Marketing Tactics

Most agency leaders are asking the wrong questions: “How do we use AI to do our work faster?” and “Do we need fewer content writers?” The question that actually matters—the one that clients are already asking—is simpler and more brutal: “Do we need an agency at all?”

That shift is already happening.

We surveyed CMOs and VP-level marketers on how they’re evaluating agencies today, and the answers were telling.

27% of marketing leaders said they’ve already started replacing parts of agency work, specifically copywriting and basic production, with internal AI tools or agents. 

One CMO put it plainly: 

“My first consideration is whether I can build an agent in Claude to do the work a marketing agency would have initially done. If not, I’ll consider the agency.”

Read that again.

You’re no longer competing with other agencies. You’re competing with a $20/month Claude subscription.

The agencies that survive this won’t do it by getting faster. They’ll be the ones who reposition their B2B marketing agency strategy entirely; the ones who’ve stopped selling execution and started selling what AI genuinely can’t replicate: judgment, taste, domain expertise, and the ability to tell a client what not to do.

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Static data visualization is a silent engagement, productivity, and conversion killer. Marketers have been uploading data to tools like Flourish, Datawrapper, and Tableau for years—then manually formatting, exporting, and embedding the results.

It works, but it’s also slow and what took weeks to create is often lost and forgotten in a four-second scroll.

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Google’s AI Overviews don’t just summarize the top result. They cite whoever answers the query best, regardless of whether that page ranks first, fifth, or not at all. 

Your brand could be invisible in AI answers while your domain authority sits at 60. That’s the new reality, and most SEO dashboards won’t tell you it’s happening.

When we started this project in March 2026, there wasn’t a dedicated Google Search Console report for AI visibility. Google has since introduced generative AI performance reporting, and third-party tools have improved too. But we wanted something simpler: a way to run our own queries, see which brands appeared, and compare those results with competitors.

So we built one.

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LLM hallucinations are a known issue. But as frustrating as they may be, hallucinated AI citations are just a symptom.

Sometimes an AI-generated answer or article links to a page on your site that never existed. The URL looks perfectly plausible. The content isn’t there. And that invented link can reveal a topic people expect your site to cover.

That’s the gap worth paying attention to.

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Google AI Overviews are rapidly becoming the first thing users see for informational queries. That citation layer is increasingly shaping perception before a user even looks at the organic results.

But most teams have no idea whether they’re showing up in these citations or not.

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SEO used to reward depth. If your article was an exhaustive piece on a topic that covered every angle and included a few keywords, clicks would usually follow.

Today, search rewards precision over sheer depth.

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Tracking AI visibility used to be largely a black box.

Third-party tools like Semrush and Ahrefs gave marketers ways to estimate where their content appeared in AI-generated answers. But first-party reporting was limited. You could monitor citations, but you couldn’t easily verify them against data from the search platforms themselves.

That changed in February 2026, when Microsoft introduced AI Performance in Bing Webmaster Tools.

For the first time, publishers could see which pages Microsoft Copilot, Bing’s AI answers, and select partner integrations were citing, along with the grounding queries associated with those citations.

Microsoft has since expanded the report with query intent, topic-level visibility, citation share, and new ways to identify content opportunities.

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The problem with competitor content analysis isn’t that it doesn’t work. It’s that doing it manually at any meaningful scale is unsustainable. 

You either do it rarely enough that it’s useless, or you burn your team’s time on a process that collapses under its own weight the moment you try to run it across more than a handful of keywords.

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Most educational content shows you the polished end result. The best practices, the final framework, the “what works.”

But what often gets left out are the setbacks, the failed experiments, and the messy process that actually leads to those results.

That’s the gap we’re closing with this new series.

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