Best of LinkedIn: AI in B2B Marketing CW 29/ 30

00:00:00: This episode is provided by Thomas Allgeier and Frennis, based on the most relevant LinkedIn posts about AI in B-to-B marketing in calendar weeks twenty nine and thirty.

00:00:10: Frennis is a b to be market research company that supports enterprise marketing teams in unlocking the full potential of their customer data with the help of AI.

00:00:19: And for those of you listening who are strategic B-To-B Marketing professionals today Is really about getting under the hood.

00:00:26: if what's actually working right now.

00:00:28: Yeah, exactly.

00:00:29: because I mean imagine a key enterprise buyer is researching software for their company.

00:00:34: They don't go to Google anymore... Right

00:00:35: they just

00:00:36: don't!

00:00:36: ...they ask ChatGPT to compare you and your biggest rival And in about two seconds the AI doesn't list your features.

00:00:44: No it invents massive downside of your product.

00:00:48: Yes based on like a two-year old Reddit thread The buyer crosses off the shortlist immediately and you never even knew they were looking,

00:00:56: which is terrifying.

00:00:58: It's

00:00:58: the reality of B to D marketing right now though I mean if you look at the landscape there is just so much noise.

00:01:03: So our mission today is cut through that hype.

00:01:06: Yeah we have to look at actual operational shifts happening on the ground.

00:01:10: Right Because finally moving past the era of simple prompt hacks If your are trying to figure out how write a slightly better prompt for blog post.

00:01:22: you're missing the structural revolution.

00:01:25: You are completely missing it.

00:01:26: Today

00:01:26: is about systems that B to be leaders building in this agentic era.

00:01:32: It's about rewiring your entire revenue engine, basically.

00:01:35: So let start at very beginning of that Revenue Engine Because before you can sell anything have actually been found.

00:01:42: Right

00:01:42: Discovery

00:01:43: Exactly.

00:01:44: But Discovery has fundamentally moved off the traditional brand website, it's gone straight into AI answer engine.

00:01:51: It is a complete shift in like physics of buyer behavior Generative Engine Optimization or GEO and Answer Engine Optimisation AEO.

00:01:59: Those

00:01:59: are basically replacing traditional SEO now Totally And to really understand what that looks for your pipeline Sara Soleimani pointed out reality I think should wake up every go-to market leader.

00:02:10: Oh

00:02:10: The shortlist thing.

00:02:11: Yes She noted that B-to-B buyers are now forming their vendor shortlists entirely inside AI tools.

00:02:19: Before they ever even visit a brand's website?

00:02:21: Exactly!

00:02:22: By the time you hit your landing page, The decision is... well it basically already made

00:02:26: And That creates massive problem for marketers Because the rules of visibility Are now completely fragmented.

00:02:32: How so?

00:02:33: Like compared to old days.

00:02:34: Well think about traditional SEO.

00:02:36: Right You optimize Google and that basically covered your bases for everyone.

00:02:42: Right Google was the only game in town,

00:02:44: but Andrew Warden shared some really eye-opening data on this.

00:02:48: he compared google AI overviews to Gemini.

00:02:51: okay

00:02:52: no Google controls both of these, obviously.

00:02:55: Yet there is only a thirty percent overlap between sighted sources and mentions on Gemini compared to a sixty-four percent overlapped on Google AI overviews.

00:03:03: Wait really?

00:03:03: Yeah So even within the exact same google ecosystem The AI's looking at entirely different signals To decide if your brand Is worth mentioning.

00:03:10: Exactly Shared corporate infrastructure does not mean shared rules anymore And it gets vastly more complicated across the different platforms.

00:03:19: I can imagine.

00:03:20: Rochelle Tognetti broke down how each major AI evaluates trust, actually.

00:03:26: She noted that ChatGPT heavily rewards credibility established on places like Wikipedia and Reddit...

00:03:32: Okay!

00:03:32: Wikipedia and Reddit for chat GPT Right

00:03:35: But Gemini on the other hand tracks your presence at Google Shopping.

