Best of LinkedIn: Go-to-Market CW 18/ 19

00:00:00: This episode is provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about go-to market in calendar weeks eighteen to nineteen.

00:00:08: Frenness is a B-to-B Market Research Partner helping ICT and tech providers identify niche channel partners.

00:00:15: by compressing the full journey from identification to qualified first meeting into four to five weeks you can find more info.

00:00:22: Uh, imagine firing your entire fifteen thousand dollar a month outbound marketing team tomorrow.

00:00:30: Just you know.

00:00:31: completely replacing them with twelve lines of text that run totally autonomously.

00:00:35: It sounds wild I know.

00:00:36: Yeah.

00:00:37: and the crazy thing is That isn't some hypothetical scenario for five years down the road.

00:00:42: it's actively happening.

00:00:43: right now For this deep dive, we are unpacking a mountain of insights from the sharpest minds across LinkedIn during calendar weeks eighteen and nineteen.

00:00:51: Absolutely!

00:00:51: And if you're a B-to-B marketing professional This is basically your cheat sheet.

00:00:55: The overarching theme were seeing Is really brutal rapid transition away From bloated manual sauce tool chains

00:01:02: Right into these lean AI native go to market systems.

00:01:05: Exactly I kinda look at it like We moving for a manual transmission car.

00:01:09: You know where have twelve different pedals and levers straight to a self-driving vehicle, but Only if you actually know how to build the engine yourself.

00:01:19: Yeah And to build that engine You basically have to tear out The old dashboard completely because for the past decade marketers have operated under this mindset of you Know whenever you hit a problem You just buy a new tool.

00:01:31: Right,

00:01:32: you buy a tool for data enrichment another one for email and other ones for intent

00:01:36: Exactly.

00:01:38: And the result is this fractured ecosystem where we as humans Just spend most of our time acting as The glue you know manually moving context between all these isolated interfaces.

00:01:49: what We're seeing in the discourse right now Is that total collapse?

00:01:52: That model

00:01:52: it's like hiring twelve different specialized contractors For project but none Of them speak the same language.

00:01:58: You just end up as the stressed out project manager running between them.

00:02:03: But, The new AI native approach feels more like a central nervous system.

00:02:07: The AIs is brain and instead of logging into different dashboards it sends electrical signals basically APIs directly to the limbs.

00:02:16: take action.

00:02:17: That's exactly what Tim Cardin was pointing at recently.

00:02:20: He noted that building a go-to market stack used to require like...a dozen different sauce seeds plus a dedicated ops person just to hold it all together.

00:02:29: Oh, easily!

00:02:29: But now he's replacing that entire infrastructure with Claude Code running thirty-three different APIs from the single terminal.

00:02:37: Wait... thirty three API?

00:02:38: Yeah

00:02:39: From one terminal No graphical user interface no dashboards Just a central language model executing commands across all these endpoints Which

00:02:47: I guess explains Lechec Lamel's recent move.

00:02:50: It was pretty drastic.

00:02:51: He posted about dropping these massive industry giants like Apollo, Zoom Info outreach just completely out of his stack.

00:02:58: Yeah that was a huge statement

00:03:00: Right and the reason he gave is they simply aren't agent friendly.

00:03:03: so he replaced them with eight API driven tools.

00:03:07: And for anyone listening, this concept of being agent-friendly is crucial to understand.

00:03:12: It really is the dividing line right

00:03:14: now because an AI agent struggles if it has to navigate a visual interface built for human like clicking buttons waiting for pages to load.

00:03:22: but If tool is API first The Agent just passes JSON data back and forth instantly.

00:03:28: Right!

00:03:28: It strips away presentation layer entirely.

00:03:30: Oh, really?

00:03:31: Ambrose summarized this brilliantly.

00:03:33: He basically said the twenty-twenty four stack was all about stitching sauce products together and you know calling it a strategy.

00:03:39: right

00:03:39: The classic integration nightmare

00:03:41: exactly.

00:03:42: but the twenty twenty six stack is just code.

00:03:44: You write your intent what you want the system to do an autonomous agents figure out the execution.

00:03:50: You aren't renting a platform that forces you into their workflow anymore.

00:03:53: You actually own at the orchestration.

00:03:55: Yes,

00:03:56: you dictate the exact logic for your specific business.

00:04:00: I do want to push back on that a little bit though, because there's this assumption... ...that consolidating into single-tominole makes everything easier.

00:04:07: Right?

00:04:07: It is just AI now, Trap!

00:04:09: Yeah…I mean wait if Claude was doing everything through thirty three different APIs aren't we swapping visual software bloat for an even more complicated opaque web of

00:04:20: code?!

00:04:21: If an API breaks the average marketer has no idea how read server logs fix it.

00:04:26: Is this actually simpler?

00:04:28: Well, you're touching on the real trade-off here.

00:04:30: It is infinitely simpler and faster to run but exponentially more complex to build than maintain.

00:04:36: Yeah

00:04:37: that makes sense Because

00:04:38: when you have AI fetching data machine to machine You eliminate human context switching.

