Best of LinkedIn: Go to Market CW 44/ 45

00:00:00: Provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about go-to market in calendar weeks forty four and forty five.

00:00:08: Frenness is a B to B Market research partner helping ICT and tech providers identify niche channel partners By compressing the full journey from identification To qualified first meeting into four or five weeks.

00:00:19: you can find more info in description.

00:00:21: And today we are cutting right through the theoretical fluff.

00:00:25: We're bringing you the top go-to market trends extracted directly from the smartest conversations happening across LinkedIn,

00:00:31: exactly no jargon just The actual strategies.

00:00:35: and you know the systems that are moving the needle today.

00:00:38: Welcome to this deep dive especially all of use strategic BDB marketing professionals listening out there.

00:00:43: Yeah I mean imagine firing your absolute best sales development team not because of budget cuts, but because well a single engineer and RevOps just completely replicated their output using an AI agent.

00:00:56: Which is wild!

00:00:58: And that isn't some crazy prediction for twenty thirty right?

00:01:01: It's happening in the trenches this very week.

00:01:04: it really is.

00:01:05: The data and real-world use cases we're seeing now show fundamental rewiring how revenue was generated.

00:01:12: So let us get into architecture at all Because you can't scale revenue today without the right underlying mechanics.

00:01:19: Right, exactly

00:01:20: and The primary architect of this new era has this title business just exploding right now...the go-to market engineer?

00:01:27: Yeah it's definitely the role of the moment

00:01:30: It is!

00:01:31: It shifted so fast from a trendy buzzword to completely mandatory discipline.

00:01:36: Just give you sense on scale here.

00:01:38: Job listings for specific title jump from sixty three to over three thousand.

00:01:42: three hundred forty in single year.

00:01:45: That is massive.

00:01:47: What are they actually doing?

00:01:48: Because I mean, an engineer can mean a lot of different things depending on who you ask.

00:01:52: Well the definition is evolving almost daily.

00:01:55: We're seeing some really interesting observations from Jan Brockowitz and Alex Lindahl that capture this.

00:02:01: They point out we already moving from GTM engineering one-point zero to two-point.

00:02:05: no Wait...

00:02:06: Really?!

00:02:07: We were already in two-points zero!

00:02:09: What was one point oh like just last six months?

00:02:12: Basically yeah.

00:02:13: Version one point now, which was essentially twenty-twenty four to early twenty.

00:02:17: Twenty five Was all about data enrichment and AI outbound

00:02:22: just scraping emails stuff.

00:02:24: Yeah exactly the goal is Just how can we scrape emails faster?

00:02:28: And How Can We use AI To write slightly more personalized cold outreach at this massive scale?

00:02:34: it was really just automating.

00:02:35: top of funnel noise

00:02:36: makes sense.

00:02:38: But version two point oh is fundamentally different.

00:02:40: It is no longer about outbound volume it's About building a GTM wide context layer and deploying what they're calling agentic selling.

00:02:49: Okay, let's slow down on Context Layer for second because that sounds a bit like you know consultant speak.

00:02:55: What does that practically look?

00:02:56: Like when you're looking at alive CRM?

00:02:58: Well think of it this way in one point.

00:03:00: Oh your AI only knew the prospects LinkedIn bio And maybe their company website.

00:03:04: it was working in a complete silo.

00:03:06: right

00:03:07: A context layer means the system is connected to literally everything.

00:03:11: So, The AI knows what the prospect did on your pricing page yesterday.

00:03:15: Oh wow!

00:03:15: Yeah it knows.

00:03:16: if their company just raised funding on CrunchBase or they have an open support ticket in Zendesk It know exactly what features that are using.

00:03:24: you're free trial right now.

00:03:25: So its pulling all of this together?

00:03:27: Exactly and connects entire systems.

00:03:30: so the AI has full context for business relationship not email sequence.

00:03:35: Okay, I gotta push back here a little bit.

00:03:37: Because when you describe system connectivity and data flow across Zendesk into CRM it just sounds like your describing traditional revenue

00:03:45: operations?

00:03:45: I hear that a lot!

00:03:47: Are we taking RevOps slapping the shiny new AI badge on it?

00:03:50: paying these people twenty percent more?

00:03:53: It feels like putting a turbocharger in this same old engine.