00:03:40: Google shopping?

00:03:41: That's so different

00:03:42: I know.

00:03:43: And then if you want to show up in Google AI overviews The model leans heavily on YouTube content.

00:03:48: Okay, let's unpack this metaphorically because it's wild.

00:03:51: It's like taking the bar exam in fifty different states.

00:03:54: Yeah that is a good way to look at it.

00:03:55: You

00:03:55: can't just study law broadly anymore.

00:03:58: you have know what specifically matters to judge in specific room.

00:04:02: Exactly Chachi BT is a Judge who loves Wikipedia and Reddit debates.

00:04:07: Gemini is a judge only cares about your retail footprint.

00:04:10: So how can a brand possibly build authority across all these, you know fragmented contradictory rules?

00:04:16: You have to focus on something called the Citation Core.

00:04:18: The Citation core...

00:04:19: Yeah Alexandra London analyzed Shopify's visibility in chat GPT To figure out why they completely dominate.

00:04:27: And Shopify presence isn't driven by its own massive blog.

00:04:31: Oh it's not.

00:04:32: No their visibility is anchored By over a thousand citations from G-II and like eight hundred citations From Kaptera Wow.

00:04:40: It perfectly aligns with what Thomas Ross outlines in his Vantage scorecard, actually AI models borrow trust from third parties.

00:04:48: right they do not trust your own marketing copy.

00:04:50: okay let me make sure i'm getting the mechanics of this.

00:04:52: if I publish say a thousand amazing blog posts about why my software is the absolute best

00:04:58: The AI essentially discounts that

00:05:00: because im biased.

00:05:01: But if a hundred independent software review sites say I'm the best, the AI takes that as like mathematical

00:05:07: proof.

00:05:08: Precisely it's a probabilistic model.

00:05:10: The AI triangulates your brand value based on what the aggregate web says about

00:05:15: you.

00:05:15: so You can't buy your way in nope?

00:05:17: You cannot pay your way into the sources.

00:05:19: AI trusts you have to earn That density of mentions elsewhere

00:05:22: which makes the way these engines display results Incredibly high-stage.

00:05:27: Oh for sure.

00:05:27: Aruprasad ran a brilliant test on this exact dynamic.

00:05:31: He created five-axis tests to evaluate AI answers.

00:05:35: Right, looking at things like presence

00:05:37: slot?

00:05:38: Yeah!

00:05:38: Presence Slot Coverage Liability and Door.

00:05:42: But I really want zero in that liability axis because when i read it's stopped me in my track.

00:05:48: It

00:05:49: is!

00:05:50: AI platforms will now assign unprompted objections to your brand

00:05:55: before the buyer even speaks.

00:05:57: Exactly, it fundamentally changes sales.

00:06:00: It

00:06:00: really does!

00:06:01: Imagine a prospect asks an AI to compare you and the competitor?

00:06:06: The ad doesn't just give a neutral list of features anymore….

00:06:09: Right...it's

00:06:09: not just bulleted lists..

00:06:10: It actively generates a downside or liability for your brand may be citing complex implementation process or lack customer support

00:06:18: And bases this on aggregated internet sentiment

00:06:21: Exactly....and presents that liability as objective fact.

00:06:25: That is wild because objection handling used to happen on a live sales call, right?

00:06:30: Where a rep could actually provide context or maybe on a carefully crafted landing page.

00:06:35: Right you had control

00:06:36: but now the AI handles the objection for the buyer in a split second completely out of your control.

00:06:41: You literally can't defend yourself

00:06:43: No!

00:06:44: So

00:06:44: how do we even start measuring this if it's all happening inside private chat window.

00:06:48: Well

00:06:48: were just getting visibility tools.

00:06:52: Nirapunj highlighted that Google Search Console is finally offering first-party reporting on AI overviews and AI mode impressions.

00:07:00: Oh, really?

00:07:01: That's huge!

00:07:02: Yeah it's basically the first real window marketers have into organic AI visibility.