00:04:43: That drives massive efficiency.

00:04:45: But because the system has built on complex logic streams instead of drag & drop interfaces Traditional marketing skills just aren't enough anymore

00:04:53: Because traditional RevOps people are suddenly locked out of their own systems.

00:04:56: If there's no user interface, the person who specialized in you know configuring Salesforce dashboards or Zapier rules they can't manipulate the central nervous system

00:05:05: Exactly and that gap is forcing the creation of a totally new role.

00:05:09: Right The go-to market engineer...the new architect?

00:05:12: Yeah an Anthocopa shared his stat on this That it just wild.

00:05:15: Postings for the GTM Engineer Role have literally doubled In twelve months.

00:05:21: over three thousand open jobs in twenty-twenty

00:05:24: six.

00:05:24: Wow!

00:05:25: And Koopa was very clear, these are not sales ops people.

00:05:29: they're the technical architects who actually code the infrastructure that generates the pipeline.

00:05:34: but

00:05:34: hiring for that is a nightmare right?

00:05:36: Niraj Kumar pointed this out.

00:05:38: The role lives in the rare Venn diagram overlap that barely exists, you need someone with a legitimate software engineering background who also has deep business context

00:05:47: Right which is so hard to find.

00:05:48: Yeah A traditional developer usually doesn't get B-to-B sales cycles or buyer psychology but brilliant demand.

00:05:54: gen marketer can't write python scripts to maintain thirty three APIs.

00:05:58: But when do you find that overlap?

00:06:00: The leverage is unbelievable.

00:06:02: You're pointing engineering talent directly at revenue generation.

00:06:06: Nate Hall shared a really cool capstone project about this.

00:06:10: He built an automated email quality assurance agent that lives entirely within HubSpot and Asana.

00:06:15: Oh, I saw that!

00:06:16: The mechanics of it are fascinating.

00:06:18: So instead of humans spending thirty minutes parsing a draft checking every UTM parameter making sure the personalization won't break

00:06:25: Right stuff everyone hates doing

00:06:26: Exactly...the GTM engineer scripts a workflow.

00:06:30: where an LLM does it You tag the agent in Asana.

00:06:33: It uses the HubSpot API, tests the HTML validates links and reports back in like sixty seconds.

00:06:41: it saves hours of manual checklists.

00:06:43: but of course the hype around this is creating a lot noise.

00:06:46: yeah Kevin Payne actually issued a warning to operators about this.

00:06:49: Oh!

00:06:49: About people just updating their LinkedIn titles?

00:06:52: Yeah

00:06:52: Before you call yourself a GTM engineer Just to catch up with trend You need be shipping real pipeline systems across five distinct domains.

00:06:59: What were they?

00:07:00: So you need data architecture moving data without loss, sequence systems for the logic trees workflow automation for triggers.

00:07:08: Okay that's three.

00:07:09: then AI Workflow design managing context windows and prompt chaining.

00:07:13: And finally revenue signal interpretation turning raw data into actual intent.

00:07:18: so if revops was The mechanic keeping the factory tools running?

00:07:22: The GTM engineer is the architect designing the fully automated assembly line.

00:07:26: That's

00:07:26: a great way to put it.

00:07:27: but here's An automated assembly line will just produce garbage at light speed if you feed it bad materials.

00:07:34: Oh, absolutely the dirty CRM problem.

00:07:36: right?

00:07:37: Sarah McNamara had a huge warning about this because human SDRs they know to ignore six-month old data Right?

00:07:44: If the account owner is clearly wrong They pause.

00:07:46: they have skepticism

00:07:47: exactly but an AI agent doesn't.

00:07:49: It trusts that dirty crm data and acts on it with absolute confidence.

00:07:58: It's the biggest point of failure right now.

00:08:02: AI doesn't magically fix fundamentally broken business logic, it is a force multiplier!

00:08:08: If your data foundation is pristine – it multiplies revenue….

00:08:12: if you CRM as graveyard …it multiplies chaos.

00:08:15: But wait isn't AI supposed to be smart enough?

00:08:18: clean our messy data for us?

00:08:19: I'm just saying we still have to clean our rooms before that AI robot vacuum will work.

00:08:24: Well yes and no.

00:08:26: Parsing data for cleanup is a totally different architecture than executing an outbound sequence.

00:08:31: If you point and execution agent at a dirty database, it doesn't pause to question the structural integrity of the tables right?

00:08:37: It just does what its told

00:08:38: exactly.

00:08:39: if The Database says someone as a CTO it pitches them as a cto even if they left the company three years ago.

00:08:45: This is why coca sexton advises this brutal audit before you plug in AI.

00:08:49: sixty second test Yes

00:08:50: Can You trace a lead from origin-to outcome In under sixty seconds?

00:08:54: If not, your workflow requires human intervention.

00:08:57: It's held together by Slack DMs and Tribal Knowledge... ...and AI cannot navigate tribal

00:09:01: knowledge."

00:09:02: Which means data governance is suddenly the most critical function of RevOps!