00:03:56: Its totally fair point And its actually the exact debate happening at executive level right now.

00:04:01: Brendan Short pulled some data on this looking at over a thousand full-time GTM engineers.

00:04:06: And what did he find?

00:04:08: He found that fifty one percent of them actually report directly to RevOps, so you aren't wrong.

00:04:13: the lineage is absolutely there

00:04:15: Right!

00:04:15: So it's ops

00:04:16: It IS but distinction in execution.

00:04:19: Petra Hajal and Charlie Saunders both stress this point.

00:04:22: If a GTM engineer just builds siloed outbound tables like clay That only delivers short term value.

00:04:29: So what's the long-term play then?

00:04:31: True, GTM engineering requires that deep ops background because you aren't just managing dashboards anymore.

00:04:38: You are actively writing code or using really advanced logic to rewire how data triggers actions across the whole system.

00:04:44: Which brings up a really interesting point from Jeremy Seale I want to mention.

00:04:48: He had this very provocative take on where all of these is heading.

00:04:53: Yeah his take was fascinating.

00:04:55: he argues that the GTM engineer title might actually already be dead, or at least it's rapidly evolving into a GTM product manager.

00:05:04: Right because the technical barrier is dropping?

00:05:07: Exactly as the tooling gets easier The critical question shifts.

00:05:10: It used to be.

00:05:11: you know can I build this complex automated workflow?

00:05:14: now the Question Is should i build This?

00:05:16: and will our sales of marketing teams Actually even use it?

00:05:20: And that leads directly To where the true competitive advantages right Now.

00:05:23: i mean anyone Can buy the same third-party enrichment software.

00:05:26: yeah the tools are everywhere.

00:05:28: Right, you can buy it.

00:05:28: Your competitor can buy but the ultimate moat as noted by JoVoo is using your own first-party product data

00:05:35: which computers obviously don't have

00:05:37: Exactly!

00:05:38: The most advanced GTM engineers are now triggering sales workflows based on actual product usage telemetry.

00:05:44: That's the real game changer.

00:05:46: What does that look like in practice though?

00:05:48: Give me a real world scenario.

00:05:50: Sure.

00:05:51: So let say you sell a developer tool Instead of a sales rep setting up You know A calendar reminder to check in at Day fourteen of a trial.

00:05:59: Which they always forget to do anyway?

00:06:01: Right, exactly!

00:06:02: So instead of that the GTM engineer builds system where if user hits specific API limit three times in one hour and are located at target region The system instantly triggers an autonomous agent.

00:06:15: And what does it do?

00:06:16: It sends highly contextual email offering upgrade their tier To prevent throttling Its this immediate value driven action based on proprietary data.

00:06:26: Your competitors just cannot copy that.

00:06:28: Because they don't have your product telemetry?

00:06:30: That is fascinating!

00:06:32: The product itself acts as the primary engine for the outbound motion.

00:06:36: Yeah,

00:06:36: exactly.

00:06:37: But you know if these GTM engineers are the architects building the roads then the AI agents set up actual vehicles driving on

00:06:43: them Right...That's a great way to put it

00:06:45: Which means we have to completely rethink this software those vehicle run-on.

00:06:50: Daniel Sachs noted this massive paradigm shift here.

00:06:52: He says software is officially moving from a passive system of record to an active system of action.

00:06:58: It's a huge shift in how we interact with technology.

00:07:01: if you think about traditional enterprise software it was like This massive secure library right?

00:07:07: You go into the library, you pull a book off The shelf you read the data and then you the human decide what action to take.

00:07:13: it Is totally passive

00:07:15: And a system of Action.

00:07:17: A system of action operates more like a central nervous system.

00:07:20: It reflexively triggers actions across the body based on stimuli, without needing any conscious human intervention at

00:07:27: all.".

00:07:27: But I mean integrating that nervous system hasn't exactly been smooth sailing... Paul Stalen shared some incredible findings from a recent dinner with top GTM leaders.

00:07:35: Yeah, that data was really surprising!

00:07:37: Totally

00:07:38: upends what most vendors are preaching right now.

00:07:40: real quick by the way just for reminder to hit.

00:07:42: subscribe To this deep dive so you catch future editions where we unpack these exact kinds of shifts.

