Best of LinkedIn: Go-to-Market CW 34/ 35

00:00:00: provided by Thomas Allgaier and Frennus based on the most relevant posts on LinkedIn about go to market in CW three, four, three, five.

00:00:07: Frennus is a B to B market research company working with enterprises to optimize their campaigns with account and executive insights far beyond AI.

00:00:16: Welcome to the deep dive.

00:00:18: Really glad to be here with you today.

00:00:19: We're gonna unpack what's happening right now and go to market or a GTM strategies.

00:00:23: We've been looking through, you know, a ton of LinkedIn posts from the last couple of weeks.

00:00:26: late August, early September basically.

00:00:29: Weeks, thirty-four and thirty-five, trying to figure out what B to B leaders and the folks on the ground are really talking about.

00:00:36: That's right.

00:00:36: And our job today really is to boil it all down, find the the pop GPM trends, pull out the key takeaways, basically give you a shortcut to understanding what's actually shifting out there.

00:00:46: You'll hear from the people driving these conversations, hopefully making sense of a field that seems to change well.

00:00:52: daily.

00:00:52: Exactly.

00:00:53: You've got AI, obviously, but also strategy foundations, engineering, lots to cover.

00:00:59: So as you're listening, maybe think about, you know, what does this mean for your GTM efforts?

00:01:04: Let's find out.

00:01:05: Okay, first up, and probably I'm not a huge shocker here.

00:01:09: AI, it's just, it's everywhere in the GTM discussions.

00:01:13: We're seeing this real shift, it feels like, towards an AI native way of operating.

00:01:18: Like it's not just a tool anymore, it's becoming fundamental.

00:01:21: But it's not just talk, right?

00:01:22: People are actually using it.

00:01:23: Oh, absolutely not just talk.

00:01:25: We're way past the theory stage.

00:01:26: We're seeing real field tests, actual results, the insights we saw show AI agents being tested across, you know, whole revenue workflows.

00:01:34: They're seeing productivity gains, though they definitely still need humans watching over them.

00:01:38: Dan Griffiths, for example, he made a really strong point about startups maybe prioritizing an AI powered GTM before hiring a big marketing team.

00:01:45: He talked about a health tech founder who built a lean revenue engine with AI tools in like

00:01:50: weeks.

00:01:52: And got early sales for way less cost than usual.

00:01:56: That's pretty compelling.

00:01:56: So it's not really AI replacing people, it's more like making them better, more efficient.

00:02:02: That's exactly it.

00:02:03: Brandon Short, the signal, he might know him, he laid this out really clearly.

00:02:07: He talked about this collaboration model, you know, humans and AI working together based on how good a fit the customer is.

00:02:16: manual outreach for your top targets, semi-automated for the middle tier, and fully automated for the low-fit

00:02:21: one.

00:02:21: Right, it avoids that kind of black and white thinking.

00:02:23: Exactly, and Vincent Regar put it nicely, he called AI a turbocharger, not the engine.

00:02:28: You can't just, you know, sprinkle AI onto your old ways of doing things and expect magic.

00:02:33: You need clarity, connection, the right culture.

00:02:35: Makes sense.

00:02:36: And if you look bigger picture, Jonathan MK's analysis.

00:02:40: This was sobering.

00:02:42: He found that despite all the money pouring into AI infrastructure, something like ninety-five percent of pilots fail.

00:02:47: Ninety-five percent?

00:02:49: That's huge.

00:02:50: Why is the tech not ready?

00:02:52: It's usually not the tech itself.

00:02:53: It's how companies are trying to use it.

00:02:56: The successful ones.

00:02:57: They focus on strategic partnerships, training people for specific roles, redesigning workflows.

00:03:02: It's implementation.

00:03:03: Okay.

00:03:04: Implementation.

00:03:04: So it's not plug-and-play.

00:03:05: Not at all.

00:03:06: Which leads to an even more radical idea from Tammy Graham.

00:03:10: She suggests that maybe traditional GTM models are just becoming obsolete in this age of inference, as she calls

00:03:19: it.

00:03:19: Age of inference.

00:03:20: What does she mean by that?

