Best of LinkedIn: Go-to-Market CW 14/ 15

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

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

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

00:00:23: Yeah, and we are really excited to jump right into this one because if you look at the data The landscape is just.

00:00:29: it's totally shifting.

00:00:30: Oh

00:00:30: absolutely.

00:00:31: So for context our mission today Is unpacking?

00:00:34: The top go-to market insights Curated directly from the front lines of LinkedIn over calendar weeks fourteen and fifteen.

00:00:40: Right And for you the B to be marketing professional listening right now Just knowing that things are changing well It's just not enough anymore.

00:00:47: no it's really not.

00:00:48: You have to understand the actual mechanics right like yeah how these new commercial engines Are being built in the ground up

00:00:52: Exactly.

00:00:54: Which brings us to our roadmap for this deep dive.

00:00:56: Yeah, we're gonna cover four major themes.

00:00:58: We saw trending the shift a i native gtm Stack simplification context lead execution and finally GTM discipline.

00:01:07: It's a lot to cover but it all connects perfectly

00:01:10: definitely.

00:01:10: so starting with theme one ai native GTM okay?

00:01:14: Let's unpack this because Looking through the insights from these operators The most foundational shift were seeing is that AI has you know, officially graduated.

00:01:23: It really has.

00:01:24: it's forcefully moved out of that basic productivity support phase.

00:01:28: right we're no longer talking about just asking a chatbot to rewrite a cold email so sounds more professional.

00:01:34: yeah those days are over.

00:01:35: its planted itself firmly into the execution core Like it's actually running things.

00:01:40: Yeah,

00:01:40: the infrastructure now and The source material really highlights this massive pivot away from simple prompt engineering And its moving toward deep system design.

00:01:51: We're looking at operators who are actively handing over research led enrichment Routing an you know even campaign setup directly to AI.

00:02:00: right that post mentioned specific platforms too like flood code clay and Demand base AI

00:02:05: exactly.

00:02:06: But the critical distinction here is how they're actually doing it.

00:02:10: They aren't just like opening a chat window and typing a prompt,

00:02:14: right?

00:02:14: It's way more advanced.

00:02:15: They are literally configuring the cognition of their commercial engine focusing heavily on context memory And

00:02:24: guardrails.

00:02:24: wait I want to stop on that phrase for a second.

00:02:26: configuring cognition.

00:02:27: Yeah because to me That implies we were asking BDB marketing teams To operate with the discipline.

00:02:34: Software developers you

00:02:35: kind of have to honestly.

00:02:36: It's

00:02:36: like going from playing a solo instrument, too Suddenly conducting a full symphony orchestra I mean our GTM teams actually ready to operate with this product-like discipline.

00:02:46: Well what's fascinating here is that the market?

00:02:48: Is ruthlessly forcing their hand.

00:02:50: oh really

00:02:51: yeah.

00:02:51: The signal from the data is undeniably clear.

00:02:54: You know ai value is no longer about novelty at all

00:02:58: right?

00:02:58: nobody in C-suite cares if your using AI just write a blog post anymore

00:03:02: Exactly.

00:03:03: The value is strictly defined by whether this technology improves your speed, cross-functional coordination and ultimately your pipeline outcome.

00:03:11: And if you don't adapt just get out executed.

00:03:13: Yeah

00:03:13: the operators adopting these product like discipline are hitting a velocity that traditional teams simply cannot match

00:03:20: Wow which naturally exposes massive liability than what companies are waking up to.

00:03:26: Oh for

00:03:26: sure!

00:03:26: The tech

00:03:27: stack Right our second theme.

00:03:29: Because if your goal is an integrated AI engine capable of executing complex logic at that speed, you quickly realize You just can't run that engine on the bloated fragmented tech stacks we've built over last decade.

00:03:42: Absolutely

00:03:43: not.

00:03:43: The sheer latency between systems will kill the automation entirely.

00:03:47: Yeah tool sprawl as official being called out as the enemy Of modern GTM.

00:03:51: For years, the industry habit was just to buy a niche Microsoft for every tiny problem.

00:03:57: Right

00:03:57: one tool for intent data?

00:03:59: One for sending sequences and one for call recording?