00:07:47: definitely

00:07:47: do That.

00:07:48: but getting back to paul stalin's finding he found The giving every individual sales rep their own AI co-pilot is actually backfiring

00:07:55: which sounds crazy at first.

00:07:57: Right.

00:07:58: In one specific case, a sales leader had to completely roll back unlimited co-pilot access.

00:08:03: Because the Revs who were spending most on AI tokens like those using the AI the most?

00:08:08: Let me guess they have worse numbers.

00:08:10: Yes!

00:08:11: They actually hit weakest key performance indicators.

00:08:13: Wait so giving sellers more AI power hurt their performance.

00:08:18: I mean that goes against everything we are being sold right now.

00:08:20: It really does But it makes sense when you dig into psychology of it.

00:08:25: Exactly, like you said.

00:08:26: The winning move isn't giving individual co-pilots to everyone.

00:08:30: Why not though?

00:08:31: Don't copilots make everyone faster.

00:08:33: They make a great performer slightly faster Sure But they made mediocre performer catastrophically noisy.

00:08:40: AI lowers the marginal cost of execution To basically zero.

00:08:43: Ah

00:08:44: I see where this is going.

00:08:44: Right

00:08:45: If give us struggling rep tool to generate infinite emails... ...they don't suddenly become strategic.

00:08:51: They just generate infinite bad e-mails.

00:08:54: So the easy button just incentivized lazy behavior.

00:08:57: They focus on volume because it's effortless, totally abandoning research and relevance

00:09:03: Exactly which is why The Smart Play is centralized ops owned agents running quietly in the background.

00:09:09: Adam Schoenfeld tracks this whole new category.

00:09:11: he calls GTM developer tools.

00:09:14: What are those exactly?

00:09:15: they're API first self-serve platforms.

00:09:18: a lot of them don't even have user interface for human sales reps.

00:09:22: They are designed specifically for centralized agents to execute workflows autonomously.

00:09:28: So the reps don't even interact with AI directly?

00:09:31: Nope, The Reps shouldn't know that they're there!

00:09:33: They just wake up to warm qualified opportunities waiting in their CRM.

00:09:37: The scale of those centralized approaches is staggering.

00:09:40: Alexander Bauer and Josh Whitfield highlighted one GTM team currently running over different autonomous agents.

00:09:49: A hundred fifty agents making decisions at light speed?

00:09:53: Yeah, and mathematically the risk factor there goes through the roof.

00:09:57: think about it.

00:09:58: The same agent that has the API permissions to qualify a new lead inherently Has the technical access to accidentally overwrite the annual recurring revenue field on a massive closed one deal if its logic breaks.

00:10:11: And that right there is the hidden iceberg of all this.

00:10:14: Fabrice Boron warns companies about exactly this problem.

00:10:18: Because everyone thinks it's so cheap to build,

00:10:21: Right?

00:10:21: Yes!

00:10:22: Founders look at cost an open AI API key and think oh building in AI GTM motion in house basically free.

00:10:29: They completely miss the hidden infrastructure costs.

00:10:32: Building agent is easy part.

00:10:33: Exactly

00:10:34: The real challenge, the part lives below water line Is governance security the observability, and the data integrity underneath it.

00:10:43: Right because if an agent hallucinates you are in trouble.

00:10:47: Massive trouble!

00:10:48: If one of those hundred-and fifty agents goes rogue... You need absolute certainty that can trace its logic isolate the error and revoke its access before completely corrupts your single source of truth.

00:10:58: That makes total sense.

00:11:00: And y'know..if Agents taking over actual button pushing into these tools and governance is becoming their main priority completely invalidates the reason we bought half of our tech stack in the first place.

00:11:10: It really does, you don't need a UI heavy expensive platform if an AI is doing their work through an API anyway

00:11:17: which is causing this massive market correction.

00:11:19: and what companies are actually buying?

00:11:21: with agents taking over actions?

00:11:23: throwing more tools at that problem is no longer the answer.

00:11:27: The best stacks are actually shrinking.

00:11:29: Oh absolutely.

00:11:30: The modern GTM stack is getting ruthlessly leaner, and it's being mapped strictly to the company's current stage.

00:11:37: Mario

00:11:37: Chardor breaks down the brutal math of this doesn't he?