00:03:22: Well, she argues that LLMs, the large language models, are increasingly the ones deciding what products even get seen.

00:03:29: AI search might just bypass websites altogether.

00:03:32: So if your content isn't readable, understandable by these AI systems.

00:03:36: You're

00:03:36: invisible.

00:03:37: Basically, yeah.

00:03:38: Invisible.

00:03:38: Okay, that's a bit scary.

00:03:40: So how do you even build for that reality?

00:03:42: It changes everything, doesn't it?

00:03:44: Jonathan Mose talks about AI native GPM systems.

00:03:48: These things leverage what he calls compound intelligence.

00:03:51: They learn from every click, every deal, and scale up predictive insights really fast.

00:03:56: He actually noted that AI native companies, say under twenty five million dollar ARR, can run their GTM teams with like thirty eight percent fewer people.

00:04:05: Thirty percent fewer.

00:04:06: I think growing just as fast.

00:04:07: Exactly.

00:04:08: It's about giving people, you know, superpowers, not replacing them.

00:04:12: Right.

00:04:12: But it's also not one size fits all.

00:04:14: Maja Voje pointed out that GTM for AI agents is totally different from GTM for SAAS.

00:04:20: She mentioned sales led growth, ecosystem partners, flexible pricing, especially before you hit product market fit with these agents.

00:04:26: Okay.

00:04:27: So a different product needs a different GTM playbook.

00:04:29: Makes sense.

00:04:30: What about the downsides, the practical challenges people are hitting?

00:04:32: Yeah.

00:04:33: Good question.

00:04:33: Ryan Staley ran an experiment.

00:04:35: with an AI agent, Manus, I think it was called, it did some cool things, like finding viral LinkedIn stuff, building templates, impressive.

00:04:42: But he was also upfront about the limitations, data accuracy issues, privacy concerns, things like that.

00:04:47: Ah, okay, usual suspects.

00:04:49: Right, which just highlights again, it's the implementation strategy that makes the difference, not just having the tech, so.

00:04:56: Really, the big takeaway on AI, it's not just a new tool.

00:04:59: It's forcing a rethink of the entire GTM model.

00:05:02: Human augmentation, strategic engineering, the how is way more important than the what.

00:05:08: Focus on the how, the strategic building.

00:05:10: That leads us perfectly into the next big theme.

00:05:12: we saw bubbling up.

00:05:14: GTM engineering.

00:05:15: Sounds pretty technical.

00:05:16: Like building a revenue factory or something.

00:05:18: What's that all about?

00:05:19: Yeah, it's a concept that's really gaining steam.

00:05:22: GTM engineering.

00:05:23: Think of it as the discipline of building the actual revenue systems, fusing data, automation, AI, across both marketing and sales.

00:05:31: Varun Chabra and Mr.

00:05:33: Sharma, Varun Anantu, they made this provocative point.

00:05:36: Your GTM isn't understaffed, it's under-engineered.

00:05:38: Under-engineered?

00:05:39: Ouch.

00:05:39: Yeah.

00:05:40: And more it's wonder.

00:05:41: Jim took it even further, he suggested one good GTM engineer could potentially outperform five SDRs by twenty twenty five.

00:05:47: Five SDRs?

00:05:48: Wow.

00:05:49: by building these sophisticated systems, you know, automating lead gen workflows, monitoring social signals, automating CRM stuff.

00:05:57: Okay,

00:05:57: that's a bold vision, but I imagine not everyone's completely sold on this whole GTM engineer label, are they?

00:06:03: No, definitely not.

00:06:04: And it's a good debate to have, actually.

00:06:07: Edward Epod, for instance, he's a bit skeptical.

00:06:10: He suggested it might be partly a marketing push by companies like

00:06:13: Clay.

00:06:13: Selling complexity, maybe making teams feel like they're behind if they don't have one.

00:06:18: He's more for AI that makes complexity just disappear.

00:06:21: Plug and play agents, zero code intelligence.

00:06:24: That's

00:06:24: a fair counterpoint.

00:06:25: Is it just a rebrand or are we just shuffling deck chairs, maybe calling Revop something new?

