.31394643 when you are in the bubble and you're just building something you think everyone understands AI and the story, what it could do, But then it's very refreshing to just go into a more traditional business and then talk about AI and they just sit across, you say, listen, I get what you're saying and I get what it could do, but I just have a problem with customer support. 92 00:08:12,32.31394643 --> 00:08:14,902.31394643 Whether that's AI or we outsource to Bangladesh. 93 00:08:14,902.31394643 --> 00:08:20,547.31394643 how do we make it cheaper? AI is a way to do that, right? It's a very efficient way to do it. 94 00:08:20,607.31394643 --> 00:08:28,967.31394643 But in the end, AI is not the thing, they're looking for, they're looking for a solution to their problem, We have a lot of people that fix machines. 95 00:08:29,387.31394643 --> 00:08:31,7.31394643 I need less people that fix machines. 96 00:08:31,587.31394643 --> 00:08:38,707.31394643 for a lot of people inside of tech, sometimes making that leap or sitting next to those type of personas is very hard to do. 97 00:08:39,427.31394643 --> 00:08:42,417.31394643 And we started building our product more for the tech audience. 98 00:08:42,417.31394643 --> 00:08:46,107.31394643 you have a lot less explaining around AI to do with tech. 99 00:08:46,717.31394643 --> 00:08:50,227.31394643 then we pivoted away from tech and more towards a business patron. 100 00:08:50,227.31394643 --> 00:08:53,527.31394643 And then we started having all these conversations that they said yeah, ai. 101 00:08:53,527.31394643 --> 00:08:54,337.31394643 Lovely, lovely. 102 00:08:54,337.31394643 --> 00:08:54,547.31394643 But. 103 00:08:55,417.31394643 --> 00:08:56,737.31394643 I just wanna solve this problem. 104 00:08:56,767.31394643 --> 00:08:59,227.31394643 Can it do that? Yeah, it could potentially do that. 105 00:08:59,227.31394643 --> 00:09:04,417.31394643 that's why I also made this statement in tech, we believe that people care about ai. 106 00:09:04,627.31394643 --> 00:09:05,917.31394643 People don't care about ai. 107 00:09:06,552.31394643 --> 00:09:06,842.31394643 Yeah. 108 00:09:06,967.31394643 --> 00:09:08,977.31394643 They care about, a solution to their problem. 109 00:09:11,287.31394643 --> 00:09:14,127.31394643 Switching a little bit more to tactically talking about problems. 110 00:09:14,157.31394643 --> 00:09:26,327.31394643 One of the things that you've built is an AI powered agent that screens candidates, onboards and does the sales outreach and saving some companies like 35,000 euros per month. 111 00:09:26,687.31394643 --> 00:09:36,357.31394643 Can you walk us through the before and after and what did that really look like for both? Client in day to day terms and also just the technology side. 112 00:09:37,232.31394643 --> 00:09:40,487.31394643 The screening agent is actually something we built for ourselves. 113 00:09:40,857.31394643 --> 00:09:45,977.31394643 we hire people because we're a startup and not everything can be done with ai, so you still hire people. 114 00:09:46,287.31394643 --> 00:09:51,867.31394643 if you are a funded startup like us, you have a huge inflow on certain roles, like hundreds of applicants. 115 00:09:52,747.31394643 --> 00:09:58,437.31394643 it used to be quite time consuming to go through all those profiles to find a couple of gems that you're looking for. 116 00:09:58,977.31394643 --> 00:10:05,217.31394643 Now, there are more traditional ways of doing that, but typically, like five years ago, we would have an internal recruiter that did a lot of that stuff. 117 00:10:05,917.31394643 --> 00:10:09,907.31394643 And we as a company had this philosophy, everything that we can automate will automate. 118 00:10:10,7.31394643 --> 00:10:15,847.31394643 So also in this instance, instead of someone going manually through these profiles, there are a couple of. 119 00:10:16,327.31394643 --> 00:10:16,897.31394643 Steps. 120 00:10:16,947.31394643 --> 00:10:35,387.31394643 when someone applies there is an AI that does an assessment of the profile in comparison to the job they applied for, and then automatically decline 70% Of the applicants because a lot of it is just hard rules, right? this person isn't based in the time zone that we needed to, it doesn't have the experience, yada, yada, yada. 121 00:10:36,17.31394643 --> 00:10:42,827.31394643 Then there's a portion that goes to the next stage and they get an email and they say, you've been progressed to the next round. 122 00:10:43,307.31394643 --> 00:10:44,657.31394643 Here is a number to call. 123 00:10:45,237.31394643 --> 00:10:54,17.31394643 they call it, and then they talk to an ai, and AI does the first interview with them which is an let's say interesting experiment, but actually works quite well. 124 00:10:54,477.31394643 --> 00:11:06,187.31394643 they have a 50 minute conversation with ai and then AI makes an assessment and send that to us in Slack, but also sends it back to the applicant tracking system, and that sort of gets another 70%. 125 00:11:06,612.31394643 --> 00:11:08,292.31394643 Out of the 30% that was left. 126 00:11:08,802.31394643 --> 00:11:11,712.31394643 And then there's a human interaction for the first time. 127 00:11:12,282.31394643 --> 00:11:18,312.31394643 Instead of having 50 minute intro calls to see if there's something there, we only Yeah. 128 00:11:18,312.31394643 --> 00:11:20,982.31394643 Get to speak to about 20 of those 400. 129 00:11:21,742.31394643 --> 00:11:25,402.31394643 So you just replace the role for recruiter, basically an internal recruiter. 130 00:11:25,982.31394643 --> 00:11:28,457.31394643 we do that for a lot of. 131 00:11:28,562.31394643 --> 00:11:29,552.31394643 BDR work. 132 00:11:29,712.31394643 --> 00:11:32,22.31394643 we're testing an AI that actually does the calling. 133 00:11:32,622.31394643 --> 00:11:47,342.31394643 We started with a full AI that does an intro, but then we have something that will connect you to a human when you're interested, so that we get the first let's say heavy lifting because cold calling, nobody wants to cold calling, right? That's just a so there's a lot of these things that you can. 134 00:11:47,967.31394643 --> 00:11:48,567.31394643 automate. 135 00:11:48,637.31394643 --> 00:11:52,482.31394643 a lot of our clients also do that with our platform in variety of ways. 136 00:11:53,972.31394643 --> 00:11:57,982.31394643 So for our listeners who are thinking, that sounds awesome, I want that too. 137 00:11:58,762.31394643 --> 00:12:06,162.31394643 How would they go about doing it and what is the first step they might take? Yeah, I think it really depends on what you're looking to do. 138 00:12:06,162.31394643 --> 00:12:09,432.31394643 I'm not that person that says, our platform is the answer to everything. 139 00:12:09,462.31394643 --> 00:12:16,112.31394643 if it's an incidental process, so let's say you do it four times a week or maybe, 10 times a month. 140 00:12:16,112.31394643 --> 00:12:21,452.31394643 I think the best way is to just build out these projects in chat or in CLOs. 141 00:12:21,452.31394643 --> 00:12:22,532.31394643 And then do your work there. 142 00:12:23,82.31394643 --> 00:12:34,67.31394643 if it is more like the ones that I mentioned that have a lot more higher frequency than like a platform, like a ChatGPT or is a very inefficient method because you have to ask it every time to do something. 