00:07:08: And on the paid side things are moving just as fast right.

00:07:11: Bill Stathopoulos shared that ChatGPT just opened self-serve ads.

00:07:15: Yes,

00:07:15: at a twenty five dollar daily minimum.

00:07:17: I think Yeah

00:07:18: but the wild part is there's zero keyword targeting.

00:07:22: Not

00:07:22: all

00:07:22: You describe who you want to reach and AI matches your ad directly into relevant user conversations dynamically.

00:07:29: That

00:07:29: completely shifts the entire paradigm.

00:07:31: But let us follow the buyer journey here for second.

00:07:35: Let say you've successfully gamed the answer engine You built your citation core, you're on the AI shortlist and The buyer is now fully aware of you.

00:07:43: Right?

00:07:43: Awareness doesn't close deals.

00:07:45: Yeah How do you actually capture that intent?

00:07:48: That's where the traditional outbound stack Is completely falling apart right Now.

00:07:52: yeah totally because if you are highly visible to Ai but Your Outbound motion is still running On some manual fragmented You know six toolstack from twenty nineteen You

00:08:01: can't capitalize on that demand And we Are seeing a complete overhaul of how outbound is structured to fix that exact issue.

00:08:10: Shabir Mohsen shared a perfect operational example with this, he's using an AI go-to market agent that monitors over forty different buying signals simultaneously.

00:08:18: Forty signals?

00:08:19: Yeah it completely replaces the manual outgun stack.

00:08:22: It researches the prospects cross references all forty signals drafts sequences and runs entire workflow.

00:08:28: So humans just step in for actual sales calls Exactly

00:08:31: And

00:08:31: look at scale.

00:08:32: when deployed correctly Alex Vaca detailed a workflow using just four prompts for a sales development representative.

00:08:40: Just four prompts?

00:08:41: And it was scaled across more than two hundred and seventy-five B to B companies.

00:08:46: Those four core prompts generated seven point eight three million dollars in pipeline.

00:08:51: That's unbelievable!

00:08:52: It proves that deep research and list building are now fundamentally agent work.

00:08:57: Oh, and speaking of scaling that research there was the pre-event scoring model too.

00:09:01: Oh

00:09:01: right!

00:09:02: Vladimir Blagojevich's Model

00:09:03: Exactly.

00:09:05: He took thirteen hundred event attendees And automatically triaged them down to a hundred nineteen high value targets Before

00:09:12: even happen Right

00:09:13: before any conversations take place.

00:09:15: Wait hold on though I have push back here A little bit.

00:09:17: Ok

00:09:17: go for it.

00:09:18: Forty signals or Scaling prompts across hundreds companies.

00:09:21: Most sales teams I know can barely handle basic intent data from their own website without drowning in false positives.

00:09:28: That's true, and then they just end up spanning people.

00:09:31: So how is a hyper automated AI agent not just creating more noise?

00:09:35: I mean are we just automating the wrong part of the funnel here?

00:09:38: since

00:09:38: it valid question

00:09:39: because Steve Armenti pointed out that buyers initiate eighty percent of first conversations themselves.

00:09:45: Deals are ultimately decided by familiarity and relationships, so why aren't we trying to speed up cold-outbound if the buyer has already made their shortlist through AI?

00:09:54: I love you brought this up!

00:09:55: That is exactly what most companies fall into right now... Automating a broken process just means you fail at scale.

00:10:03: Exactly, faster garbage?

00:10:04: basically

00:10:04: Yes and your pushback raises the most critical issue in AI.

00:10:08: go to market right now which is the underlying data bottleneck.

00:10:11: The data decay.

00:10:12: Right.

00:10:13: Ryan Staley observed that B-to-B data decays at roughly thirty percent of year.

00:10:17: Wow!

00:10:18: Thirty percent.

00:10:19: Yeah

00:10:19: People change jobs.

00:10:21: Companies pivot Budgets freeze.

00:10:23: This datarot Is exactly why ninety five percent Of enterprise AI pilots fail.