00:06:30: What's the real difference?

00:06:31: It's the key question, isn't it?

00:06:32: Some people, like Steve Traviglini, do see it as basically rebops or sales ops with a new coat of paint.

00:06:38: Product market fit is still king, he'd argue.

00:06:40: But then you have practitioners like Rebecca Shimala over at Built.

00:06:43: They're actually doing GTM engineering, building signal-based outbound triggers, automated lead scoring, using AI for copy.

00:06:51: And they're seeing real efficiency gains, real impact.

00:06:55: So there are tangible results for some folks.

00:06:57: Exactly.

00:06:58: And Heath B drew a really clear line.

00:07:00: He said RevOps manages the machine, but GTM engineering builds the machine.

00:07:05: It's focused on experimentation, integration, automation, getting those deep insights.

00:07:12: It's not just growth.

00:07:12: It's smarter growth, better unit economics.

00:07:15: Building

00:07:15: versus managing.

00:07:16: Okay, I get that distinction.

00:07:17: So if someone's thinking, okay, this sounds interesting.

00:07:20: Where do they even start with GTM engineering?

00:07:23: Richard Makara offered a nice starting point.

00:07:25: He emphasized understanding the basics first, the core mechanics of demand and supply.

00:07:30: He has this acronym, pull project, urgency, looking.

00:07:34: Lacking understanding your buyers deeply before you try to engineer repeatable processes.

00:07:39: So know the fundamentals before you automate.

00:07:41: Precisely.

00:07:41: It's about being intentional, finding the real growth lever.

00:07:44: So yeah, whether you call it GTM engineering or just super smart rev ops, the trend is clear.

00:07:49: To scale effectively now, you need to deliberately build and refine those growth systems.

00:07:53: Okay, so we've hit the cutting edge AI native GTM engineering revenue systems, but you know, all this fancy tech.

00:08:02: It doesn't mean much without a solid foundation, right?

00:08:04: Yeah.

00:08:05: What are people saying about getting back to those core principles, the stuff that never changes?

00:08:09: That's a really important point that came through loud and clear.

00:08:12: A lot of posts were stressing a return to fundamentals, especially getting laser focused on your ideal customer profile, your ICP, plus your positioning and messaging.

00:08:21: Alexander Esner had this great checklist, like really nail down the job position, the company type, the product attributes for your ICP, define your category clearly for positioning, and craft a really crisp value prop.

00:08:34: for your messaging.

00:08:35: Get those wrong, and you're kind of doomed from the start, no matter the tech.

00:08:38: Right.

00:08:39: And what are the warning signs?

00:08:40: How do you know if those foundations are maybe a bit shaky?

00:08:43: TK Kader pointed out three classic signs of a dodgy SaaS GTM plan.

00:08:48: One, deals always getting stuck somewhere in the middle of the funnel.

00:08:51: Two, saying yes to literally every single inbound demo request.

00:08:55: Ah,

00:08:56: the tire kickers.

00:08:57: Exactly.

00:08:58: Waste so much time.

00:08:59: And three, relying too heavily on referrals or existing customers without a steady stream of new leads.

00:09:05: He really pushed for a strong ICP and investing in founder-led marketing early on.

00:09:10: Makes sense.

00:09:10: And Marlon Davis highlighted a related issue.

00:09:13: Only about thirty-six percent of product managers felt their features were consistently well received.

00:09:17: That means stalled adoption, lost revenue.

00:09:20: Plus, Swetha S talked about GTM Death Valley, hashtag three, that painful spot where you can sell the product, but you can't actually deliver it properly.

00:09:28: Oof, that kills trust fast.

00:09:30: Instantly,

00:09:31: leads to high churn.

00:09:31: Just a reminder that sales and delivery have got to be locked in step.

00:09:35: Absolutely critical.

00:09:36: So, okay, those are the pitfalls.

00:09:38: What's the positive path?

00:09:40: How do you build that solid foundation?

00:09:42: Pedro Pinto laid out a pretty comprehensive GTM framework.

00:09:45: It's all about sustainable growth principles.

