Marketing Tactics

YouTube is already a major part of how people discover content, learn new skills, and research products. And AI search is creating more ways for video content to surface outside YouTube itself.

But for B2B marketers, the interesting question isn’t whether YouTube is big. It’s whether they understand which videos actually perform, and why.

So why are so many marketing professionals avoiding this opportunity for YouTube growth?

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Search isn’t just about finding the right website anymore.

Google can answer a question directly through AI Overviews. ChatGPT can compare five products without sending someone to a single review page. And buyers can research an entire category, narrow their shortlist, and decide which brands are worth considering before visiting a company’s website.

That’s a fundamental change to how discovery works.

AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are the names marketers have given to adapting to that shift. There’s still plenty of hype around both. But the changes to rankings, citations, brand visibility, and organic traffic are measurable.

The more useful question isn’t whether AEO/GEO is just SEO with a different name. It’s how AI has changed the search journey, and what marketers need to do differently.

Here’s what the data tells us.

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People aren’t just Googling anymore; they’re asking ChatGPT, Claude, and Perplexity to make decisions for them. “What’s the best CRM for startups?” “Which landing page builder should I use?”

The challenge is that you can’t see where ChatGPT looks before it picks a winner: the queries it uses to retrieve information and build AI recommendations are completely invisible.

Until now.

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The problem with B2B AI marketing tactics right now is that everyone’s selling certainty in a system that’s changing weekly.

LinkedIn is drowning in posts about “guaranteed” AI strategies: screenshot after screenshot of viral wins, tools that “definitely” get you ranked, and carousels that “always” perform. 

But when you actually test them, most fall apart. Not because the tactics are completely wrong, but because they’re presented without context, caveats, or an honest assessment of what changed since they stopped working.

We’ve come across a handful of interesting AI and B2B studies and strategies worth sharing.

In this article, we’ll look at AI tactics that actually work, what’s overhyped, and how to pressure-test similar claims when you see them.

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Planning content around search volume data alone while LLMs are running numerous backend queries is the fastest way to miss out on content opportunities.

In a 2025 study of 501 prompts using Gemini 3 with Google Search grounding enabled, Seer Interactive found that 95% of the generated fan-out queries had zero tracked monthly search volume. Zero.

So every time you open your keyword tool and filter by volume alone, you’re potentially missing the specific questions AI search systems investigate when answering buyer prompts.

Discovery is moving from keywords and rankings to AI-driven chat experiences. And the real advantage isn’t when you train AI to recognize your brand in these experiences,  when you get AI to recommend it.

Alex Birkett walked through how to extract topics from customer data, reverse-engineer citation patterns, and use a three-part validation model to create an effective content strategy for AI search.

Here’s the system.

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You’ve been there. You start building lead scoring in HubSpot or Marketo, and immediately fall into dropdown purgatory: endless clicking through menus, assigning point values to job titles, email opens, and webinar attendance.

Weeks later, you launch it. Before you know it, you’re stuck QAing, checking how your rules affect MQL flow, and realizing you may have over-scored or under-scored in ways that ripple through your ABM funnel. 

The problem isn’t the tool. It’s the approach.

Manual lead scoring treats every signal equally and forces you to guess point values before you see the data. You’re building a lead scoring model based on intuition, instead of patterns. And once it’s live, changing those rules means clicking through the same menus again.

ABM fit scoring is the faster, saner alternative.

Instead of scoring individual leads based on behavior, you’re scoring accounts based on how closely they match the patterns of customers who actually closed.

This article outlines how Steve Armenti, founder of twelfth agency and former ABM lead at Google, uses AI to build fit scoring outputs using enriched account data, a scoring prompt, and a scored CSV (ideally with confidence indicators and top drivers). 

This isn’t about replacing HubSpot or other platforms entirely. It’s about using AI to do the pattern recognition work that humans are terrible at, then importing those scores into whatever CRM you’re using.

Here’s the playbook.

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The AI search conversation has devolved into a mess of conflicting data points, breathless predictions, and cherry-picked statistics. 

One report tells you to panic while another says nothing’s changed. Meanwhile, marketing buzz swings between “Google is dead” and “AI is overrated,” depending on which study dropped that week.

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Brainstormed personas aren’t customer research. They’re easy to build, easy to forget, and often disconnected from how people actually buy because they’re based on opinions instead of evidence.

AI can help replace that guesswork with actual customer evidence. Feed it purchase data, surveys, reviews, and competitive research, and it can surface patterns that would take a team much longer to identify manually.

The result should be a working customer avatar that helps you decide who to target, what to say, and which messages are worth testing.

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B2B audience research shouldn’t be a luxury item. If insights require a massive budget, most teams will never get them. 

You don’t need a six-figure research sprint to learn what people want. The conversations you need are already public, sitting on Reddit, LinkedIn, review sites, and forums, waiting to be mined. The only thing stopping most marketers is habit and a misguided belief that market research needs an agency badge to be legitimate.

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