Best of LinkedIn: AI in B2B Marketing CW 47/ 48

00:00:00: This deep dive is provided by Thomas Allgaier and Frennis based on the most relevant LinkedIn posts about AI and B to B marketing in calendar weeks, forty seven and forty eight.

00:00:09: Frennis is a B to B market research company, helping enterprise marketing teams sharpen their strategies and outreach with customer segmentation.

00:00:17: ideal customer profiles and deep dives.

00:00:20: Customer needs analyses and buying center insights.

00:00:23: And that idea of a sharpened strategy is it's exactly what the B to B world was focused on in these two weeks.

00:00:30: It's been really interesting to see.

00:00:31: How so?

00:00:32: Well, there's been this noticeable shift, a really necessary one, I think.

00:00:36: We're finally moving past the, you know, the endless hype cycle and the constant chasing of the next shiny tool.

00:00:42: Absolutely.

00:00:43: The conversation is less about if we should use AI and more about how we actually make it work reliably.

00:00:48: That's

00:00:48: it, exactly.

00:00:49: The whole discussion on LinkedIn was about discipline execution, structured workflows, and this is the big one, organizational readiness.

00:00:55: The mission now is to stop doing scattered pilots and start building real scalable systems that actually grow the pipeline.

00:01:01: Okay, so based on that, we've kind of organized the key insights into three areas.

00:01:05: Right.

00:01:06: First, we'll cover the non-negotiables, the foundational changes in strategy in org discipline.

00:01:11: Then, we'll get into the specifics of AISDRs, where we're seeing some surprising results.

00:01:17: And finally, the move towards specialized agent workflows.

00:01:21: The new GTM operating system.

00:01:23: Exactly.

00:01:25: Okay, so let's unpack this.

00:01:26: Let's start with that first thing, because it's the most critical one and maybe the most uncomfortable truth we saw.

00:01:32: Which is?

00:01:33: AI is an amplifier, not a fixer.

00:01:35: It's an acceleration engine.

00:01:36: And if you point an engine like that at a broken wall, you just get a bigger crash

00:01:40: faster.

00:01:41: So it's like the ultimate x-ray for your processes.

00:01:43: It exposes every single weakness you thought you were hiding with manual work.

00:01:47: Precisely.

00:01:47: The consensus on this was pretty clear.

00:01:49: AI just amplifies a weak go-to-market strategy.

00:01:52: It helps you fail faster.

00:01:54: People like Harshavan Kailapati and Amy Carmichael both warned against this idea that AI is some magic wand that will fix a broken process.

00:02:02: So if my GTM strategy is weak, what does AI actually do to it?

00:02:05: Just send bad emails faster.

00:02:07: It does that, but it's more systemic.

00:02:09: So if your ideal customer profile is fuzzy or your messaging is just confusing, AI doesn't clarify it.

00:02:15: It takes those fuzzy inputs and scales them to a hundred thousand prospects.

00:02:19: You basically guarantee failure across your whole market, not just one territory.

00:02:24: And when people actually try to fix the process, what's the number one blocker they hit?

00:02:29: I'm guessing it's not the AI model itself.

00:02:31: No, not at all.

00:02:32: It's something much more mundane and uncomfortable for a lot of teams.

00:02:37: Data quality.

00:02:37: Data

00:02:38: quality.

00:02:38: Torben Rint was very direct about this.

00:02:40: He said, messy CRM data hygiene is the silent killer.

00:02:44: It's the structural reason.

00:02:45: these powerful sales tools, including AISDRs, just fail to deliver ROI over and over again.

00:02:51: The

00:02:51: classic garbage in, garbage out.

00:02:53: Except now, the garbage is being produced at an exponential scale.

00:02:57: If your AI is trying to personalize outreach based on data that's forty percent incomplete, the personalization isn't just useless, it's actively harmful.

00:03:04: Right, you can't build a Ferrari on a crumbling foundation.

00:03:08: And this data issue quickly becomes a bigger conversation about organizational maturity.

00:03:13: Philippe Rutens pointed out that only about one percent of companies have actually reached what he'd call full AI maturity.

00:03:20: Just one percent, that's low.

00:03:21: It is.

00:03:22: And to get there, he says you have to fix the GTM plumbing first.

00:03:26: Fixing the plumbing, that sounds like a big RevOps project.

00:03:28: Exactly.

00:03:29: Rutens emphasizes building an AI-ready operating model.