143 00:12:34,677.31394643 --> 00:12:36,597.31394643 So then you would land with. 144 00:12:36,872.31394643 --> 00:12:48,552.31394643 Products like ours where you build out the steps of a particular workflow with AI that has to be repeatable if you're in the go-to market or rev ops and you need to do a high quality lead score. 145 00:12:48,552.31394643 --> 00:12:56,522.31394643 And you have all these leads coming in, you want to look for that personal LinkedIn, you wanna look for their company, you wanna, get some other stuff from the internet. 146 00:12:56,522.31394643 --> 00:12:59,552.31394643 And then you wanna calculate a lead score based on your criteria. 147 00:12:59,972.31394643 --> 00:13:06,452.31394643 And let's say there's a hundred leads coming in every month, then you want to have something where you can build out all those steps. 148 00:13:06,942.31394643 --> 00:13:11,22.31394643 a workflow or an agent builder is probably your most efficient way to get to that. 149 00:13:11,602.31394643 --> 00:13:14,642.31394643 you asked, how do I get started? it depends. 150 00:13:14,702.31394643 --> 00:13:20,667.31394643 Make a decision on what kind of process is this a incidental let's say not frequent process. 151 00:13:20,727.31394643 --> 00:13:25,877.31394643 Then it's a good idea to Build out a project in ChatGPT or Gemini or in cloth, and do it there. 152 00:13:26,177.31394643 --> 00:13:32,847.31394643 If it is a highly repeatable process, pick an agent or a workflow builder that you understand. 153 00:13:33,487.31394643 --> 00:13:38,62.31394643 everyone has a different level of how technical or proficient they are with ai. 154 00:13:38,637.31394643 --> 00:13:44,317.31394643 And I think the best thing to do is to look at a variety of platforms that offer these capabilities. 155 00:13:44,317.31394643 --> 00:13:45,997.31394643 And a big one that. 156 00:13:46,212.31394643 --> 00:13:51,942.31394643 Matches with the abstraction layer that you like in terms of complexity and how you can use it. 157 00:13:52,612.31394643 --> 00:14:00,957.31394643 there are a lot of flavors here, right? You have very technical platforms like n8n And then you have something that is a bit more business focused like our. 158 00:14:01,127.31394643 --> 00:14:01,607.31394643 Form. 159 00:14:01,607.31394643 --> 00:14:09,647.31394643 You also have things that are really in the Microsoft realm, so if you live in that world, you can use something like Power Automate and plug some AI in there. 160 00:14:09,647.31394643 --> 00:14:11,52.31394643 So it depends, on who you are. 161 00:14:12,642.31394643 --> 00:14:13,842.31394643 I'm a user of leverage. 162 00:14:13,842.31394643 --> 00:14:17,112.31394643 I use it like every day, and that's how I got connected to ard. 163 00:14:17,162.31394643 --> 00:14:22,792.31394643 But you said something this idea of assist before you replace, and I think that's really smart. 164 00:14:23,362.31394643 --> 00:14:33,757.31394643 Can you share why that principle is so foundational in how you think about rolling out ai? humans are quite resistant to change or at least hesitant. 165 00:14:33,827.31394643 --> 00:14:38,297.31394643 What people sometimes do is say, we're gonna automate this process, and we have now 20 people doing it. 166 00:14:38,337.31394643 --> 00:14:41,7.31394643 and then we flip a switch and then we have zero people doing it. 167 00:14:41,7.31394643 --> 00:14:43,297.31394643 But let's say you're the owner of that process. 168 00:14:43,612.31394643 --> 00:14:52,422.31394643 There's huge risk in that, right? So what if it doesn't work? Or how do I control the quality or how do I get comfortable that, let's say it also gets the edge case, et cetera. 169 00:14:53,52.31394643 --> 00:15:03,42.31394643 So we often start with companies saying, okay, you pick that process that you wanna automate, and then the first thing you do is you put that agent next to the person that is actually currently doing it. 170 00:15:03,752.31394643 --> 00:15:16,222.31394643 And you have that person check the output give feedback on it, iterate on it until everyone feels it is doing as good a job as the person doing it, or at least good enough that It works. 171 00:15:16,582.31394643 --> 00:15:20,572.31394643 by doing that, you get buy-in from the people that actually use it. 172 00:15:20,812.31394643 --> 00:15:25,192.31394643 As the owner of the business process, you get insights on, what it can and can't do. 173 00:15:25,562.31394643 --> 00:15:29,182.31394643 it's a soft landing for AI rather than a hard replacement. 174 00:15:29,522.31394643 --> 00:15:36,982.31394643 it becomes easier to integrate it into your organization than if you say, we're now going to automate this entire process, and we're firing those 20 people. 175 00:15:37,897.31394643 --> 00:15:46,487.31394643 If you start with, we put it as an assistant to those people first, to have them work more efficiently, that is a way easier segue to get done. 176 00:15:46,567.31394643 --> 00:15:55,427.31394643 It's a really good way around building trust with the employee rather than, leaning into that fear of replacement there's a lot of fears that people have around ai. 177 00:15:56,657.31394643 --> 00:15:58,697.31394643 Thinking about that trust concept. 178 00:15:59,87.31394643 --> 00:16:01,278.31394643 Your platform is designed for technical users. 179 00:16:02,797.31394643 --> 00:16:05,132.31394643 Simple, very human and intuitive. 180 00:16:05,402.31394643 --> 00:16:13,607.31394643 What UX choices or habits Help teams trust and actually use the platform? A couple of things there are quite interesting. 181 00:16:13,607.31394643 --> 00:16:17,197.31394643 a lot of these automation platforms are moving data from A to B. 182 00:16:18,107.31394643 --> 00:16:28,467.31394643 But it's very hard for a human to understand what is happening So we have constructs in our platform that are UI components where you can see the output of what the workflow is doing. 183 00:16:29,137.31394643 --> 00:16:32,17.31394643 we also have this concept of resumable workflows. 184 00:16:32,17.31394643 --> 00:16:36,417.31394643 I can show you the output and you can say, that's okay. 185 00:16:37,77.31394643 --> 00:16:39,597.31394643 Then it will resume the rest of the workflow. 186 00:16:40,87.3139464 --> 00:16:48,427.3139464 you can also make that integrated with where people work, we have workflows where the AI does something and then sends a slack message to a particular channel. 187 00:16:48,427.3139464 --> 00:16:50,867.3139464 Say, I have this insight, or this is my output. 188 00:16:51,392.3139464 --> 00:16:54,662.3139464 You tell me left or right, and then the person in the channel says, go right. 189 00:16:54,662.3139464 --> 00:16:57,42.3139464 And it completes the next steps of the workflow. 190 00:16:57,322.3139464 --> 00:17:03,612.3139464 in that way people feel that they're collaborating with the AI rather than it's this thing in the corner just does something. 191 00:17:04,182.3139464 --> 00:17:08,962.3139464 And to facilitate that, you need integrations with the platforms where people work. 192 00:17:09,497.3139464 --> 00:17:22,467.3139464 we have all these integrations in our platform, but making it visual and having constructs where it interacts with humans are really key I think that the visual piece has really changed it for a lot of folks who may not be technical. 