00:09:47: Things like treating GTM as one cohesive cross-functional engine, embracing a hybrid model, maybe blending product-led growth PLG with sales-led motions, and using AI and real-time data to keep your ICPs dynamic, not static.

00:10:01: Dynamic ICPs, interesting.

00:10:03: Yeah, and crucially, prioritizing customer success over just chasing the latest shiny object or trend.

00:10:10: Alon even also warned against complexity.

00:10:12: He said it often just hides problems and slows things down.

00:10:15: He used Stripe as an example of beautiful simplicity.

00:10:18: Clear ICP, developers, clear message, strong PLG strategy.

00:10:22: Right, focus.

00:10:23: Exactly.

00:10:24: Focus is the cure for complexity syndrome, he said.

00:10:27: Focus on the right GTM, the right buyers, the right message, the right execution.

00:10:31: And Heidi Hattendorf suggested using a market investment map to make sure you're putting resources behind your highest value products, not spreading yourself too thin.

00:10:39: So the core message seems to be, technology amplifies, but it doesn't replace strategy.

00:10:43: Get the fundamentals who, what, why absolutely right first?

00:10:47: Then, once that strategy is solid, it's game time.

00:10:51: Execution.

00:10:52: What were the key takeaways there for getting things done well?

00:10:54: Execution is where it all happens, isn't it?

00:10:57: And the big thing here was speed, speed to insight, speed to execution.

00:11:00: It consistently beats trying to get everything perfect before you launch, especially when you're starting out.

00:11:06: Chris Elshakey and Adam Jay both made this point really strongly.

00:11:09: Launch fast, figure out what's broken, iterate like crazy.

00:11:12: Learn

00:11:13: by doing.

00:11:13: Exactly.

00:11:14: Adam Jay basically said the market rewards action, not perfection.

00:11:18: He even suggested a simple four week plan.

00:11:21: Pick accounts, start outbound, iterate scale.

00:11:24: I like that.

00:11:25: Four weeks just get moving.

00:11:27: How does that translate into the tools and processes teams are actually using for this fast iteration?

00:11:32: Well, integrated execution is key.

00:11:34: Bill Stathopoulos talked about a minimum viable GTM stack.

00:11:37: Three essential layers, outreach, data, and AI.

00:11:40: He mentioned tools like Instantly.ai for outreach, Clay for orchestrating data, Lavender for AI personalization, the goal being to potentially ten X meetings booked without getting bogged down in too many tools.

00:11:52: Yeah.

00:11:53: And Alex Faca proposed this cool idea of a flywheel GTM system, where every channel actually feeds the others, like you turn your best content into ads, then you use the engagement data from those ads to feel really personalized outbound sequences.

00:12:07: Ah,

00:12:08: so it's not cold outbound anymore.

00:12:09: Exactly.

00:12:10: He shared an example where a post about clay automation led to a twenty percent reply rate on the follow-up.

00:12:16: outbound because people had already engaged.

00:12:18: It connects the dots.

00:12:19: That's

00:12:19: smart.

00:12:19: Very integrated.

00:12:20: Yeah.

00:12:21: What about content itself?

00:12:22: Is it better to go fast or be precise?

00:12:25: Anil Antony tackled that classic debate.

00:12:27: Content velocity versus content precision.

00:12:30: His take?

00:12:31: use a barbell approach?

00:12:32: Barbell.

00:12:32: Like weights.

00:12:33: Kind of.

00:12:34: Use lots of quick, frequent content.

00:12:36: that's the velocity to get rapid feedback, build awareness, and then have fewer, really high stakes, polished pieces.

00:12:43: that's the precision for building deep credibility and driving conversion.

00:12:47: Got it.

00:12:47: Know when to go fast, when to slow down.

00:12:49: Right.

00:12:50: And often using AI to help scale the drafting process for that velocity piece.

00:12:54: And then Todd Handy, he was referencing Dave Boyce, argued for moving beyond just thinking about sales methodology.

00:13:00: He advocated for a full revenue operating moral, aligning product, marketing, sales, customer success into one revenue factory.

00:13:08: Revenue factory, I like that.

