Best of LinkedIn: Go-to-Market CW 22/ 23

00:00:00: This episode is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about GoToMarket in calendar weeks twenty-two to twenty three.

00:00:09: Frennes is a BDB market research partner helping ICT and tech providers identify niche chow partners by compressing their full journey from identification into qualified first meeting for four or five weeks.

00:00:22: You can find more info at the description.

00:00:24: So imagine finding out that your company's highest paid, new hire like a top tier systems engineer is actually just functioning as an expensive archaeologist.

00:00:34: An

00:00:34: expensive archaeologists I love

00:00:35: them right?

00:00:36: Just spending all day digging through the minds of your sales reps trying to figure out how your company actually sells.

00:00:42: yeah That's exactly what we're getting into today.

00:00:44: welcome to our latest deep dive.

00:00:46: We are unpacking atop go-to market trends from across LinkedIn over last two weeks and Well, we're going straight past the buzzwords today.

00:00:54: Yeah no fluff

00:00:55: exactly.

00:00:56: We want to look at the actual mechanics of what's working right now because you know it's a really critical time To look at those mechanics.

00:01:02: Oh absolutely I mean if you sift through The insights shared by B-to-B operators Right Now You realize?

00:01:08: We are in the middle Of this This massive structural shift.

00:01:13: Teams aren't just changing their tools anymore They Are Completely Rearchitecting.

00:01:18: How A revenue Engine Even Runs.

00:01:20: And so our mission for this conversation is really just to cut through all the noise.

00:01:24: I mean, we all know AI as the biggest buzzword in BW right now.

00:01:27: undeniably

00:01:28: but frankly if you're listening to this You really don't need another lecture on why AI is the future?

00:01:34: You know that you need to know how high performing teams are deploying it today without Completely breaking their current pipeline.

00:01:41: Yeah Without blowing everything up.

00:01:43: So let's get right into that structural shift.

00:01:45: The conversation online has completely pivoted away from you know, buying software to this new concept of engineering systems.

00:01:53: All right there's a specific title attached I mean, a thirteen X increase.

00:02:08: that isn't a trend.

00:02:09: That is companies suddenly realizing they have a massive gap in their infrastructure.

00:02:13: it absolutely Is.

00:02:14: and well what's most revealing?

00:02:16: How companies are actually valuing that cap?

00:02:19: so Neil Vithlani recently analyzed nearly two hundred job postings for GTM engineers Yeah just to see what the market is actually paying For this skill set And he found that the compensation is entirely bimodal like there is no middle ground

00:02:34: at all.

00:02:35: wait What does that mean in practice?

00:02:37: Well, you have one cluster of people making two hundred thousand dollars working in-house and then another cluster.

00:02:43: Making maybe two thousand a month running campaigns from overseas.

00:02:47: That is a massive gap right

00:02:49: And the dividing line really comes down to mechanics.

00:02:52: Are you just pushing buttons into software tool somebody else built or are you architecting The actual revenue system that the tool operates within?

00:03:00: Oh man!

00:03:01: That distinction Is everything Right now.

00:03:03: It's kind like building A smart home You know, well a lot of companies are rushing to hire an electrician.

00:03:08: To plug in a few smart bulbs and they think great we're AI first company.

00:03:12: now right

00:03:13: check-in the box

00:03:14: exactly.

00:03:15: but The people commanding those massive salaries?

00:03:19: They're the architects there the ones designing the entire electrical grid from the ground up so that when you plug In A new piece Of tech the house doesn't just short circuit.

00:03:29: That analogy maps perfectly onto how Brandon Redlinger breaks down the reality of this role.

00:03:34: Oh, he had those three levels right?

00:03:35: Yeah!

00:03:36: He identified THREE distinct levels of GTM engineers out there right now.

00:03:40: So at the one-on-one level you have The Workflow Builder.

00:03:43: Okay This is a person just living inside the CRM asking How do I automate these ONE specific tasks?

00:03:49: so my SDR saves five minutes.

00:03:51: Just basic efficiency stuff

00:03:52: Exactly.

00:03:53: Then you move up to the two oh one level, The Systems Builder.

00:03:55: They look across functions.

00:03:57: so marketing sales customer success.

00:04:00: and they ask how data flows end-to-end

00:04:02: getting a bit more holistic.

00:04:03: right

00:04:04: but those architects he mentioned they sit at the three Oh One Level.

00:04:07: Rendlinger calls them Revenue Architect

00:04:09: Revenue architect.

00:04:11: that sounds expensive.

00:04:12: It is because they are designing the underlying data infrastructure with AI orchestration baked in from day one.