193 00:17:22,467.3139464 --> 00:17:28,377.3139464 I'm much more of a visual learner, so these tools for me, being able to see, where does it break? great. 194 00:17:28,377.3139464 --> 00:17:29,367.3139464 I need to fix that part. 195 00:17:29,417.3139464 --> 00:17:35,932.3139464 I think, a lot of people get super anxious when they're thinking about automations and diving in, especially in the agent space. 196 00:17:36,232.3139464 --> 00:17:48,947.3139464 What do you say to folks, who are fearful of adopting or how are you helping folks to shift that mindset? I always try to understand where the fear comes from first. 197 00:17:49,207.3139464 --> 00:17:52,797.3139464 Because there's no, silver bullet in solving that. 198 00:17:52,797.3139464 --> 00:18:01,252.3139464 sometimes the fear comes from, I tried it at some point, and they hallucinate a lot, and you try to mediate that fear or show them how they can mediate it. 199 00:18:01,792.3139464 --> 00:18:05,242.3139464 sometimes the fear comes from, it might replace what I'm actually doing now. 200 00:18:05,342.3139464 --> 00:18:07,292.3139464 then you have to try to get them into the mindset. 201 00:18:07,302.3139464 --> 00:18:17,537.3139464 do you really like that part of your job? what if that is just being done and you have more time for the things that actually matter? So when it comes to, let's say you're in go to market and you are A BDR. 202 00:18:17,627.3139464 --> 00:18:23,887.3139464 There are so many manual data entry things that you have to make, create lead lists, and then create personalized messaging, et cetera. 203 00:18:24,307.3139464 --> 00:18:42,267.3139464 Now, if you're a, B, DR, it would be way cooler to actually think about, which new verticals should I actually try to tap into? Or how can I change my story et cetera, right? So the strategic or creative things are by nature a lot more interesting for humans to do than just repeatable data entry. 204 00:18:42,867.3139464 --> 00:18:57,857.3139464 if the fear is, it might actually replace me, I always try to get them to a point where you say, yeah, it will replace the things that you don't like so that you have more time for the things that you do In the end, sometimes you also have to wake up people like this is happening, right? Whether you like it or not. 205 00:18:57,907.3139464 --> 00:19:06,217.3139464 if you don't like it, your competitor or peer will like it and they will become a lot more efficient and effective in what they do. 206 00:19:06,667.3139464 --> 00:19:09,917.3139464 And in the end, that will do harm to your position. 207 00:19:09,927.3139464 --> 00:19:14,157.3139464 Yes, I understand fear, Let's say the soft approach in mediating the fear. 208 00:19:14,157.3139464 --> 00:19:25,257.3139464 Sometimes also the hard approach with, wake up here, right? this isn't a thing that is just bossing by and you just sit on it and then maybe in three years time it's flew over and then you're still doing manual data entry. 209 00:19:25,307.3139464 --> 00:19:27,727.3139464 That's not going to happen, people also have to be realistic. 210 00:19:28,862.3139464 --> 00:19:37,242.3139464 One of the things I do when I'm training people on how to use leverage specifically is they'll ask if something's possible and I'll say, yes it is. 211 00:19:37,242.3139464 --> 00:19:46,822.3139464 But if we break it down, you can get more clarity and understand the steps to actually how we built it, which will help you your team, build trust, which will help with adoption. 212 00:19:47,452.3139464 --> 00:19:51,802.3139464 I know that you've said that adoption is more about a behavior than the actual technology. 213 00:19:51,842.3139464 --> 00:20:00,832.3139464 what role do you think leaders play in ensuring AI tools actually get used? Not just stuck in this pilot phase that a lot of companies seem to be stuck in right now. 214 00:20:01,237.3139464 --> 00:20:09,317.3139464 The variety of ways, but what I see working a lot is that you bring it in a way that it is seen as constructive rather than frightening. 215 00:20:09,357.3139464 --> 00:20:12,867.3139464 You lead by example by doing a lot with AI yourself. 216 00:20:12,867.3139464 --> 00:20:30,572.3139464 But in the end, if you really wanna get it into your organization and into workflows, what I see working well is, hey you put it in as an assistant first, but at some point when you are comfortable as a leader, hey, this is actually doing better than my humans You start enforcing its use. 217 00:20:30,622.3139464 --> 00:20:37,672.3139464 So what I see happening is that they bring it in as an assistant and then they gather a lot of feedback on how it's used. 218 00:20:37,672.3139464 --> 00:20:41,512.3139464 They iterate on the agent and at some point they think, Hey, this is actually working really well. 219 00:20:41,902.3139464 --> 00:20:43,282.3139464 And then they flip the switch. 220 00:20:43,342.3139464 --> 00:20:47,452.3139464 So rather than AI being the assistant, they say, Hey, you. 221 00:20:47,457.3139464 --> 00:20:52,517.3139464 Put it into the AI to do the sanity check before it can go to the next phase. 222 00:20:52,757.3139464 --> 00:20:55,217.3139464 So let's say you're doing invoice handling. 223 00:20:55,657.3139464 --> 00:21:04,467.3139464 first you're checking what the AI is doing, but later it is, you have to put it in the AI first to do all the screening before you can send it to the accounting system. 224 00:21:05,7.3139464 --> 00:21:07,557.3139464 You just can't send it directly to the accounting system anymore. 225 00:21:07,557.3139464 --> 00:21:10,557.3139464 that street is closed, right? You have to go through the AI first. 226 00:21:11,227.3139464 --> 00:21:12,337.3139464 It's not an option anymore. 227 00:21:12,937.3139464 --> 00:21:22,847.3139464 And then people adapt a lot quicker because people are a lot of times stuck in their ways, right? And then they just get used to this AI as an assistant, and that's the new way. 228 00:21:22,917.3139464 --> 00:21:26,427.3139464 So then they flip it and they say, AI is the primary source. 229 00:21:26,427.3139464 --> 00:21:28,77.3139464 And then we go to the next step. 230 00:21:28,377.3139464 --> 00:21:29,637.3139464 I think that's really interesting. 231 00:21:29,737.3139464 --> 00:21:37,407.3139464 So maybe next up we could do what we call the gladiator round otherwise known as show me the tool. 232 00:21:37,757.3139464 --> 00:21:40,387.3139464 Would love Len for you to show us something that. 233 00:21:40,622.3139464 --> 00:21:47,832.3139464 Our listeners and folks that might be watching online could use in a practical way, if you don't mind taking the challenge okay. 234 00:21:48,22.3139464 --> 00:21:51,982.3139464 So a platform, a lot of SaaS constructs that I won't bore you with. 235 00:21:52,32.3139464 --> 00:21:55,242.3139464 The meat is building workflows or agents now. 236 00:21:55,332.3139464 --> 00:22:04,362.3139464 Instead of people having to figure out how they put all these boxes on the canvas and draw the lines between them, et cetera, our starting points after a bit different. 237 00:22:04,812.3139464 --> 00:22:06,372.3139464 So you describe what you wanna build. 238 00:22:06,952.3139464 --> 00:22:14,742.3139464 Erin, what would you like to automate? I am excited about that BDR idea of doing some of the cold outreach. 