00:04:01: Exactly!

00:04:02: And the posts from Weeks Fourteen & Fifteen frame this fragmented architecture as fatal flaw.

00:04:09: When your commercial data is sitting in six different silos Your AI agent can't build a complete picture of that account.

00:04:16: It creates data decay

00:04:17: Yes, it fragments ownership and it grinds execution to a complete

00:04:21: halt.

00:04:22: Here's where it gets really interesting though.

00:04:25: we aren't just seeing thought leaders complained about tool sprawl on LinkedIn.

00:04:29: We're seeing the capital markets react.

00:04:31: Oh

00:04:32: acquisitions.

00:04:33: Yeah.

00:04:33: look at Apollo acquiring Pocas.

00:04:36: that isn't just a corporate merger.

00:04:37: That is a massive signal About the death of the commercial handoff.

00:04:41: It's the perfect illustration Of this consolidation trend.

00:04:44: Pocas built this reputation on product-led sales signals and complex account scoring, right?

00:04:49: Mm-hmm.

00:04:49: Essentially acting is the brain that figures out who you should actually be talking to

00:04:53: Exactly.

00:04:54: Apollo in other hand Is the execution layer The hands that actually send emails make the dials And run the sequences.

00:05:00: So by bringing them together You completely collapse the distance between the insight In the action.

00:05:05: Right, you no longer have a RevOps manager exporting a CSV of hot accounts from Pocus on a Friday.

00:05:12: And uploading it into Apollo in a Monday?

00:05:14: Exactly!

00:05:15: Hoping the sales reps actually follow up... The logic that prioritizes and the workflow that executes the outreach now live in the exact same environment.

00:05:24: That makes total sense!

00:05:25: And we're seeing this demand for connected environments totally reshape how companies approach ecosystem partnerships too.

00:05:32: Oh so

00:05:33: Well, The old model of Ecosystem Partnerships was purely about marketing breadth right?

00:05:37: Putting fifty different vendor logos on a slide just to look comprehensive

00:05:41: oh yeah...the massive integration webs.

00:05:43: Today, partnerships are judged entirely on execution & data proximity.

00:05:48: Give me the mechanical reality of that.

00:05:49: Like, how does data proximity actually change the workflow?

00:05:53: Look at the example in our sources of rocks becoming built on Databricks.

00:05:57: Databracks is this massive heavy-duty enterprise data infrastructure.

00:06:01: Historically if marketing wanted to use that data they had to pump it out of The Data Lake filter and push into a separate engagement platform That introduces massive latency

00:06:12: Because its constantly sinking back and

00:06:13: forth Exactly.

00:06:15: But by building ROX, which is an AI revenue agent layer directly on top of Databricks the AI is swimming in The native data lake.

00:06:23: Oh wow there Is no handoff No sinking delay?

00:06:27: The agent can act On the raw data.

00:06:29: the second it updates demand base is pushing a very similar narrative with their connected platform approach

00:06:35: emphasizing that execution speed depends entirely Unaligned real-time view Of the account.

00:06:41: precisely I

00:06:42: understand the appeal of stripping away the silos.

00:06:44: I really do.

00:06:45: nobody likes navigating fifteen different vendor dashboards just to figure out why a deal stalled.

00:06:50: It's a nightmare,

00:06:51: but let me push back on this push for consolidation.

00:06:53: For second if leaner architecture is the mandate and partnerships only matter when they shorten The path to pipeline action aren't we risking severe vendor lock-in?

00:07:04: If we dump all our specialized Best of Breed tools just to reduce handoffs, aren't we sacrificing the deep functionality that made those niche tools valuable in first place?

00:07:14: It is a classic tension between best-of-breed and all-in-one.

00:07:18: But the operators driving this conversation in weeks fourteen or fifteen suggest that cost fragmentation has simply outgrown the benefit of specialized features.

00:07:26: Really?

00:07:27: Yeah!

00:07:28: Having a NUSH tool with slightly better UI doesn't matter if intent data it generates gets stuck in an API bottleneck for three days before reaching the wrap.

00:07:36: Right, by then the buyer has moved on

00:07:38: Exactly!

00:07:39: Speed and coordination are trumping specialized features.