00:13:10: Yeah, focusing on dual transformation, the classification of media, really honing in on metrics like CAC and LTV for sustainable recurring revenue.

00:13:19: Okay, all this focus on AI engineering.

00:13:23: iteration, although it comes back to data, doesn't it?

00:13:25: Yeah.

00:13:26: How are GTM teams actually using data and analytics in smarter ways?

00:13:30: Data

00:13:30: is definitely the fuel.

00:13:32: And signal-based GTM is really front and center, using those real-time inputs, buying signals, intent data to figure out the next best action and create this kind of compounding learning loop.

00:13:41: Nate Suter talked about the absolute priority of bridging the GTM and data teams, turning analytics into actual pipeline and retention.

00:13:48: He sees speed to insight as a key leading indicator of how healthy your early GTM is.

00:13:52: It's not just having data.

00:13:54: It's how fast you can act on it.

00:13:55: And this is changing the tech stack itself, right?

00:13:58: Moving beyond just reporting dashboards.

00:14:00: Oh, completely.

00:14:01: Manny Iyer described how modern AI GTM stacks are shifting, moving away from traditional ABM platforms towards what he called signals hubs.

00:14:11: Is it those

00:14:11: hubs?

00:14:12: Yeah.

00:14:12: These hubs basically pull together all your signals, first party, second party, third party buying signals, then they filter them against your ICPs and personas, make sure the data is clean.

00:14:22: and push it out to sales.

00:14:24: It's a total rethink of how data flows and actually informs strategy in real time, much more dynamic.

00:14:29: Okay, that makes sense.

00:14:31: So with all these shifts happening, people must be wondering about the specific tools making waves.

00:14:36: What stood out in the B to B GTM tech landscape recently?

00:14:39: Well, we mentioned AI agents already, like Ryan Staley's Manus experiment, showing promise but needing careful direction.

00:14:46: Lechec Lamel is apparently doing comparisons right now, testing GPT-V, Claude Gemini on real GTM tasks to see how they stack up practically.

00:14:54: Interesting,

00:14:55: real world bake-off.

00:14:56: Exactly.

00:14:57: And Karina Gersberg's case study on NeuroBudget.ai, that's an AI personal finance coach, showed a really lean data-driven GTM for an AI agent in action.

00:15:08: It seems like the tools getting attention aren't always the flashiest ones, though.

00:15:11: Sometimes it's the plumbing.

00:15:12: That's a great point.

00:15:13: Often the most underrated tools are the orchestration platforms, the ones connecting everything.

00:15:19: Matteo Titarelli highlighted Wesley Hong, he co-founded Simit and Akiza, Huang named Octid as a super powerful, but maybe underrated, GTM engineering tool, right alongside Clay.

00:15:30: Okay, Octive.

00:15:31: Yeah, it speaks to the importance of that underlying orchestration for making complex workflows actually work, which then brings up How do companies choose these tools effectively?

00:15:40: Laurence B shared some interesting insights there.

00:15:42: Apparently, once startups get past, say, a twenty million dollar ARR, they stop relying just on what their peers recommend.

00:15:48: They get more formal about

00:15:49: it.

00:15:49: Exactly.

00:15:50: They treat AI tool selection more like enterprise procurement, structured evaluations, industry benchmarking, often finding tools at industry events, and this more strategic approach.

00:16:00: It correlates directly with better results.

00:16:02: That's a really key insight for scaling companies.

00:16:04: Mature your procurement.

00:16:07: What about tools for getting quick answers from data?

00:16:15: It basically acts like a conversational AI analyst.

00:16:19: You can ask it critical revenue questions like, why is churnup or where should we invest more?

00:16:24: And it

00:16:24: just tells you.

00:16:25: Yeah,

00:16:25: it gives you instant answers, supposedly replacing weeks of back and forth with an analyst team, like having an analyst on speed dial.

00:16:32: Wow,

00:16:32: that could be powerful.

00:16:33: Okay, so beyond the internal tech and systems, what about external factors?

00:16:38: How are partnerships and different routes to market fitting into this evolving picture?

00:16:43: Yeah,

00:16:43: you can't do it all yourself.