00:04:19: They're building a machine that basically adapts to market changes automatically,

00:04:23: which it's wild to think about and to visualize what?

00:04:26: That three oh-one level actually looks like in practice.

00:04:28: Shashank Chandrasekharan shared this concept of The GTM workstation of twenty.

00:04:33: twenty six.

00:04:33: Oh the conband board thing.

00:04:35: yeah he says we won't be hiring traditional, massive go-to market teams anymore.

00:04:40: Instead you build a localized workstation.

00:04:43: he describes it like a Hermes conband board running right on your laptop.

00:04:46: okay so how does that work without a huge team?

00:04:49: So...you have columns for every stage of your outbound motion.

00:04:54: but instead if humans moving the deals just drop in cards and AI agents pick them up

00:04:59: wow

00:05:00: Yeah, so one agent is tasked with sourcing the accounts.

00:05:03: Another qualifies them based on your proprietary criteria another drafts a copy and human operator just sits above it all supervising board managing exceptions.

00:05:12: And if you think about mechanics of making that Kanban Board actually function.

00:05:17: It explains exactly why that revenue architect is pulling a two hundred thousand dollar salary.

00:05:22: Right, for sure!

00:05:23: Because you cannot just drop an autonomous AI agent into a messy undocumented process and expect it to magically generate revenue.

00:05:30: You have design the logic set the rules of engagement map data flows first.

00:05:35: Otherwise The Agent just executes bad strategy at light speed Exactly

00:05:38: Just doing wrong things much faster.

00:05:41: But

00:05:41: wait I have push back here for second because A thirteen X growth in twenty months That sounds a lot like panic hiring

00:05:48: to me.

00:05:49: Oh, totally!

00:05:49: Like if you're VP of sales or CMO listening to this are worried that companies are just sprinting blind into it?

00:05:56: Are we paying top dollar to engineer bigger mess?

00:05:59: That is the exact risk.

00:06:01: and Eva Christine Reader called out brilliantly.

00:06:03: she warned that a lot of companies are hiring these advanced GTM engineers one step too early Too

00:06:08: early.

00:06:08: in what sense?

00:06:09: Well, her argument is that if you haven't codified your go-to market knowledge before the engineer arrives things are going to stall immediately.

00:06:17: I mean, think about it.

00:06:18: If your winning sales process still lives entirely in the heads of your two best account executives there's no logic to automate Right!

00:06:26: The AI can't read their minds Exactly

00:06:28: So you bring this highly paid systems builder and as she put they just become an expensive archeologist.

00:06:35: There is that phrase again.

00:06:36: Yes They spend their first two quarters Just digging through Slack messages interviewing reps, trying to unearth the institutional knowledge that the team never bothered to write down.

00:06:49: Man expensive archaeologists is such a visceral way to describe that failure.

00:06:53: and honestly digging through the minds of your sales reps is bad enough but digging through a bloated tech stack is even worse.

00:06:59: oh without a doubt.

00:07:01: which brings

00:07:01: us to the operational reality teams are facing right now.

00:07:04: yeah because if you're foundation is flawed throwing more ai tools on top of it isn't a strategy.

00:07:10: Lissy Maggis came out with a very blunt assessment of this.

00:07:13: What did she say?

00:07:13: She declared that the Forty Tool GTM stack is officially dead!

00:07:17: Wow,

00:07:18: just

00:07:18: dead... Yep The highest performing teams she's observing are actually ruthlessly subtracting tools right now.

00:07:26: They're going deep on just eight foundational essentials, so platforms like Notion, Slack and Claude

00:07:33: Which completely reverses the narrative.

00:07:35: we've been fed for what?

00:07:36: last five years?

00:07:37: Oh yeah where buying another point solution was supposed to magically increase your pipeline?

00:07:41: Exactly an Eon McGinnis analyzed this made a really crucial distinction.

00:07:45: He noted that most teams today do not actually have a pipeline problem.

00:07:50: OK, then what do they have?

00:07:51: They

00:07:51: have a leak problem.

00:07:52: A leak

00:07:52: problem

00:07:53: Yeah.

00:07:53: so if your ideal customer profile definition is fuzzy or you're speed to lead routing takes hours instead of minutes You just bleeding opportunities.

00:08:02: Buying a forty first tool to dump more leads into a leaky bucket doesn't fix the bucket?

00:08:07: No!

00:08:08: Of course not.

00:08:09: Let's talk about what those leaks actually look like mechanically because Siranya Roy pointed out something that I think will hit really close to home for a lot of listeners.

00:08:17: Your go-to market system rarely breaks down in the big obvious moments, it doesn't usually fail during Completely unowned handoffs.