239 00:22:14,822.3139464 --> 00:22:15,112.3139464 Yeah. 240 00:22:30,152.3139464 --> 00:22:39,477.3139464 So for folks that are listening Len is putting in a basic prompt of I wanna automate the process of doing outreach and I want to have ai, Generate Yeah. 241 00:22:39,577.3139464 --> 00:22:44,857.3139464 So let's say you wanna build a lead list and you have an idea of the vertical that you wanna target. 242 00:22:44,907.3139464 --> 00:22:48,977.3139464 then you would say, What if I can just say to that ai, here's the vertical. 243 00:22:48,977.3139464 --> 00:22:52,787.3139464 Now find me companies that are actually relevant in that vertical and their contact details. 244 00:22:53,127.3139464 --> 00:22:54,387.3139464 that's basically what is in there. 245 00:22:54,867.3139464 --> 00:22:59,67.3139464 So let's say you put this in our system, what the AI will try to figure out. 246 00:22:59,692.3139464 --> 00:23:01,602.3139464 Is what you wanna try to do here. 247 00:23:02,212.3139464 --> 00:23:04,842.3139464 sometimes it'll ask a couple of questions back. 248 00:23:04,842.3139464 --> 00:23:09,722.3139464 Sometimes it will just start building something because it feels it has all the information it needs. 249 00:23:10,152.3139464 --> 00:23:12,882.3139464 And basically it just starts building the automation for you. 250 00:23:13,462.3139464 --> 00:23:14,512.3139464 Now take a while. 251 00:23:14,562.3139464 --> 00:23:18,72.3139464 put all these notes onto the canvas on what thinks it should do. 252 00:23:18,412.3139464 --> 00:23:21,352.3139464 And then hopefully in the end you get something that works. 253 00:23:21,772.3139464 --> 00:23:24,242.3139464 the entire idea is that we get you. 254 00:23:25,42.3139464 --> 00:23:34,557.3139464 Up and running a lot quicker than you having to try to figure everything out This isn't a perfect workflow but it is a very good idea of, what it could look like. 255 00:23:34,742.3139464 --> 00:23:35,972.3139464 And we put all the blocks out. 256 00:23:35,972.3139464 --> 00:23:37,977.3139464 I wanna do a vertical. 257 00:23:37,977.3139464 --> 00:23:40,137.3139464 I wanna do a geographic location. 258 00:23:40,137.3139464 --> 00:23:41,247.3139464 I think it makes a lot of sense. 259 00:23:41,527.3139464 --> 00:23:43,297.3139464 maybe you wanna do some company size. 260 00:23:43,752.3139464 --> 00:23:46,502.3139464 Then it will start generating a company list. 261 00:23:46,502.3139464 --> 00:23:50,172.3139464 It will do some web search based on a company list. 262 00:23:50,172.3139464 --> 00:23:53,2.3139464 It will extract information for those companies. 263 00:23:53,372.3139464 --> 00:23:56,372.3139464 process that into lead data, and then return the list. 264 00:23:56,422.3139464 --> 00:24:11,802.3139464 the entire idea is, how can I get to a working workflow really quickly? And then you can start talking to that workflow, right? Instead of, again, you can also just draw lines and boxes on the canvas, right? But the idea is it's based on how you would like to work. 265 00:24:11,862.3139464 --> 00:24:19,442.3139464 So either you just type it in and does it save to Google sheets? And you pick the spreadsheet that you wanna connect it to by talking to it. 266 00:24:19,472.3139464 --> 00:24:23,932.3139464 Or you can put all these complicated blocks on it yourself if you can't figure it out. 267 00:24:24,582.3139464 --> 00:24:25,752.3139464 And that is how we. 268 00:24:26,562.3139464 --> 00:24:40,682.3139464 Try to make it a lot simpler to build these type of automations than someone having to understand all these concepts, either when do I need a scrape web and when do I need a prompt note and which integrations do they have, et cetera. 269 00:24:40,732.3139464 --> 00:24:43,942.3139464 a lot of our clients just chat with this box and then, get to some. 270 00:24:45,712.3139464 --> 00:24:47,722.3139464 Yeah, this is really cool. 271 00:24:47,722.3139464 --> 00:25:03,142.3139464 So I will say, as I've mentioned, I've used the platform, I use it daily the idea behind it that I think is really powerful is breaking down the steps For the user so they can see how each of the nodes impacts the output. 272 00:25:03,212.3139464 --> 00:25:08,502.3139464 also if something's not connected to the right, Google Drive you can see why it breaks. 273 00:25:08,502.3139464 --> 00:25:11,922.3139464 it's a really intuitive way especially now that I've done it a few times. 274 00:25:12,2.3139464 --> 00:25:16,802.3139464 And also you guys have really good customer support, so I feel very much supported in that. 275 00:25:17,32.3139464 --> 00:25:17,962.3139464 I'm a big fan. 276 00:25:18,567.3139464 --> 00:25:21,692.3139464 how was the gladiator test? I think it's success. 277 00:25:21,742.3139464 --> 00:25:24,922.3139464 as I mentioned, I really like visually being able to see it break out. 278 00:25:25,282.3139464 --> 00:25:26,602.3139464 I use n8n quite a bit. 279 00:25:26,602.3139464 --> 00:25:31,72.3139464 And the thing that I like is just that the connection piece. 280 00:25:31,552.3139464 --> 00:25:35,162.3139464 So in my head, then it starts to make sense and I think that's what I really like about. 281 00:25:35,392.3139464 --> 00:25:35,812.3139464 Seeing that. 282 00:25:35,812.3139464 --> 00:25:37,162.3139464 So I'm gonna give you survived. 283 00:25:37,252.3139464 --> 00:25:37,732.3139464 You survived. 284 00:25:37,732.3139464 --> 00:25:39,12.3139464 I think you made it. 285 00:25:39,532.3139464 --> 00:25:58,262.3139464 So sticking to the leverage, could you share three quick wins new users should focus on in building something so that they can really get an understanding of the platform and how to use it versus what I have maybe made the mistake of is overshooting it and trying to build something too complicated and having to like reverse engineer it. 286 00:25:58,772.3139464 --> 00:26:02,327.3139464 I always say to start with something where you can actually see the output first. 287 00:26:02,772.3139464 --> 00:26:16,962.3139464 If you start with a process that starts with an integration and ends with an integration, it's very hard to understand what is this or workflow actually outputting because yeah, you then have to, for example, go into Google Sheet and look at it there. 288 00:26:16,962.3139464 --> 00:26:20,932.3139464 I always say start with something that you can see the output of in our platform. 289 00:26:21,527.3139464 --> 00:26:29,757.3139464 then you get a feel of the quality of the workflow that you're building, and at some point replace the last step with where you actually wanna send it. 290 00:26:30,247.3139464 --> 00:26:31,187.3139464 So that is one. 291 00:26:31,757.3139464 --> 00:26:35,568.3139464 I also think that people should start with something that is. 292 00:26:36,317.3139464 --> 00:26:44,247.3139464 At most five or six building blocks just to get a handle of, Hey, this is what it could do, this is its limitations. 293 00:26:44,297.3139464 --> 00:26:46,787.3139464 then I get some understanding of what is going on. 294 00:26:47,267.3139464 --> 00:26:53,897.3139464 Like our co-pilot sometimes generates these complex workflows with 20 building blocks, and then it's yeah, okay, that is cool. 295 00:26:53,897.3139464 --> 00:26:55,707.3139464 But very hard also. 296 00:26:57,102.3139464 --> 00:27:04,357.3139464 If it breaks, and that sometimes happen to debug it, but also let's say it functionally works in the end. 297 00:27:04,357.3139464 --> 00:27:05,527.3139464 These are all business rules. 