00:09:07: Jeff Ignacio broke this down into four layers that RevOps has to own….

00:09:12: You need a registry documenting what autonomous loops are actually running... Identity controlling.

00:09:17: which AI agent has permission to alter fields in your CRM?

00:09:21: And observability & attribution

00:09:23: Observability to track API costs and error rates, so if an open AI update breaks your logic you know before it sends ten thousand broken emails.

00:09:32: An attribution actually trace the pipeline back into the AI workflow that generated it?

00:09:37: Emerald Patel really summed up philosophy here.

00:09:39: he said You should pay for proprietary data in only orchestration.

00:09:43: don't rent generic platforms.

00:09:45: Because everyone has the same reasoning models.

00:09:46: now, The Claude you use is the same Claude your competitor uses?

00:09:50: The only actual moat Is the highly accurate data You feed it exactly.

00:09:54: so okay once the Data is clean and engine is engineered How does It actually hit the market?

00:10:00: because from the sources it looks like outbound And abm have completely converged

00:10:05: totally.

00:10:06: the old spray-and-pray model is officially dead.

00:10:09: The new competitive advantage is extreme relevance driven by timing and intense signals.

00:10:14: Vanessa Pont shared a story about this that really highlights the shift.

00:10:17: She went from spending ten hours a week grinding on LinkedIn to zero hours, And she booked twelve demos in five days.

00:10:25: That's incredible.

00:10:26: Right

00:10:26: simply by stopping cold outreach.

00:10:28: an only targeting ICP contacts who just showed a buying signal.

00:10:32: Yeah setting up scrapers to monitor triggers like a target account commenting on a competitor's post or posting a job description.

00:10:39: The AIC is the trigger, and you reach out at that exact moment—you're joining a conversation they've already

00:10:44: started.".

00:10:45: And when you automate it across a whole market…it's wild!

00:10:48: Tiberius Serbaneschi posted this efficiency flex — he replaced a fifteen thousand dollar-a month manual GTM team...

00:10:54: I saw that researchers, copywriters, SDRs all gone.

00:10:58: ...replaced by a twelve prompt autonomous system.

00:11:00: It fetches live hiring triggers, passes the data to Gemini to score the intent and if it passes hands-it-to-clawed to draft a copy.

00:11:07: It runs an entire outbound workflow in twenty five minutes... Twenty

00:11:11: Five Minutes To Do Weeks Of Manual Labor.

00:11:14: And this applies to existing Pipeline too!

00:11:17: Kyle Poyer highlighted An AI Native Play For Closed Lost Deals.

00:11:21: That Is Just Brilliant.

00:11:22: Oh

00:11:23: The Continuous Reengagement.

00:11:24: One

00:11:25: Yes Instead of an arbitrary nine month follow up where Arup just says you know Bubbling this to the top of your inbox,

00:11:32: which never works by the way.

00:11:34: Never an agent continuously monitors The account for signals like a new funding round.

00:11:40: when the signal hits It pulls the actual call transcript from nine months ago, identifies this specific objection that killed the deal and drafts an email referencing what has actually changed since then.

00:11:51: Oh

00:11:51: wow so if it was like a missing integration?

00:11:53: The e-mail says hey we noticed your new funding!

00:11:55: And by the way We built that integration we talked about

00:11:57: exactly.

00:11:58: there's a level of hyper relevance That human SDR just can't scale across hundreds of accounts.

00:12:03: And Genevransic mapped this out for twenty-twenty six ABM, sharing a seven layer motion where AI does ninety percent of the orchestration.

00:12:10: Right capturing triggers across whole buying committee routing them and leaving sales to fine tune just the last ten percent.

00:12:17: But this brings up huge question from me.

00:12:19: if every BtoB marketing professional listening right now adopts these hyper personalized AI driven systems What happens to the market?

00:12:29: I mean when everyone's AI is perfectly pitching.

00:12:31: Everyone else's AI Doesn't?

00:12:33: the buyer eventually just become numb to it?

00:12:36: Oh, yeah

00:12:37: That is the inevitable reality of infinite scale.

00:12:39: When the cost of generating a perfect pitch drops to near zero The pitch itself loses its value.

00:12:45: But are no air be shared or really profound insight that answers this?

00:12:49: oh that anthropic

00:12:50: Yes.

00:12:51: And Tropic, the leading frontier AI lab driving this exact shift just hired a human to run their GTM strategy for three hundred and ten thousand dollars a year.

00:13:00: Wait really?

00:13:00: The company building the AI hired a Human to Run Enterprise Sales

00:13:04: Exactly because they know the limitation of their own tech.

00:13:07: AI can score ten-thousand prospects in right flawless emails but it cannot look an executive in the eye and earn deep irrational human trust required to close multi million dollar deal.

00:13:18: AI generates logic not conviction.

00:13:20: Wow, that leaves you with something really important to think about.

00:13:24: You know?

00:13:26: If you enjoyed this episode, new episodes drop every two weeks.

00:13:43: Also check out our other editions on account-based marketing, field marketing

00:13:56: channel.