00:07:42: right now The market is aggressively favoring platforms that offer broad orchestration capability because An AI engine requires frictionless fuel to function.

00:07:50: But tearing down your tech stack To let that Frictionless Fuel flow freely Reveals a terrifying new problem Which brings us to Theme Three.

00:07:59: Yes When you strip away the silos, You're suddenly standing in a massive uncontrolled flood of raw data.

00:08:07: And that is exactly where most AI motions are drowning right now.

00:08:11: We have officially crossed out of the era of data abundance and into the era Of decision usefulness.

00:08:17: A recurring warning across all the posts from these weeks Is that raw data has lost its strategic value.

00:08:23: Totally Ten years ago, simply knowing that a target account visited your pricing page was huge competitive advantage.

00:08:30: Today every single one of you competitors have the exact same signal.

00:08:34: Yeah having massive database contacts is baseline table stakes now.

00:08:38: So if everyone has the same raw ingredients, The only thing that separates a winning campaign from spam is how you interpret those ingredients?

00:08:45: Exactly.

00:08:45: and the sources point to this massive rise in what is being called context engineering

00:08:50: which I kind of look at like a prism Like raw data's just blinding white light.

00:08:54: It's totally overwhelming an unusable right.

00:08:57: but Context Engineering Is the prism That fractures that White Light into specific actionable color spectrums that a GTM team can actually use to drive revenue.

00:09:07: That is the perfect way to visualize it, because an AI platform can easily collect ten million data points across your target market in a single day.

00:09:15: Which

00:09:15: just noise?

00:09:16: Total noise!

00:09:17: Context engineering means building this specific business logic that tells you AI hey when a tier one target account exhibits a surge of intent around cybersecurity compliance they just hired new CISO... Right.. ...that specific combination signals mean X for our product and automated action we take next should be

00:09:36: Y. So it's about translating a generic market event into hyper specific buying relevance for your brand.

00:09:43: Exactly

00:09:44: Can we walk through what that actually looks like in a modern workflow?

00:09:47: Like let's take the tools, We've been discussing how does a team orchestrate That prism effect?

00:09:51: sure

00:09:52: imagine a seamless three step automated work flow.

00:09:55: Step one, demand-based detects an anonymous intent surge from a target account researching a specific pain point on third party sites.

00:10:03: Okay so that is your raw signal?

00:10:04: Right.

00:10:05: step two instead of just alluring a rep the system automatically pushes that account data into clay.

00:10:10: Clay then runs a waterfall logic to identify the exact buying committee members at that account and enriches them with recent LinkedIn activity in company news.

00:10:19: That Is Your Context Engineering right there

00:10:21: Exactly.

00:10:22: And finally step three Clay pushes those enriched, highly contextualized profiles directly into Apollo automatically dropping them in to a hyper-personalized sequence.

00:10:33: A sequence that references the exact pain point demand based detected and recent company news clay uncovered.

00:10:43: the logic routing and the campaign setup, delivering a warm highly relevant interaction.

00:10:50: Yep if we connect this to The Bigger Picture you can see why message relevance is becoming the ultimate performance differentiator.

00:10:56: because If You don't engineer that context...If

00:10:59: you just take an AI agent And hook it up To raw intent data without That contextual layer?The AI will Just blast out generic messaging at lightning speed.

00:11:08: Yeah!You aren't scaling your go-to market..you are just automating Your own irrelevance at scale.

00:11:12: Automating

00:11:13: irrelevence At Scale.

00:11:14: It's so true.

00:11:16: The entire game is translating data into clear next actions that resonate deeply with the buyer's current specific situation.

00:11:24: Automating irrelevance at scale a really sobering concept, it proves.

00:11:28: simply buying best AI tools won't save bad strategy which perfectly sets up our final theme.

00:11:35: We have an AI engine.

00:11:37: we know need to simplify this stack and reduce latency but who is actually running this highly sophisticated machinery?

00:11:46: That's the human bottleneck.

00:11:47: You just cannot operate that new architecture with a legacy mindset!

00:11:51: Yeah, The insights from these past two weeks heavily emphasize the rise of GTM engineering as formalized discipline within commercial teams.

00:11:59: This is massive evolution in traditional marketing operations.