298 00:27:05,527.3139464 --> 00:27:12,967.3139464 They're blocks with business rules, right? So even as a human, if it functionally works to understand what goes on in that workflow. 299 00:27:13,702.3139464 --> 00:27:15,412.3139464 Less information is better. 300 00:27:15,572.3139464 --> 00:27:21,262.3139464 And a lot of these ais are just, you just give it business rules, right? So do this and then do this, figure out this. 301 00:27:21,712.3139464 --> 00:27:24,502.3139464 So yeah, keep it compact initially. 302 00:27:24,742.3139464 --> 00:27:26,157.3139464 Make sure you can see the output. 303 00:27:27,82.3139464 --> 00:27:45,842.3139464 And do something that you understand and what I mean by that is, let's say if you're a marketer, then if you do something with content, then content generation or let's say creating optimized pages, that's something you actually understand so that you get a better grasp on what AI can do and its limitations because you are the subject matter expert. 304 00:27:46,452.3139464 --> 00:27:54,792.3139464 If I now build a workflow that tries to invent medicine, have zero clue whether it's done a good job, right? No clue. 305 00:27:55,182.3139464 --> 00:27:58,452.3139464 So those three things, make sure you can see the output. 306 00:27:58,452.3139464 --> 00:28:01,422.3139464 Start compact and make sure you start with something that you actually understand. 307 00:28:03,747.3139464 --> 00:28:05,937.3139464 I think that's really good advice for folks. 308 00:28:05,987.3139464 --> 00:28:10,287.3139464 One of the things we're talking about this stuff all day, and Len, you're on the, early stage of it. 309 00:28:10,347.3139464 --> 00:28:17,82.3139464 Where do you see the most potential for agentic AI and go to market? In go to market. 310 00:28:17,812.3139464 --> 00:28:29,442.3139464 One is BDR replacement, right? if you look at these YC cohorts and batches, there are very good representation of the first things that a new technology can actually solve. 311 00:28:30,52.3139464 --> 00:28:34,507.3139464 if you analyze these batches, a lot of them when they came to go to market, were focused on. 312 00:28:35,252.3139464 --> 00:28:44,802.3139464 BDR replacements or assistance or content generation or post, creation, So those are the most evident ones. 313 00:28:45,447.3139464 --> 00:29:06,522.3139464 Now you're seeing a layer deeper where you see agents helping in procurement processes like RFPs, et cetera, or helping AEs prep Meetings a lot better, right? you automatically gather an entire summary of the client that you're talking to, their pain points and all that sort of thing, because it was AI research. 314 00:29:06,522.3139464 --> 00:29:11,797.3139464 So it always starts with a very simple, most apparent one, and then it grows in complexity. 315 00:29:11,847.3139464 --> 00:29:20,807.3139464 I think on rev ops and go to market, it has a huge potential to do a lot of the heavy lifting when it comes to data gathering and data entry. 316 00:29:21,427.3139464 --> 00:29:25,937.3139464 I always say a CRM, like it's a legacy system to an extent. 317 00:29:26,297.3139464 --> 00:29:29,777.3139464 The best version of a CRM is a CRM that isn't there. 318 00:29:30,652.3139464 --> 00:29:32,572.3139464 That's very hard for people to Digest. 319 00:29:32,572.3139464 --> 00:29:40,642.3139464 But who likes being in a CRM? You like being there to see charts of the results, but you don't like being there, putting all the information and dragging the car there and there. 320 00:29:40,642.3139464 --> 00:29:41,572.3139464 You don't like doing that. 321 00:29:41,992.3139464 --> 00:29:45,742.3139464 So what AI can do a lot there is just take that away. 322 00:29:46,472.3139464 --> 00:30:01,262.3139464 I think for go to market there is huge upside in data gathering, data entry prepping of meetings, prepping of prospects identifying verticals, doing research about, let's say which go to market work. 323 00:30:01,602.3139464 --> 00:30:11,552.3139464 one of The groups of people that I see interact with AI almost the most in comparison to other groups, their devs and their go to market. 324 00:30:11,612.3139464 --> 00:30:21,132.3139464 Those are, let's say the primary people that currently pick up ai, the rest will follow But it's interesting to see that they're quite on the forefront of them. 325 00:30:23,522.3139464 --> 00:30:40,292.3139464 I think what's really interesting about all of this is there's all this conversation happening around what's possible with go to market, and there's a ton possible, you just highlighted a lot of repeatable tasks that people can automate, save them a lot of time, and allow 'em to do more human centric things. 326 00:30:40,652.3139464 --> 00:30:43,382.3139464 There's also a lot of noise out there. 327 00:30:43,862.3139464 --> 00:30:50,542.3139464 That's like AI can save your whole life and you can make three x your pipeline and you close 10 extra deals. 328 00:30:50,732.3139464 --> 00:30:54,632.3139464 And so it's a little overwhelming for people receiving that message. 329 00:30:55,652.3139464 --> 00:30:58,142.3139464 A lot of those AI promises just fall flat. 330 00:30:59,72.3139464 --> 00:31:09,862.3139464 What's any guardrail or guidance you would give to a go-to-market leader to ensure that they don't fall into some sort of trap or maybe even come in with their own expectations and be disappointed. 331 00:31:10,297.3139464 --> 00:31:16,987.3139464 I think the most simple one I can give is if you don't understand what that AI is doing, then don't buy it. 332 00:31:18,242.3139464 --> 00:31:22,712.3139464 I see these guys that, share these workflows with 300 blocks on them. 333 00:31:23,292.3139464 --> 00:31:25,392.3139464 I just look at it, whoa, that's inefficient. 334 00:31:25,912.3139464 --> 00:31:39,322.3139464 A and B is who understand what's going on here? And then it's just, yeah, I put something in and I hope I get something out if you're in GoTo market, you're probably a subject matter expert and I would at least like to understand what is going on here, on a high level. 335 00:31:39,942.3139464 --> 00:31:50,702.3139464 So if someone comes to you with this entire silver bullet that will three x your pipeline and you just give me money and if you don't understand what that thing is actually doing. 336 00:31:52,142.3139464 --> 00:31:53,312.3139464 I would stay clear of it. 337 00:31:53,627.3139464 --> 00:31:53,917.3139464 Yeah. 338 00:31:55,227.3139464 --> 00:32:04,977.3139464 I don't know how that company's going, whether it was 11 XI one of the first AI BDRs they had this entire black box yeah, just give us money and we'll do all your BDR work. 339 00:32:05,337.3139464 --> 00:32:06,927.3139464 Nobody actually knew how it worked. 340 00:32:07,77.3139464 --> 00:32:14,807.3139464 So yeah, you get leads, and then it ended up not generating so many leads or generated a lot of leads, but a lot of very poor leads. 