00:11:39: He does!

00:11:40: He points out that lethal danger.

00:11:42: buying a Series B tech-stack when your company is only at the seed stage A proper preseed stack should cost about one hundred three hundred dollars per month.

00:11:50: That

00:11:50: practically nothing

00:11:51: Right.

00:11:51: And by series A It scales maybe fifty-five hundred to eight thousand dollars, but early stage founders are burning massive amounts of venture capital on enterprise tools they simply aren't ready to utilize.

00:12:03: Because they think buying the tool magically creates a pipeline.

00:12:07: it's like to borrow with thought from David Trombie scaling a broken go-to market engine just helps you fail faster.

00:12:13: It was like putting a rocket booster on a bicycle.

00:12:16: that is perfect analogy.

00:12:18: And David Emanuel Marrera introduced a brilliant singular filter for evaluating any new tool for the stack.

00:12:25: He asks, does it actually move pipeline or does it only add complexity?

00:12:31: And if it just creates another dashboard for our manager to check you reject It.

00:12:35: this lean philosophy is deeply affecting outbound strategy too.

00:12:39: How so

00:12:40: well.

00:12:40: James Mason argues that your Outbound tooling should stay incredibly minimal until You Actually find message market

00:12:46: fit.

00:12:47: Don't buy a massive sequencing platform until you know what to say

00:12:51: exactly.

00:12:52: don't scale your outreach Until you have proven manually that your specific message resonates.

00:12:57: Yep

00:12:57: furthermore Ramkumar highlights a massive shift in procurement.

00:13:01: What are teams buying instead?

00:13:03: They're abandoning those big inflexible annual contracts for nimble usage-based tools and they're centering it all around a single data orchestration layer.

00:13:12: It's like swapping ten disconnected spreadsheets for one centralized table that handles all the logic

00:13:17: That forces a level of operational discipline that we frankly just haven't seen in awhile.

00:13:21: Diego Mangabera gives incredibly grounded advice here about scaling humans alongside this lean tech.

00:13:28: Yeah, his thirty-day rule

00:13:29: Right before founder goes out and hires a fully loaded sales rep which will easily cost one hundred twenty two hundred thousand dollars A year.

00:13:36: they need to run the GTM motion by hand for thirty days.

00:13:41: Don't hire a rep to fix emotion.

00:13:42: you haven't even proven yourself.

00:13:44: It's the ultimate reality check for founders.

00:13:46: it really is.

00:13:47: But let's assume a company gets this right.

00:13:49: Their tech stack is lean, their infrastructure solid and their agents are flawlessly handling the repetitive top-of-funnel actions.

00:13:57: Okay

00:13:57: What actually differentiates their brand?

00:14:00: Because if your tech stack Is lean an agent handle The repetitive stuff.

00:14:03: everyone has the same capabilities.

00:14:06: That is the million dollar question.

00:14:08: And the answer becomes human connection highly strategic content, and pinpoint accuracy on who your ideal customer actually is.

00:14:16: So we are looking at the intersection of Content Engineering & Relationship-led selling

00:14:21: Precisely.

00:14:22: We're seeing a rise in this new operational duo.

00:14:25: The GTM engineer paired with the Content Engineer David Terowitz and Sasha Kayward noted that specific two person pod can now replace an entire ten percent SDR team.

00:14:34: Wait walk me through the mechanism.

00:14:36: How does a content engineer actually replace a human SDR dialing the phone?

00:14:41: It's all about warming the environment before the automated strike.

00:14:44: The Content Engineer relentlessly distributes highly targeted, valuable content to specific accounts.

00:14:50: They engineer visibility

00:14:52: So they're building familiarity

00:14:54: Exactly.

00:14:55: So by the time, the GTM engineers automated outbound touch lands in a prospects inbox.

00:15:00: it isn't cold at all.

00:15:01: It's landing on a prospect who already knows the brand has consumed the thought leadership and recognizes The name?

00:15:07: The content essentially replaces the initial fifteen dials an SDR used to make.

00:15:11: just get brand recognition.

00:15:13: You hit the nail on the head

00:15:14: Okay...the mechanics of that makes perfect sense.

00:15:17: But I keep coming back to core problem.

00:15:19: If our content is great And our agents are flawless who should we actually be targeting?