00:03:33: You need sales, marketing, and RevOps aligned, all using the same playbook, ensuring data flows correctly before you launch the next GiniPilot.

00:03:42: It's systemic change.

00:03:44: And that kind of change needs a change in talent too.

00:03:47: Jessica Aries made a great point about this.

00:03:48: She said, the real risk isn't using AI wrongly.

00:03:52: It's having a team that isn't ready to use it at all.

00:03:54: Why does AI expose those skill gaps so much?

00:03:57: Because AI doesn't replace the strategist.

00:04:00: It demands one.

00:04:01: Aries says it just lays bare all the existing gaps and things like structured thinking.

00:04:07: or the ability to identify a good use case.

00:04:09: If your team can't give the AI a high-quality prompt and a clear goal, all that efficiency doesn't matter.

00:04:16: So we're back to investing in the human capacity first, otherwise we're just accelerating confusion.

00:04:22: And that focus on solid human strategy is the perfect pivot to our second theme, which was, you know, the most discussed application out there, AISDRs.

00:04:30: Sales development representative automation.

00:04:33: Right.

00:04:34: And this is where it gets really interesting because the companies that have fixed their plumbing are starting to see some very real results.

00:04:39: Yeah, the difference between the pilots from last year and the results now is just staggering.

00:04:44: Michael Yahoshua noted at Sastronaut AI that AI-SDRs only got truly effective this year around Q-II.

00:04:51: What changed?

00:04:51: Better models.

00:04:52: Specifically, he mentioned things like Claude Four.

00:04:55: The tech just got better.

00:04:56: Hallucinations dropped, the outputs got tighter, and they could hold a conversation.

00:05:00: That stability led to huge results.

00:05:02: Like, what are we talking about here?

00:05:04: Well, Dan Elkheim summarized some social learnings, and they were citing things like, seven hundred thousand dollars in pipeline.

00:05:11: Wow.

00:05:11: And meetings being booked daily.

00:05:13: But, and this is the key.

00:05:15: only when it was combined with rigorous human in the loop oversight and deep hyper segmentation.

00:05:21: You

00:05:21: can't just blast an entire database.

00:05:23: Absolutely

00:05:24: not.

00:05:24: A company called Artisan, for example, reported getting seven point five percent response rates.

00:05:29: In B to B cold outreach, seven and a half percent is massive.

00:05:32: The benchmark is what, one or two percent?

00:05:34: Exactly.

00:05:35: The key was that extreme personalization, but still guided by humans.

00:05:39: What's fascinating to me is how this is already causing a pivot away from general cold outreach and more toward high intent mining.

00:05:47: That's

00:05:47: a huge shift.

00:05:48: Amon Kerr showed a great example.

00:05:50: They built an automated system specifically to mine LinkedIn on viral posts in their industry.

00:05:55: So

00:05:55: instead of emailing someone who just works at a target company, you're talking to someone who literally just engaged with a relevant problem in public.

00:06:02: Precisely.

00:06:03: They are turning engaged prospects directly into booked calls.

00:06:08: And the insight is that these prospects convert up to four times better than a cold list.

00:06:12: AI is best used to find the warmest signals.

00:06:15: And hold on.

00:06:16: If the tech is this good-generating huge pipeline getting these incredible response rates, why did we see such a pushback against the whole AI employee narrative?

00:06:27: Amos Barjosev had a really compelling take on this.

00:06:29: What does he say?

00:06:30: He looked at those first wave startups like Eleven X and Artisan.

00:06:34: They initially sold the idea of AI replacing humans and the market just decisively rejected it.

00:06:39: Why?

00:06:40: Because in complex high value B to B deals, buyers demand human engagement.

00:06:45: They need trust, creativity.

00:06:46: The AI can do the transactional work, but it doesn't have the nuance for a big enterprise deal.

00:06:51: So those companies had to pivot their messaging fast from AI employees to augmenting human... intelligence.

00:06:57: So

00:06:57: it's about making every SDR a one hundred X version of themselves not replacing

00:07:01: them.

00:07:01: It's all about amplification.

00:07:03: Which brings us back to the need for human discernment.

00:07:05: Taylor John talked about this dirty secret.

00:07:08: he's hearing from sales leaders.

00:07:09: That reps using chat GPT for meeting prep are consistently missing critical deal context.

00:07:16: Things they would have picked up if they'd done the research manually.

00:07:19: So the AI is making them efficient, but less effective on the deals that actually matter.

00:07:23: Exactly.