341 00:32:14,937.3139464 --> 00:32:17,757.3139464 AI is almost human You can see the business rule. 342 00:32:17,757.3139464 --> 00:32:28,327.3139464 you can understand it because if you're in rev ops or go to market you know how you do it yourself, you also know how an AI should probably do it in a more scalable way, but a lot of the core principles are the same. 343 00:32:28,967.3139464 --> 00:32:31,548.3139464 So again, make sure you understand it. 344 00:32:32,282.3139464 --> 00:32:47,152.3139464 It's a good Talking about understanding a lot of folks are talking about MCP right now, and how does it change how people build agents or workflows, or what advantages do you see for go-to-market teams? the biggest advantage is that's just way simpler. 345 00:32:47,782.3139464 --> 00:32:52,237.3139464 the old school sales world was made of APIs talking to each other. 346 00:32:53,227.3139464 --> 00:32:58,957.3139464 So you have this particular thing that you can do in a particular app and that has a particular API endpoint. 347 00:32:59,7.3139464 --> 00:33:06,552.3139464 it is very granular in how you can connect something and for a lot of go to market people, an API call. 348 00:33:07,237.3139464 --> 00:33:07,807.3139464 I don't know. 349 00:33:07,907.3139464 --> 00:33:12,717.3139464 how does that work? which one should I use do I use a post or put if they even get there. 350 00:33:13,237.3139464 --> 00:33:16,507.3139464 And what MCP does, it abstracts all of that away. 351 00:33:16,972.3139464 --> 00:33:27,712.3139464 Because it's often just one link and the company that enabled that MCP server or client saying, okay here are all our actions, all those API endpoints that you have. 352 00:33:27,742.3139464 --> 00:33:32,432.3139464 We put it in a box and you can just tell it what you wanna do in natural language. 353 00:33:32,992.3139464 --> 00:33:35,542.3139464 And then we'll figure out what you actually meant. 354 00:33:36,232.3139464 --> 00:33:40,12.3139464 That is, let's say for you and I'm technical, but a lot of people. 355 00:33:40,477.3139464 --> 00:33:59,112.3139464 That's just way more efficient, than going to an API documentation figuring out, ah, what's this, what's that like? And so what MCP unlocks is, let's say, integration between systems that were possible in the prem CPH, but so hard to do that. 356 00:33:59,112.3139464 --> 00:33:59,802.3139464 Nobody did that. 357 00:34:01,482.3139464 --> 00:34:07,862.3139464 Yeah, and I think what you said earlier was really insightful about who's adopting AI with these go-to-market teams. 358 00:34:07,862.3139464 --> 00:34:11,972.3139464 I think it actually gives more power to the go-to-market teams. 359 00:34:12,707.3139464 --> 00:34:16,297.3139464 You don't have the friction of being like, oh, I gotta get an engineer to do it for me. 360 00:34:17,777.3139464 --> 00:34:25,127.3139464 And I think that's in general the power of ai, to be honest that it enables a lot of people to do things that they couldn't do before. 361 00:34:25,887.3139464 --> 00:34:30,447.3139464 It enables people to now build a website without any technical knowledge. 362 00:34:30,567.3139464 --> 00:34:34,867.3139464 It enables people to do contract drafting while they're not a lawyer. 363 00:34:35,87.3139464 --> 00:34:41,7.3139464 It enables people to do let's say automated process that they couldn't do I think AI is Itself. 364 00:34:41,7.3139464 --> 00:34:43,827.3139464 the biggest enabler in tech that I've ever seen. 365 00:34:43,927.3139464 --> 00:34:46,217.3139464 And also for Go this is another example. 366 00:34:46,217.3139464 --> 00:34:53,777.3139464 Like integrate, just given an MCP link, authenticator, MCP, in cloud we have, for example, ourselves, like we have everything there. 367 00:34:54,147.3139464 --> 00:35:19,147.3139464 And if I wanna do something in HubSpot, I try just to Never go into HubSpot again and say, Hey, what is the status of that deal? Did we move anything in the pipeline today? Can you update this? It's just way simpler to do it in such way than having to go into that system, into the ticket, into the field, Oh, I think that you're probably speaking the language of a lot of our listeners. 368 00:35:19,327.3139464 --> 00:35:23,227.3139464 I don't think a lot of them wanna go into HubSpot or Salesforce Give me the goods. 369 00:35:23,307.3139464 --> 00:35:24,27.3139464 I don't wanna deal with it. 370 00:35:24,297.3139464 --> 00:35:24,717.3139464 Yeah. 371 00:35:25,377.3139464 --> 00:35:42,192.3139464 You guys at leverage target a lot of mid-market companies What really gives those teams an edge versus a big enterprise team or other types of companies? The reason why we target more mid-market is mainly if you go really upstream to enterprise you see a couple of things happening. 372 00:35:42,432.3139464 --> 00:35:50,212.3139464 A, there's a lot of focus on those companies from big consultancy firms like Accenture, but also from open ai, themselves. 373 00:35:50,792.3139464 --> 00:35:52,127.3139464 So it's quite competitive. 374 00:35:52,862.3139464 --> 00:35:58,682.3139464 Then the process of getting things to work there is lengthy, right? There's procurement and all that sort of thing. 375 00:35:58,682.3139464 --> 00:36:00,692.3139464 So getting to value is very hard. 376 00:36:01,332.3139464 --> 00:36:06,582.3139464 And also the people in those organizations are less willing to try things are less agile. 377 00:36:07,132.3139464 --> 00:36:11,282.3139464 if you move a bit more, mid-market, then a lot of those things fall away. 378 00:36:11,747.3139464 --> 00:36:18,417.3139464 There's less competition, less friction in the buying process, and people are often a bit more open to just try out things. 379 00:36:18,997.3139464 --> 00:36:31,677.3139464 and this is more true on the completely SMB side, but on the completely small businesses side, our proposition doesn't have enough ROI or not as much because we automate repetitive processes. 380 00:36:31,677.3139464 --> 00:36:36,357.3139464 And if you are a guy with one bakery, then the amount of back office process is. 381 00:36:36,657.3139464 --> 00:36:38,607.3139464 A lot less than if you have a hundred people. 382 00:36:38,927.3139464 --> 00:36:42,137.3139464 our ROI increases when the organization gets bigger. 383 00:36:42,187.3139464 --> 00:36:44,757.3139464 But when it gets too big, there's too much friction. 384 00:36:44,787.3139464 --> 00:36:46,347.3139464 that's why we focus on mid-market. 385 00:36:47,202.3139464 --> 00:36:52,452.3139464 Yeah, until you can build the robot to start rolling the bread, yeah, it's called monumental. 386 00:36:52,452.3139464 --> 00:36:53,112.3139464 It's very cool. 387 00:36:53,112.3139464 --> 00:36:56,232.3139464 And actually a lot of Dutch construction company are now using it. 388 00:36:56,682.3139464 --> 00:37:05,872.3139464 So there's, literally a robot driving to, and then does all the walls and it's all AI because it has to understand when it has turn a corner and all that sort of thing. 389 00:37:06,392.3139464 --> 00:37:11,162.3139464 So if we can do brick laying, then at some point they can also roll bread, right? That is the, yeah. 390 00:37:11,537.3139464 --> 00:37:12,197.3139464 Wow. 391 00:37:12,247.3139464 --> 00:37:14,317.3139464 It's coming for everything. 392 00:37:14,797.3139464 --> 00:37:20,947.3139464 There's so much happening right now and there's probably more that's going to happen in the coming months and years. 393 00:37:21,157.3139464 --> 00:37:46,932.3139464 Could you share a belief you have now about AI and go to market that you think most leaders will embrace maybe in 12 months, but they either can't see it right now, or they're just not ready to believe it? We believe that we live in a society that in four or five years time, somewhere like that, 90% of what people professionally do will be strategy, creativity, or human interaction. 