00:08:31: Yeah, it's a mid-quarter territory revision where list of accounts just gets orphaned.

00:08:36: Oh that happens all the time.

00:08:38: or It's a quick undocumented patch someone makes in Salesforce on a Friday afternoon That quietly breaks all your lead routing

00:08:44: rules huh?

00:08:46: Over time All those tiny patches create this shadow system that just drags Your entire revenue engine to a halt

00:08:53: which really requires a fundamental shift.

00:08:55: and how leadership views operations?

00:08:58: Rosalind Sanelana provided a very necessary reality check for RevOps leaders on this.

00:09:03: What was her take?

00:09:04: She said, your tech stack is the symptom not strategy.

00:09:07: if you're pipeline of stalling buying the newest AI sales engagement platform will not solve the underlying dysfunction.

00:09:13: You just giving disfunction to faster engine

00:09:15: exactly and Zilshaw actually noticed that exact dynamic playing out at recent GTM meetup.

00:09:21: Yeah, he said everyone in the room wanted to talk about advanced AI workflows and automated outbound sequencing.

00:09:27: But he pointed up that almost none of the issues these teams were facing where actually automation problems

00:09:33: They're process problem

00:09:34: Right!

00:09:34: Foundational data infrastructure issue If you have terrible signal quality or your email deliverability is completely shot The smartest language model on world still going fail.

00:09:46: Yeah,

00:09:46: I struggle with this next point though because everyone is selling AI as the ultimate solution to literally everything.

00:09:53: But Anna Erson suggested something highly contrarian.

00:09:57: She argues that despite all the hype your core GTM system should remain ninety-five percent deterministic and manual until they are absolutely proven.

00:10:07: But how does a deterministic system even survive when the whole industry is pushing probabilistic large language models?

00:10:14: Like, How do you balance that in a real revenue engine?

00:10:17: well it comes down to understanding The mechanics of how these models actually work.

00:10:20: Let's use an analogy.

00:10:21: A Deterministic System Is like a train on a track.

00:10:25: It runs On hard rule-based logic.

00:10:27: You know if x Happens?

00:10:29: Do y?

00:10:30: you Know Exactly Where the Train is Gonna end up Every Single Time?

00:10:33: Very Safe

00:10:33: exactly.

00:10:34: An LLM on the other hand is probabilistic.

00:10:37: It's essentially an off-road vehicle, it analyzes the terrain and guesses next best move Now that off road might find a brilliant faster route to destination but without guardrails.

00:10:56: Erson's point is that you use the offer at Vehicle, The AI to ideate.

00:11:00: To draft content and accelerate research.

00:11:03: but the underlying track...the routing..the data compliance....The core logic has to remain deterministic.

00:11:09: That makes total sense.

00:11:10: You need human built tracks to keep machine going in right direction And Maja Voja had a really sharp critique related to this.

00:11:15: What did

00:11:16: she say?

00:11:16: She noted when companies abandon those human tracks and just let AI run wild ...you end up with what she calls AISlop.

00:11:24: Ugh!

00:11:24: AI Slop Perfect term for it.

00:11:26: Right, she sees all these twenty-twenty six GTM strategies that have this impressive technical structure but absolutely zero conviction.

00:11:35: The messaging just sounds like every other vanilla AI tool on the market because

00:11:39: It's written by the same models.

00:11:41: exactly.

00:11:42: yeah to fix That you don't need To write a better prompt.

00:11:46: You Need Human Brains?

00:11:48: the strategic beachhead and actually shape the proprietary point of view.

00:11:52: Yeah,

00:11:52: we really have to recognize that while we are obsessing over fixing these internal systems and cleaning up our data The external reality has shifted dramatically.

00:12:02: It's a completely different world out there.

00:12:04: You can engineer the perfect train track internally?

00:12:06: But it is completely useless if the buyer's no longer waiting at

00:12:10: this station.

00:12:11: That is exactly what we're seeing, The behavior of BDB Buyer has fundamentally changed and its just breaking the metrics that we've relied on for a decade.

00:12:18: Adam Schoenfeld brought up a brilliant mechanical reality.

00:12:21: todays buyer.

00:12:23: We could debate AI hype all day but the fact is buyers do not fill out forms anymore.

00:12:28: They really don't.

00:12:33: Half the time, The Human Buyer doesn't even read your website.

00:12:36: Their AI agent does it for them.

00:12:38: Wow!

00:12:39: Think about that.

00:12:40: from a tracking perspective A buyer tells their AI Go to these five vendor websites Extract their pricing models And tell me which one integrates with our ERP.

00:12:51: Right and when you map out the mechanics of interaction You see why traditional tracking completely falls apart.