00:15:25: Because right now every single tech company is being bombarded.

00:15:28: The noise level is deafening,

00:15:30: it really does.

00:15:31: Well Sherrod Verma has a brilliant strategic insight regarding this exact saturation problem.

00:15:36: he suggests that for AI native startups Tech companies might no longer be the best ideal customer profile.

00:15:42: I got to push back on that.

00:15:44: Tech companies have always been early adopters.

00:15:46: if you're selling an AI tool why wouldn't target most tech forward companies

00:15:52: Because those tech forward companies have developed an immune system response to AI vendors.

00:15:56: An immune system?

00:15:57: Yeah, whenever you pitch and engineering heavy-tech company their default objection is always well we have fifty developers.

00:16:04: We can just build this ourselves even if they can't do it.

00:16:06: Well that internal ego blocks the sale.

00:16:09: Oh wow

00:16:11: So

00:16:11: who was the smarter target then?

00:16:13: Sherrod argues The Smarter Target Is AI aware.

00:16:16: Traditional Enterprises Think Heavy Manufacturing Global Logistics, Legacy Finance.

00:16:22: They are used to buying heavy implementation software and they actually need the help.

00:16:27: but legacy finance in manufacturing have notoriously awful procurement cycles.

00:16:32: you're just trading technical objections for nine months of red tape.

00:16:35: You are!

00:16:36: But consider the trade-off...they know they cannot build it themselves.

00:16:41: A nine month sale cycle that ends in a closed one enterprise deal is infinitely better than a three week cycle.

00:16:47: That ends with the tech buyer deciding to build a crappy version and house.

00:16:51: okay, that Is a deeply pragmatic pivot?

00:16:54: And this shift in who we sell too?

00:16:56: it totally affects the sellers themselves right.

00:16:59: It completely breaks down how he structure of the sales team.

00:17:03: Yako van der koei pointed out a massive vulnerability In the classic specialized sales model.

00:17:08: write the whole assembly line

00:17:10: model Exactly, where an SDR books the meeting.

00:17:13: An account executive closes the deal and a customer success manager handles the onboarding.

00:17:18: that's falling apart

00:17:20: And the reason is to shift toward usage-based and outcome based pricing isn't it?

00:17:24: Yes If you don't get massive six figure upfront fee at Signature but rather pay on usage over twelve months handing them off three different people creates massive friction insurance.

00:17:36: So we need return full cycle seller.

00:17:39: You need a highly strategic rep who stays with the account from the first call to drive actual change management and adoption.

00:17:46: Absolutely, yeah because in a world where AI handles all of the administrative busy work The seller's true value is guiding a human buyer through organizational change.

00:17:56: But amidst this high-level strategy sometimes the simplest human tactics win.

00:18:01: Jessica Lynn reminds founders of the ultimate anti ghosting rule in B to be Bamfam

00:18:08: Book a meeting from a meeting.

00:18:10: Exactly never leave the first call without the next one on the calendar.

00:18:14: We are building these incredibly sophisticated AI systems But a rep will still get out of a call and then let the prospect walk away saying just send me some times

00:18:21: And the momentum dies instantly.

00:18:23: that's right No matter how advanced your GTM engineering is if you don't secure The next step while you have the human on screen, the entire multi-million dollar tech stack failed to do its one job.

00:18:33: It's humbling reminder

00:18:35: it is but I want to leave you with one final provocative thought to mull over regarding that exact point.

00:18:42: We've spent this entire time talking about how GTM engineering and AI agents are automating how we sell, but how long until the buyers start deploying their own AI agents to screen evaluate and reject our sales agents?

00:18:56: Oh man procurement agents

00:18:59: exactly.

00:19:00: Are we moving away from business to business selling and entering the era of agent-to-agent selling?

00:19:06: Because when a highly trained sewing machine meets an equally aggressive buying machine, all our current playbooks are going to become obsolete.

00:19:14: That is a wild...and honestly terrifying thought to Andon!

00:19:19: If you enjoyed this episode new episodes drop every two weeks.

00:19:22: Also check out other editions on field marketing channel & partner marketing AI&BDB MarTech ABM and social selling.

00:19:32: Thanks so much for tuning in!

00:19:34: Thank you for joining us on this deep dive, don't forget to subscribe.