394 00:37:47,562.3139464 --> 00:37:47,922.3139464 That's it. 395 00:37:48,622.3139464 --> 00:37:56,627.3139464 And when you translate that to go to market, that means that what you're, let's say how you operate will be much more. 396 00:37:57,482.3139464 --> 00:38:24,652.3139464 Thinking about, okay what is my persona? What is product market fit? How do I tell a story? Human interaction, sitting with your client, understanding them, getting feedback, translating that feedback into your AI to do something I envision that a lot of marketing and sales will be if you have more like an enterprise play, almost your entire day will be talking to people no matter your seniority, So nowadays, if you're very senior, you just talk to people. 397 00:38:24,702.3139464 --> 00:38:27,162.3139464 but if you're very junior, you sit behind your computer and you do. 398 00:38:27,712.3139464 --> 00:38:30,292.3139464 Effectively data entry in a lot of cases. 399 00:38:30,382.3139464 --> 00:38:36,292.3139464 And what I see happening is that, let's say in a couple of years times, even the juniors, the only thing they do is talk to people. 400 00:38:36,982.3139464 --> 00:38:41,482.3139464 'cause AI is just doing the rest, right? So there's no real role for a human there anymore. 401 00:38:41,852.3139464 --> 00:38:49,977.3139464 So it's talk to people or coming up with creative things that AI can't do if you think about it, AI is a very smart par. 402 00:38:51,612.3139464 --> 00:38:52,422.3139464 That's what it is. 403 00:38:52,482.3139464 --> 00:38:59,532.3139464 And it can be so smart that it can almost seem that it is not even a parrot, but can actually just talk on itself. 404 00:39:00,262.3139464 --> 00:39:05,322.3139464 But in its core it's a smart parrot because it's trained on data and it can replicate that data. 405 00:39:05,652.3139464 --> 00:39:15,312.3139464 So that means for real creativity and real strategy, humans are probably more efficient So in go to market, I would really focus on people that are. 406 00:39:16,17.3139464 --> 00:39:23,587.3139464 Good with humans that are creative, that can think outside of the box than people that are really efficient in Excel or PowerPoint. 407 00:39:23,617.3139464 --> 00:39:29,492.3139464 That was really a skill maybe two or three years ago, but that won't be a valuable skill going forward. 408 00:39:31,902.3139464 --> 00:39:41,502.3139464 I think the critical thinking piece is gonna be so desired, and like you couple that with the creativity and that's what people are gonna hire for. 409 00:39:41,862.3139464 --> 00:39:59,97.3139464 If you could think about maybe one, piece of advice you'd give every go to market leader, who's adopting AI now, what would it be? the biggest advice, and this will sound very cliche, is that probably you're going too slow, move faster. 410 00:40:00,147.3139464 --> 00:40:04,942.3139464 And that is it sounds like, yeah, these in AI that's true. 411 00:40:05,42.3139464 --> 00:40:12,22.3139464 if I look at a lot of SaaS companies, I think, you're gonna have really tough times staying competitive in the coming two to three years. 412 00:40:12,782.3139464 --> 00:40:26,597.3139464 and then you talk to those people and say, yeah, we wanna do stuff with ai, but we have also our, let's say, yeah, we have our other initiatives a lot of people to onboard we're rebranding and I need to redo my sales collaterals it's, yeah, we'll get to it next quarter. 413 00:40:26,747.3139464 --> 00:40:34,97.3139464 And I think That is backward thinking, right? It is okay, but why don't you hop on the train now, go faster. 414 00:40:34,97.3139464 --> 00:40:43,502.3139464 And then those other problems are solvable, partially also with ai, right? So my biggest advice will be yeah remove the, how do you call it? The blinders. 415 00:40:43,502.3139464 --> 00:40:44,72.3139464 move fast. 416 00:40:45,602.3139464 --> 00:40:48,662.3139464 I think SAS is ripe for disruption now. 417 00:40:48,662.3139464 --> 00:40:50,372.3139464 It's gonna be really interesting. 418 00:40:51,122.3139464 --> 00:40:51,182.3139464 Yeah. 419 00:40:51,842.3139464 --> 00:40:58,782.3139464 One of the things I'm finding when talking to leaders is they know they should be doing something or want to but they don't have the urgency to actually make a jump. 420 00:40:59,172.3139464 --> 00:41:06,262.3139464 And we're starting to hear rumors of, leaders actually having their roles terminated because they didn't. 421 00:41:06,592.3139464 --> 00:41:08,32.3139464 Evolve and start using ai. 422 00:41:08,32.3139464 --> 00:41:12,172.3139464 They weren't considered visionary enough to lead their team through the next transformation. 423 00:41:12,222.3139464 --> 00:41:13,482.3139464 I think we could see more of that. 424 00:41:13,482.3139464 --> 00:41:15,522.3139464 I don't wanna scare people, but it's something to be aware of yeah. 425 00:41:15,522.3139464 --> 00:41:16,642.3139464 But that is far away. 426 00:41:16,687.3139464 --> 00:41:20,437.3139464 A problem, and these are problems that are now on my to-do list. 427 00:41:21,547.3139464 --> 00:41:34,967.3139464 But that's also like the blinders, right? So yeah, there's a fire three kilometers away, but my house is fine, right? So I'm going to bake a cake, right? So I'm going to bake a cake and I'll look outside out the window in a couple of days and see where it is. 428 00:41:35,357.3139464 --> 00:41:39,257.3139464 But then when the fire is at your neighbor's house, then it's too late. 429 00:41:39,712.3139464 --> 00:41:42,552.3139464 What are you gonna do then? You don't have fire extinguishers. 430 00:41:42,552.3139464 --> 00:41:43,962.3139464 You didn't call the fire station. 431 00:41:44,12.3139464 --> 00:41:46,532.3139464 I sometimes do it right, and I think everyone does it. 432 00:41:46,712.3139464 --> 00:41:48,972.3139464 You have these clients, they want stuff from you. 433 00:41:49,22.3139464 --> 00:41:53,412.3139464 your manager asks you to do something, and then AI seems far away. 434 00:41:53,922.3139464 --> 00:41:57,912.3139464 Yeah, it might be an existential exponential threat, but it isn't now. 435 00:41:58,252.3139464 --> 00:42:02,962.3139464 So I'm just going to create that automation HubSpot that was on my to-do list for a long time. 436 00:42:03,572.3139464 --> 00:42:04,292.3139464 that's a very. 437 00:42:05,237.3139464 --> 00:42:06,347.3139464 Dangerous. 438 00:42:06,407.3139464 --> 00:42:08,807.3139464 Five years ago, was actually a pretty decent strategy. 439 00:42:08,837.3139464 --> 00:42:11,987.3139464 Just take off all the things that you had to do and then progress. 440 00:42:12,107.3139464 --> 00:42:18,868.3139464 But now with ai, there's a new world out there, and if you don't understand it, I think that's a very dangerous thing to do. 441 00:42:19,542.3139464 --> 00:42:24,17.3139464 Yeah, it's like iceberg ahead and they're just not moving away It's not moving. 442 00:42:24,77.3139464 --> 00:42:24,617.3139464 We'll see it. 443 00:42:24,617.3139464 --> 00:42:25,157.3139464 We get there. 444 00:42:25,257.3139464 --> 00:42:25,457.3139464 Hey. 445 00:42:25,537.3139464 --> 00:42:29,107.3139464 This has been a great conversation, but we have to wrap up unfortunately. 