00:10:28: So it's not that the AI model itself isn't smart enough, It is we are feeding in absolute garbage.

00:10:33: Precisely!

00:10:34: If you feed an AI stale wrong or thin data A highly confident perfectly written email sent to a person Is actually worse than doing nothing at all.

00:10:44: Damage as the brand.

00:10:45: Exactly You don't need smarter models You need honest pristine data.

00:10:50: Majvoji's case study on owner.com illustrates this perfectly.

00:10:54: Oh, I saw that they hit nearly a hundred million dollars in annual recurring revenue right?

00:10:58: In under four years.

00:10:59: But they didn't do it by just turning on quote-unquote AISDRs to blast more emails.

00:11:04: They did it by managing system throughput and driving deep organizational change around their data.

00:11:11: They treated AI as an operational shift, not just a software update.

00:11:15: Which is such a crucial distinction.

00:11:17: and actually speaking of operational shifts if you listening are finding these structural changes in B-to-B marketing As fascinating as we are?

00:11:24: make sure to hit subscribe so you catch all our future deep dives.

00:11:27: We are tracking this evolution constantly because

00:11:29: that organizational change Is really where the rubber meets The road

00:11:32: exactly.

00:11:33: You clearly can't run an AI native outbound motion with a siloed?

00:11:40: Structure or marketing teams has to fundamentally break down and rebuild.

00:11:43: It has too.

00:11:44: If you are a CMO listening right now, just look at your org chart.

00:11:48: You probably have team of channel specialists an SEO person A paid social person An email person

00:11:56: And according to Carolyn Healy who referenced Gartner's twenty-twenty eight projections That structure is going be completely obsolete Really?

00:12:04: Yeah!

00:12:05: Gartners states that sixty percent brands will use agentic.

00:12:08: one the one adoption.

00:12:10: This effectively spells the end of traditional channel-based marketing structures.

00:12:15: Wow, so The Marketing Leader's job is completely shifting?

00:12:18: It's shifting from being a campaign architect to be an systems designer.

00:12:22: But restructuring team is intensely political.

00:12:25: I mean people are protective of their roles.

00:12:26: Oh

00:12:26: absolutely

00:12:27: John Miller shared great story about DAXCO.

00:12:29: that shows what it actually takes to do this.

00:12:32: DAXCOS currently running over Eighty A.I agents, eighty.

00:12:36: but they didn't get there by making it a side hustle.

00:12:38: They didn't just tell their team to you know play around with chat GPT on Friday

00:12:43: right?

00:12:43: It wasn't a hobby

00:12:44: No!

00:12:45: They only achieved that scale By pulling the vice president entirely out of their day job To build a dedicated AI center of excellence.

00:12:54: Taking a VP offline to focus solely on AI orchestration is a massive commitment.

00:12:59: It

00:12:59: really is!

00:13:00: But it's necessary because, as Chris Culler pointed out, marketing roles are shifting back toward generalists rather than narrow specialists.

00:13:08: Interesting.

00:13:09: why generalist?

00:13:09: Because the AI is the ultimate specialist now...it can write code and draft copy and analyze spreadsheet which you need.

00:13:17: our generalists who can orchestrate all those special agents.

00:13:20: But let's talk about the danger of getting that orchestration wrong, because right now for The Average Team it feels like an orchestra where every single musician is playing a completely different song in their own practice room.

00:13:32: That has very accurate picture.

00:13:33: Right.

00:13:33: one person uses Claude another use as custom GPT someone else using AI writing tool.

00:13:39: they all have different prompts and absolutely no shared standard

00:13:42: And Orin Greenberg highlighted this fragmented individual.

00:13:46: AI adoption is actually the single largest tax on BWB marketing ROI right now.

00:13:51: A tax on ROI?

00:13:52: That makes so much sense!

00:13:54: It

00:13:54: creates this illusion of productivity.

00:13:56: when everyone uses AI for low friction tasks in their own silo you don't get a symphony, You just get a racket

00:14:03: Right.

00:14:03: The team was operating at exactly same headcount with slightly better grammar.