00:12:56: Kevin Payne highlighted this.

00:12:58: He pointed out that MQL volume marketing qualified leads and email open rates have lost almost all their predictive

00:13:05: power because of the bots.

00:13:06: exactly if an AI agent hits your website scrapes, the pricing data in milliseconds And leaves it doesn't trigger a form fill to your legacy software.

00:13:14: That just looks like about

00:13:15: even though they're actually evaluating you

00:13:17: right?

00:13:18: In reality You were just shortlisted for a massive deal.

00:13:25: scan and summarize it for the buyer.

00:13:27: Oh,

00:13:27: so triggers the tracking pixel?

00:13:28: Yes!

00:13:29: Your SDR thinks they have a hot lead but human eyes never actually saw.

00:13:44: Paine suggests we have to start measuring entirely new concepts.

00:13:47: The first is answer share.

00:13:48: Answer Share, like share of voice?

00:13:50: Kind

00:13:50: of but it's a shift from share or voice.

00:13:53: when a buyer asks their AI model to compare vendors in your specific category how often does your company actually show up in the output?

00:14:01: are you part of the AIs?

00:14:02: answer oh

00:14:03: that's fascinating.

00:14:04: and the second metric is agent intervention.

00:14:06: rate this measures.

00:14:07: How Often a Human Actually Steps In To Correct guide or respond to an AI workflow.

00:14:13: Okay, it makes sense

00:14:14: because if your AISDR sends five hundred emails before nine AM that volumetric is totally meaningless.

00:14:21: you only care about the moments where a human buyer intervened and actually engaged.

00:14:26: And If buyers are using AI to ruthlessly filter out the noise like that It means their tolerance for risk in there.

00:14:32: tolerance for long drawn-out sales cycles Is just gone completely

00:14:35: gone.

00:14:36: Mark Roberge validated this.

00:14:38: He recently met with a hundred and seventy eight founders in GTM leaders in San Francisco, And the takeaway was incredibly clear.

00:14:45: The era of the proof-of concept is dead

00:14:48: Really?

00:14:49: Dead!

00:14:49: Yeah Buyers today demand proof of outcome.

00:14:52: They do not care about the technical feasibility of your tool anymore.

00:14:56: they care About measurable business impact within the first ninety days.

00:14:59: That's huge shift

00:15:01: It Is.

00:15:02: You also noted that hyper personalization no longer luxury It's table stakes.

00:15:07: If you cannot deliver a highly contextual Market of One experience at scale, your outreach is just invisible!

00:15:13: And if you want to deliver that market-of-one experience, You have to find where the signal density of your buyers is highest.

00:15:36: Yeah, decision makers are spending roughly forty seven minutes a day on the platform.

00:15:40: But it's not just about reach It's about context.

00:15:42: They're consuming operator insights watching how competitors position themselves validating market shifts.

00:15:48: right.

00:15:49: that forty-seven minutes is where professional Context actually turns into pipeline.

00:15:54: So let's put this all together.

00:15:55: then we have these incredibly sophisticated AI driven hyper personalized systems That our targeting buyers exactly were.

00:16:03: they spend their time online.

00:16:05: Yep.

00:16:05: If you're a revenue leader looking at this setup, it is incredibly tempting to think amazing I have the ultimate automated engine!

00:16:12: I can just press a button and scale my company globally.

00:16:15: Oh but that's dangerous assumption.

00:16:17: It is crashing hard against market reality right now

00:16:19: Because hyper personalization inherently requires local context which completely breaks the idea of global copy paste.

00:16:27: The human element still dictates the realty of the market regardless how good your routing rules are.

00:16:32: Absolutely.

00:16:34: Aliexandra Makretsova issued a really strong warning about this friction.

00:16:38: She noted that trying to copy a European go-to market strategy and paste it directly into the US market is a recipe for burning budget, getting zero traction.

00:16:47: Oh!

00:16:47: Completely different worlds?

00:16:49: Yeah A product narrative that crushes in Berlin or Paris will often fall completely flat in America.

00:16:56: U.S buyers have a different mechanical expectation of software.

00:16:59: They expect immediate clarity an incredibly fast time value.

00:17:04: If your messaging takes too long to get the point, The US buyer has already moved on.

00:17:20: they speak English, is essentially executive laziness with a travel budget.

00:17:24: Well tell us how you really feel?

00:17:25: Right but he's right.

00:17:27: the UK is not a shortcut to your European product market fit.

00:17:30: The european market is this complex architecture of different buying cultures legal frameworks trust signals.

00:17:36: It's not a monolith

00:17:38: Exactly!