446 00:42:29,297.3139464 --> 00:42:36,497.3139464 Before we go, we like to do quick hits, just your practical tips and tools for our listeners in a lightning round. 447 00:42:36,497.3139464 --> 00:42:38,417.3139464 Are you open to it? Sure. 448 00:42:38,727.3139464 --> 00:42:44,967.3139464 what's one underrated AI tool or feature you're loving right now that's not leverage. 449 00:42:45,637.3139464 --> 00:42:46,477.3139464 Projects in cloud. 450 00:42:48,127.3139464 --> 00:43:06,142.3139464 What's your go-to prompt framework or workflow that saves you time every single week? Projects in cloud that have MCP How do you keep up with everything happening in AI without drowning follow the right people. 451 00:43:08,122.3139464 --> 00:43:09,82.3139464 Anyone you'd recommend. 452 00:43:10,112.3139464 --> 00:43:17,142.3139464 And that's why I say a lot of people make the mistakes of just following a lot of people, and then some of them are interesting and some of them are not. 453 00:43:17,592.3139464 --> 00:43:24,552.3139464 if you're in go to market, follow three or four people that actually write valuable stop around AI and go to market. 454 00:43:25,497.3139464 --> 00:43:26,457.3139464 Don't follow. 455 00:43:26,457.3139464 --> 00:43:27,597.3139464 I don't know, Sam Alban. 456 00:43:27,627.3139464 --> 00:43:29,757.3139464 You could, but that's also just distracting. 457 00:43:30,297.3139464 --> 00:43:32,7.3139464 So makes sure relevant for you. 458 00:43:32,357.3139464 --> 00:43:41,142.3139464 That's, I what's one smart AI use case you've seen recently that has made you think more people should be doing this? Okay. 459 00:43:41,192.3139464 --> 00:43:42,572.3139464 I think this was a pretty cool one. 460 00:43:42,622.3139464 --> 00:43:44,902.3139464 I mentioned it somewhere and let's go to mark rate. 461 00:43:44,902.3139464 --> 00:43:47,392.3139464 So you have to cut this because otherwise it's not lighting. 462 00:43:47,852.3139464 --> 00:44:02,742.3139464 But I saw this guy for every meeting he has in a certain stage, he has AI automatically do deep research on that particular client based on his HubSpot information, and sends him a very concise, this is the person you're talking to. 463 00:44:03,47.3139464 --> 00:44:08,327.3139464 These are three talking points that he mentioned in his LinkedIn, social, et cetera, that you can use as a hook. 464 00:44:08,657.3139464 --> 00:44:12,467.3139464 And these are four things that are relatable to our product that you can use. 465 00:44:12,987.3139464 --> 00:44:20,187.3139464 I think that I've, like I see in a lot of these tools and they give you lengthy reports and nobody uses it it's a lot of noise. 466 00:44:20,577.3139464 --> 00:44:23,927.3139464 So while I saw what he was doing, He just opened up every conversation. 467 00:44:23,927.3139464 --> 00:44:24,917.3139464 He didn't prep anything. 468 00:44:25,7.3139464 --> 00:44:31,757.3139464 He just walked into the call, looked at it, and then he start, Hey Ken, see you actually have podcast. 469 00:44:32,87.3139464 --> 00:44:36,807.3139464 Can you tell me a bit about it? And that sort of is you, he has this instant connection. 470 00:44:36,877.3139464 --> 00:44:38,497.3139464 And yeah, I thought that was really cool. 471 00:44:39,382.3139464 --> 00:44:40,132.3139464 That is really cool. 472 00:44:41,257.3139464 --> 00:44:43,732.3139464 Hey, Lanar, this has been incredible. 473 00:44:43,732.3139464 --> 00:44:44,572.3139464 Thank you so much. 474 00:44:44,572.3139464 --> 00:44:45,502.3139464 We've learned a ton. 475 00:44:47,222.3139464 --> 00:44:50,42.3139464 we are going to wrap up and be back right after this. 476 00:44:50,832.3139464 --> 00:44:57,82.9455089 um, uh, uh, uh, Alright, and we're back. 477 00:44:57,142.9455089 --> 00:45:02,467.9455089 So Ken, what was your biggest takeaway? There were so many takeaways. 478 00:45:02,467.9455089 --> 00:45:10,107.9455089 I learned so much talking to someone who's in it every day from a different perspective, The team at Leverage talks to customers all the time. 479 00:45:10,107.9455089 --> 00:45:14,727.9455089 I've talked to them quite a bit around using the platform, and they are learning how people are actually using the product. 480 00:45:15,357.9455089 --> 00:45:20,787.9455089 What really stuck out for me though, was the idea of how to think about adoption for employees. 481 00:45:20,967.9455089 --> 00:45:28,437.9455089 If you're struggling or worried about how to get your employees adopt AI and get outta this pilot phase, having. 482 00:45:29,187.9455089 --> 00:45:39,427.9455089 An employee sit down or a team sit down and map out a workflow about how they'd work with AI and have it be human ai, the AI human sandwich, if you will I think is a really good idea. 483 00:45:39,487.9455089 --> 00:45:52,347.9455089 And then when he talked about, leverage and showed you can map out the nodes or think of them as steps so that someone can see where there's actual critical points that need human attention and more clarity to remove that black box. 484 00:45:52,527.9455089 --> 00:45:59,997.9455089 I just think that creates so much trust and opportunity to really help accelerate companies and teams using AI in a more productive way. 485 00:45:59,997.9455089 --> 00:46:01,827.9455089 So that was incredible. 486 00:46:01,887.9455089 --> 00:46:09,37.9455089 What about you? What did you learn? That's actually a really great lead into my takeaway, which is like, sense of urgency. 487 00:46:09,337.9455089 --> 00:46:11,767.9455089 it's gotta start now if you're not on the boat. 488 00:46:12,157.9455089 --> 00:46:15,577.9455089 jobs are gonna change and they're changing fast. 489 00:46:15,577.9455089 --> 00:46:25,162.9455089 the way that Len talked about getting that sense of urgency, whether you're an IC or a, leader, it's really up to all of us to embrace this change. 490 00:46:25,242.9455089 --> 00:46:26,562.9455089 whether or not we like it, it's here. 491 00:46:27,227.9455089 --> 00:46:29,77.9455089 Yeah, it is here. 492 00:46:30,572.9455089 --> 00:46:33,332.9455089 Sit on this, don't punt it to next quarter. 493 00:46:33,422.9455089 --> 00:46:37,802.9455089 Start now and start thinking about how to transform your pilots into something more tangible. 494 00:46:37,802.9455089 --> 00:46:43,792.9455089 And think, about getting your teams to adopt it and Erin and I hit us up on LinkedIn we love talking about this stuff. 495 00:46:43,792.9455089 --> 00:46:47,402.9455089 We have some experience doing it, so we're happy to, share with you a little bit of what we learned. 496 00:46:48,302.9455089 --> 00:46:48,662.9455089 Yeah. 497 00:46:48,722.9455089 --> 00:46:49,112.9455089 Love it. 498 00:46:49,772.9455089 --> 00:46:51,802.9455089 So thanks to Len for coming on the show. 499 00:46:51,832.9455089 --> 00:46:53,242.9455089 Really interesting conversation. 500 00:46:53,392.9455089 --> 00:46:55,852.9455089 And thanks to everybody who's listening or watching. 501 00:46:56,72.9455089 --> 00:47:08,242.9455089 if you liked what you heard today, please share or give us a like or comment on LinkedIn, We also have full episodes of video on YouTube, which you may be watching now, thanks again for listening. 502 00:47:17,737.9455089 --> 00:47:22,447.9455089 And until next time, let's keep crafting the future of go to market together. 503 00:47:22,892.9455089 --> 00:47:23,342.9455089 Thank you.