00:14:08: To fix this you have to prove the fundamentals first.

00:14:11: Okay, but how do you do that without just adding more tools to the pile?

00:14:14: You'd do it by hand!

00:14:16: Andrei Zenkovich broke down on how Backbase handled this transition.

00:14:20: They ran a completely manual account-based marketing pilot first...to prove their go-to market fundamentals Manual

00:14:27: like no AI at all

00:14:28: None.

00:14:29: they figured out the exact sequence.

00:14:31: The messaging got real human to reply All manually.

00:14:35: They didn't let AI automate anything until they had proven the process actually worked.

00:14:39: Because you cannot automate a process your team hasn't nailed down manually?

00:14:43: Exactly!

00:14:43: If the manual process is broken, AI just scales the chaos

00:14:47: And that brings up the risk of teams trying to build their own workarounds.

00:14:50: Rachel Roundy talks about the Ikea effect in software.

00:14:53: Oh...the

00:14:53: Ikea Effect?

00:14:54: Yeah it's the danger of teams vibe coding there own messy fragile tools instead relying on stable enterprise grade infrastructure.

00:15:04: Right, because a marketer might build the clever prototype that looks great in a demo but it completely falls apart production when hits real customer data.

00:15:12: Exactly!

00:15:13: Alex as a party's update on service now reinforces this reality.

00:15:17: actually they saw a nine-fold jump in production agentic AI customers The Nine

00:15:23: Fold Jump.

00:15:24: Yeah But that massive scale was driven entirely by strict governance not just deployment speed.

00:15:30: Safe orchestration at scale is where the real work is

00:15:32: because governance sounds super boring until your vibe coded AI agent starts hallucinating custom pricing to a key enterprise account

00:15:39: and legally binds you too, right?

00:15:41: Precisely.

00:15:42: Governance Is The Breaks That Allow You To Drive Fast Without Crashing Your Brand.

00:15:45: Okay so let's bring this high-level org transformation down to the individual practitioner.

00:15:49: if we have this new team of BtoB generalists how do they actually work with AI day today

00:15:55: it's changing fast

00:15:56: Because we are definitively moving away from just typing a question into Every single day at seven AM.

00:16:17: a workflow runs automatically.

00:16:19: It scans competitor updates, pulls account intelligence from across the web and drops a synthesized summary for him before he even wakes up.

00:16:28: No code required no manual trigger Exactly.

00:16:31: And Zohi Mustafa expanded on this with the concept of reusable Claude skills.

00:16:36: Instead of a marketer writing one off prompt over-and-over again to say analyze a campaign They package that capability into a repeatable building block.

00:16:44: But the real magic of those building blocks is what they can connect to, right?

00:16:48: Yes

00:16:48: and that's where Model Context Protocol or MCP comes in.

00:16:52: Can

00:16:52: you explain MCP really quickly?

00:16:53: for who might not know?

00:16:54: Sure Think of MCP as this secure universal translator between your AI and actual databases.

00:17:00: So instead if downloading a spreadsheet from tool and pasting it onto Claude MCP allows Claude securely reach through that tool run query itself And pull exactly what he needs.

00:17:11: That is a massive workflow shift.

00:17:13: I mean, Anna York used an MCP connection with Semrush and it completely changed her output.

00:17:18: Oh yeah!

00:17:18: Her keyword research?

00:17:19: Yeah she cut our keyword research time from four hours down to twenty minutes but It wasn't just about doing the exact same task faster.

00:17:26: The MCP allowed her to shift her entire focus

00:17:30: Right From keywords to questions.

00:17:31: Exactly.

00:17:32: Instead of traditional SEO asking what keywords people search for, the MCP allowed her to focus on specific questions that AI needs to answer before it decides to cite a brand.

00:17:45: It's like moving from checking ten different disconnected dashboards... ...to just asking a hyper-competent colleague who has already memorized every single file in your server

00:17:53: and can synthesize instantly.

00:17:55: Exactly!