00:17:38: A sales motion that closes deals in London does not automatically translate to Germany France or the Nordics.

00:17:43: Yeah You have to earn that market architecture Not just try cover it from laptop and London.

00:17:48: That brings up a really fascinating example from Katarina Ginting about expanding into Southeast Asia.

00:17:55: She shared a story by the BDB company that spent six months meticulously translating their entire content library and blog, in English specifically for the Southeast Asian market.

00:18:05: Okay

00:18:05: so six month of work

00:18:06: Six months.

00:18:08: Do you know how many inbound leads that translated content generated?

00:18:12: I'm guessing not many.

00:18:13: Zero literally zero.

00:18:15: They called it ghost town marketing.

00:18:16: Wow, because in highly trust based markets enterprise software is not bought from translated corporate blogs.

00:18:23: It has bought from local founders and faces that the buyers recognize and trust to their local feeds.

00:18:29: See this exact dynamic Is why Karina Artega advocates for what she calls The seventy thirty GTM playbook when expanding globally

00:18:37: the seventy-thirty Playbook.

00:18:38: let me guess how that breaks down.

00:18:39: go first.

00:18:40: So the seventy percent is the automated engine we've been talking about.

00:18:43: And I'm guessing that thirty percent.

00:18:44: it's just like translating the language for local market?

00:18:47: It

00:18:48: actually goes much deeper than just translation, its' bout translating trust signals.

00:18:52: Oh interesting

00:18:52: She shared this example of a smart glasses product launch The physical kit and technical stacks The baseline positioning on paper.

00:19:01: That was all identical globally.

00:19:06: It's the scalable core, your AI workflows.

00:19:08: Your foundational planning cadences.

00:19:10: You build that once and run it on automation.

00:19:14: But how that company actually showed up And built the narrative in Germany Was fundamentally different from How they showed up In the Middle East.

00:19:21: That is a thirty percent!

00:19:23: It simply refuses to be copy pasted

00:19:25: Because of cultural nuance

00:19:26: Exactly!

00:19:27: It requires experienced humans Who understand those nuances And know exactly which local levers Build trust.

00:19:33: The entire goal of automating that seventy percent isn't to fire your humans, it's to free up their cognitive bandwidth so they can focus obsessively on perfecting the local thirty-percent.

00:19:44: That is a brilliant way frame relationship between AI and human operator.

00:19:49: You engineer systems to handle scale So you're humans have energy to handle trust

00:19:54: Exactly.

00:19:54: But really brings us into final piece of puzzle here.

00:19:58: You could have the perfect seventy per cent automated system and you can map out your thirty percent local strategy.

00:20:03: But how do you actually capture that trust mechanically?

00:20:06: You do it through the words, you use.

00:20:08: Ivan Dimitrijevich shared a concept that ties this entire deep dive together beautifully.

00:20:14: he calls at The Byer

00:20:16: Language Bank.

00:20:16: Yeah He points out really stark reality.

00:20:18: buyer buyers Do not wake up in the morning thinking about your company's highly polished twenty-twenty six positioning narrative.

00:20:26: They really don't.

00:20:27: they wake up trying to figure out How Not To Look Stupid In Their Next Internal Meeting Yes.

00:20:31: Yet what do most marketing teams do?

00:20:33: They feed their highly engineered AI agents with generic persona notes and corporate jargon,

00:20:38: And when you feed generic corporate jargons into an LLM the output is just vendor fog.

00:20:44: at machine speed The AI strips out all of humanity.

00:20:47: You sound like every other vendor on the internet

00:20:50: Exactly!

00:20:51: The best go-to market strategy doesn't try to sound clever.

00:20:54: It sounds recognizable.

00:20:56: It captures the raw, unpolished language that the buyer actually uses when they're stressed.

00:21:02: When their talking about risk and when there feeling

00:21:04: urgency.".

00:21:05: If you are building an AI-native revenue engine... The fuel you put in that engine has to be real human language!

00:21:13: So we want to turn this directly over to YOU THE LISTENER.

00:21:15: Think of your own messaging.

00:21:16: think about the prompts you were feeding into tools right now And ask yourself this final challenge What is the exact raw unpolished phrase your buyers use when they are stressed that you're marketing team still refuses to say out loud?

00:21:31: That's a great question.

00:21:32: To leave them with.

00:21:33: if you enjoyed this episode new episodes drop every two weeks.

00:21:36: Also, check our other additions on account-based marketing field Marketing channel marketing MarTech social selling and AI in B to be marketing.

00:21:44: Thanks for tuning in everybody.

00:21:46: take a hard look at your systems This week.

00:21:47: don't forget to hit subscribe And we'll catch ya next one.