00:12:02: So what does all mean for the B-to-B marketing?

00:12:04: professional listening Who might be feeling entirely overwhelmed right

00:12:07: now?

00:12:07: It sounds like a lot

00:12:08: If future go to market involves orchestrating AI cognition managing API waterfalls in clay, and mapping data models directly on top of Databricks.

00:12:18: What happens to the core tenets of marketing?

00:12:21: Does the art of it just disappear into the

00:12:23: algorithm?".

00:12:23: I'd argue the exact opposite is happening.

00:12:25: The operators in our source material make a deeply reassuring point.

00:12:30: AI Is ultimately just an amplifier.

00:12:32: right It isn't amplifier.

00:12:33: have a fundamentally sound go-to market motion that cannot invent strategy.

00:12:40: If you feed an AI engine a weak generic positioning strategy, it will simply scale failure faster than you ever thought possible.

00:12:48: It's the old garbage in-garbage out principle just moving at light speed.

00:12:52: Exactly!

00:12:52: The source is actually stressed that human fundamentals are more critical now.

00:12:56: then they were before the AI boom.

00:12:58: things like clear category positioning founder led go to market motions meticulously documented playbooks and trust based messaging.

00:13:04: Yes

00:13:05: those are real signals of commercial maturity.

00:13:07: Those elements give the AI its actual power

00:13:10: Because if your brand doesn't have a distinct point of view, you're automated.

00:13:13: sequences will just blend into the endless noise of AI-generated spam.

00:13:18: Right and this raises an important question about how we train and structure our commercial teams.

00:13:24: moving forward?

00:13:26: The industry is aggressively moving out of the.

00:13:28: let's just give the team chat GPT and see what happens.

00:13:31: phase Yeah...the

00:13:32: novelty has completely worn off.

00:13:33: We are entering a phase of serious structured capability building.

00:13:39: GoToMarket is increasingly being treated as a managed commercial system with rigorous architecture and strict governance.

00:13:45: Linking heavily into revenue operations, post-sales coordination... Definitely!

00:13:49: It really demands completely different skillsets.

00:13:52: I mean B to B. buyers are drowning in noise right now And they're actively looking for vendors who are operationally credible

00:13:58: Absolutely.

00:13:59: That means your internal teams need structured frameworks, they need deep training on how to govern these complex systems not just how to log in and use them.

00:14:07: The

00:14:07: data totally backs that up.

00:14:09: Training certification and structured enablement around the exact workflows are gaining serious traction.

00:14:16: It is no longer an art project.

00:14:18: it's a measurable engineered business discipline.

00:14:23: Building that internal capability, ensuring your human talent actually understands how to drive the machine.

00:14:29: Rather than just letting the machine drive them is the definitive frontier for BDB leaders right now.

00:14:34: It's a demanding evolution.

00:14:35: no doubt

00:14:35: it's a total paradigm shift.

00:14:37: We have moved from a mindset of hoarding isolated best-of-breed tools To designing an integrated AI native commercial engine.

00:14:45: We have recognized that we have to reeflessly simplify our tech stacks, To make sure the engine doesn't choke on its own data.

00:14:52: We've learned that fuel for this new engine isn't raw signals but deeply engineered context That acts like a prism of relevance.

00:15:00: And ultimately seeing human mechanics running in this system need completely new level of operational discipline to turn automated speed into actual measurable revenue.

00:15:11: But for the B-to-B marketing professionals who embrace this system level thinking, who learn to architect workflows and engineer context.

00:15:18: The operational velocity and cross functional alignment they achieve will be a nearly insurmountable competitive advantage.

00:15:25: If you enjoyed today's episode new episodes drop every two weeks.

00:15:29: Also check out our other additions on account based marketing field marketing channel marking more tech social selling an AI in btb.

00:15:38: Thank you so much for joining the conversation today.

00:15:41: We know your time is valuable and we hope these insights help you build a stronger, leaner and more intelligent go-to market engine.

00:15:48: Don't forget to subscribe So You Never Miss A Deep Dive!

00:16:08: don't get automated out of existence.

00:16:10: Really great question to end on, see you all next time!

00:16:13: Bye

00:16:13: everyone.