00:07:24: If the AI gives you ninety percent of the info, but misses the ten percent that actually closes the deal, it's a net loss.

00:07:31: The best leaders are now teaching not just how to use AI, but when not to.

00:07:36: The need for that human judgment is not going away.

00:07:39: Not at all.

00:07:39: And that realization really moves us into our third theme.

00:07:43: the rise of orchestration and specialized agents.

00:07:46: We're moving beyond the idea of one single AI tool to rule them all.

00:07:50: Right.

00:07:50: It's more like building a custom tech stack of agents.

00:07:53: Yes.

00:07:53: Aditi Kinvasara and Raveena Oh both highlighted this.

00:07:57: The question isn't, should we use an AI agent anymore?

00:07:59: It's, which specialized agent owns this job in our stack?

00:08:04: And we saw some concrete examples of this paying off in a big way.

00:08:07: We

00:08:07: did.

00:08:07: No Rasmussen explained how his team cut their GTM work by sixty percent.

00:08:12: Sixty

00:08:12: percent.

00:08:13: By chaining specialized agents together.

00:08:15: They had a scraping agent, like Clay's AI agent, to pull data.

00:08:19: Then an AISDR, like AISDR for the outreach.

00:08:22: And then a research agent.

00:08:24: to enrich the leads that came back.

00:08:26: That's a

00:08:26: powerful workflow.

00:08:27: It collapses the time spent on all that prep work.

00:08:30: It's

00:08:30: compounding ROI.

00:08:31: Ryan Staley reported something similar, taking a four-hour GTM process down to just fifteen minutes by stacking tools for things like deal coaching and proposal generation.

00:08:41: It only works if the agents are specialized and they talk to each other.

00:08:44: And we're also seeing platforms recognize that the bottleneck isn't just at the top of the funnel anymore.

00:08:49: Right.

00:08:49: It's the post-meeting follow-up where deals die.

00:08:52: Olaf Matthae from Mixmax launched their AI sales execution platform to solve exactly that problem.

00:08:58: What are they doing?

00:08:59: They're using specialized co-pilots for your inbox, for meetings, for engagement, all focused on maintaining momentum, capturing next steps, and just keeping the deal clean.

00:09:08: They're applying AI to the middle and bottom of the funnel.

00:09:11: And this all circles back to strategy, doesn't it?

00:09:13: Kieran Flanagan made the point that AI is forcing marketers to optimize for the customer instead of the data.

00:09:20: That's a fantastic distinction.

00:09:21: What does that mean in practice?

00:09:23: It means because AI can handle the repetitive data work.

00:09:26: You know, the click metrics and vanity KPIs.

00:09:28: marketers can actually focus on customer centricity.

00:09:31: They can use these tools to build rich interactive customer personas and focus on motivations and pain points instead of just the next click.

00:09:40: It brings the focus back to human insight driving the strategy.

00:09:43: And this all points to a huge long-term trend that Koen Stam summarized.

00:09:48: He argues we're moving toward truly autonomous growth systems by twenty twenty six.

00:09:52: Autonomous growth systems.

00:09:54: What does that look like?

00:09:55: It means scaling relies on user-centric architecture and eliminating friction, not just adding more headcount.

00:10:01: This requires that new AI-powered operating system that Lucy Hackman described, where relevance is everything.

00:10:07: And only the companies with the cleanest plumbing and the clear strategy will be able to compete.

00:10:11: Exactly.

00:10:12: OK.

00:10:13: So wrapping up here.

00:10:14: So what does this all mean for you right now?

00:10:17: I think the core tension is really clear.

00:10:18: The market demands massive efficiency and scale from these automated systems.

00:10:23: But the real value, the thing that differentiates you, that still comes from clear human strategy, meticulous data discipline, and experienced human judgment.

00:10:33: You can automate the execution, but you have to lead the thinking.

00:10:37: That's

00:10:37: it.

00:10:38: If you enjoyed this deep dive, new deep dives drop every two weeks.

00:10:42: Also check out our other editions on account-based marketing, field marketing, channel marketing, martech, go-to-market, and social selling.

00:10:49: And here's a final thought to leave you with, drawing on a warning from Leah Thorin.

00:10:54: Given how easily AI can generate technically perfect but strategically irrelevant content and what people are calling AI slump, how are you ensuring your AI workflows generate genuine insight and creativity that actually differentiates you rather than just accelerating your rush toward organized mediocrity?

00:11:10: Thank you for listening and don't forget to subscribe.