00:17:56: Amelia Mahler made a fascinating point along these lines too.

00:17:59: She talked about connecting live meeting transcripts and client briefs directly inside Claude.

00:18:04: So the AI isn't giving generic internet scraped advice?

00:18:08: Right, it's looking at Live Proprietary Data And Giving a Prioritized Action Plan Based on that.

00:18:15: Which really makes me wonder Does this mean Context Engineering is now officially more important than Prompt Engineering?

00:18:20: Absolutely The prompt Is just trigger or context is intelligence.

00:18:24: That's great way to put.

00:18:25: Kieran Flanagan confirmed this dynamic perfectly.

00:18:28: He noted that a marketer's ultimate leverage used to be domain authority, right?

00:18:33: If you had high-domain authority your content won on Google.

00:18:36: Today AI and automated workflows are the new leverage.

00:18:41: The variable that separates a brilliant output from a useless one using the exact same AI model isn't how cleverly you phrased the prompt.

00:18:49: It's the context.

00:18:50: It is the depth, quality and structure of the contexts you provided.

00:18:55: Context wins every single time

00:18:56: Even in highly creative work.

00:18:58: Jessica Aries tested a carousel design across four different AI tools.

00:19:02: Oh I saw this test.

00:19:03: Yeah, she found that tools like Claude Design actually make structural design decisions based on the context provided rather than just blindly filling in a visual template.

00:19:13: So it actually understands what its making?

00:19:14: It understood the hierarchy of information and arranged the design logically much like human designer would purely because the context was engineered correctly.

00:19:23: Wow, so we've traced this incredible evolution today.

00:19:27: We've gone from the black box of discovery in AI answer engines to restructuring how we capture intent through AI driven outbound.

00:19:37: We looked at why you have to tear down your old marketing org chart, to govern all this.

00:19:41: Right!

00:19:41: All the way down into daily context engineering of an individual marketer's workflow.

00:19:46: it is a complete structural rewiring of B-to-B Marketing.

00:19:50: It Is.

00:19:51: And if we pull these threads together there s one final profound implication that every go-to market leader needs to internalize today.

00:19:58: Okay

00:19:59: What s That?

00:19:59: Well Patrick Berghoff introduced his concept of the Oscillation Tax.

00:20:03: The Oscillations Tax.

00:20:05: what does actually mean in practice?

00:20:07: Well, if we connect the oscillation tax back to our own Prasad's liability axis from our very first theme.

00:20:12: Oh

00:20:13: right!

00:20:13: The idea that AI generates unprompted objections?

00:20:17: Exactly...the warning here is stark If you cut your customer experience or brand building budget today just save money for a quarter.

00:20:26: You aren't taking temporary dip in metrics.

00:20:28: Okay You are permanently cementing negative reputation into weights of an LLM.

00:20:34: Wow Let that sink in for a second.

00:20:36: You are literally baking your own liability into the machine

00:20:40: exactly because AI models train on the aggregate sentiment of the web over time if Your service slips or your brand goes quiet and stops earning.

00:20:48: those third-party citations The negative reviews, and the lack of validation become part of the model's institutional intelligence

00:20:56: And you can't just buy a top Google ad to fix it next year.

00:20:59: no The machine has a very long memory.

00:21:01: Your brand's institutional intelligence and relationship capital are being judged by algorithms right now.

00:21:06: So if you're invisible today?

00:21:07: Or worse, If your viewed as the liability of AI today You simply won't even make it shortlist tomorrow.

00:21:14: That is a chilling thought honestly but incredibly necessary to understand!

00:21:19: You are building equity with this machine every single day And just can't fake

00:21:23: that... You really cant

00:21:24: If you enjoyed this episode.

00:21:26: new episodes drop every two weeks.

00:21:28: Also, check out our other editions on field marketing channel and partner marketing account based marketing.

00:21:33: Martek go to market and social selling.

00:21:36: Thank you so much for joining us for this deep dive And don't forget to subscribe So you don't miss out on what's next.

00:21:41: we'll see you next time.