00:16:44: Right.

00:16:45: Partnerships are definitely coming up as crucial, especially for getting AI initiatives off the ground or commercializing these new AI agent products.

00:16:53: Syed Sabine Nadeem made a really clear distinction, particularly in FMCG, between go to market, which is about creating demand, and route to market, which is delivering the supply.

00:17:02: Demand versus supply chain.

00:17:04: Exactly.

00:17:05: You need both to win market share.

00:17:06: And for really deep tech, Ken Chen outlined different strategies.

00:17:10: vertical discretion, horizontal specialization, or maybe a hybrid approach to drive revenue.

00:17:16: It just underlines that it's not just what you build, but how you get it to customers and who you partner with along the way.

00:17:23: Makes total sense.

00:17:24: Okay, final theme.

00:17:25: None of this happens without leadership and the right organizational setup.

00:17:29: What are leaders focusing on to navigate all this change?

00:17:33: Leadership mindset is huge.

00:17:35: Sangram Vajra shared some really powerful stuff on overcoming limiting beliefs.

00:17:39: He listed like fifteen mindset shifts for GTM leaders.

00:17:43: Things like prioritizing all in and all, focusing on margins revenue and building community commodity.

00:17:47: The margins of revenue, interesting shift.

00:17:49: Yeah, it's about cultivating that winning mindset.

00:17:51: Being bold, not driven by fear so you can actually embrace these new ways of working.

00:17:55: That mindset is critical.

00:17:57: But what about the structure?

00:17:59: Scott Travis highlighted a pretty worrying trend.

00:18:01: the short lifespan of CROs in B to B saws, like, seventeen months.

00:18:06: Yeah, it's a tough gig.

00:18:08: He attributes that revolving door largely to chaotic GTM setups.

00:18:13: He really emphasized the need for genuine cross-functional alignment in systems thinking.

00:18:18: It's got to be more than just adopting a sales methodology.

00:18:21: You need a sustainable growth engine.

00:18:24: The whole system has to work together.

00:18:25: Absolutely.

00:18:26: And Liza Adams, she was talking about the GTM twenty twenty five event basically said departmental silos are just breaking down.

00:18:33: They have to.

00:18:34: teams have to work together, align on business outcomes, especially Now with AI in the mix, her quote was great.

00:18:41: AI doesn't care about her silos and neither do our customers.

00:18:44: So that collaborative spirit where knowledge flows where it's needed, that's becoming the standard.

00:18:50: Which

00:18:50: then loops back to that GTM engineering question, doesn't it?

00:18:53: Marcus Stahlberg raised the point.

00:18:54: If GTM engineering reports to the CRO, not marketing, even though there's overlap.

00:19:00: Does that create new silos?

00:19:02: It's a really valid question.

00:19:03: What does that mean for a marketing's future role?

00:19:05: It's a challenge lots of organizations are wrestling with right now.

00:19:08: It forces a real organizational rethink.

00:19:10: so If we try and pull all these threads together, what really stands out, I think it's that GTM success today isn't about having a perfect static plan.

00:19:20: And it's not just about adding the latest tech gadget.

00:19:23: It's really about building these adaptable, intelligent revenue systems, systems that use AI, use engineering principles, but stay absolutely focused on fundamental buyer needs and critically fostering that deep cross-functional alignment across the whole company.

00:19:38: Well said.

00:19:39: So.

00:19:40: Maybe the provocative thought for when listening is this, as you look at your own GTM, are you genuinely building that kind of compounding AI native engine, one that learns and adapts faster than your competitors?

00:19:51: Or are you still maybe relying on older models that could potentially be making you invisible in this new age of inference we talked about?

00:19:57: Something to think about.

00:19:59: Well, if you found this deep dive useful, remember we drop new episodes every two weeks and definitely check out our other editions covering account based marketing, field marketing, channel marketing, mark tech, social selling and AI in B to B marketing.

00:20:10: Thank you so much for spending your time with us today waiting through all these GTM insights.

00:20:14: Yeah, thanks for tuning in and don't forget to subscribe so you catch the next shortcut to staying well informed.