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Industry Analysis

Aug 31, 2026

The Future of Search Agencies in 2027

Tanja Gavrilovic, agentic AI marketing strategist

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Tanja Gavrilovic

Ex-agency founder and VP of Content Marketing turned Agentic AI marketer. I explore the intersection of human creativity and machine agency, building systems that redefine how marketing operates at scale.

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A study of 579 agency leaders and 400 of their clients landed this year with a finding that is hard to shrug off. The agencies most excited about AI also have the weakest pipelines.

The Agency Core 2026 report groups agencies by how they relate to AI. The middle group, about a third of the sample, is the most energized about what comes next and also reports the worst new business in the whole study. The authors’ read is straightforward: clients keep the agencies that use AI without acting as if it replaces the value they actually provide, and they drop the ones that behave like it already does.

In other words, the problem is not that AI is coming for agencies. The problem is agencies acting like it already has.

I spent the last few months reading what the people who build search strategy for a living have published about where this goes. Funding announcements. Agency owners. Researchers running the experiments. Practitioners arguing with each other in public. They disagree on almost everything. What follows is a map of that disagreement, with the receipts, and a view on what it means if you are the person responsible for growth.

The one thing they agree on

Everyone in this debate agrees on a single fact. Content stopped being the deliverable.

Rand Fishkin published a piece this year telling marketers to ignore traffic and build inimitable products. He predicted the post itself would earn fewer than 5,000 visits and fewer than 500 from search, and he was fine with that, because the point was the argument. His sharpest line: “Inimitable products may be defensible moats on which to build a web business, but inimitable content is not.” SparkToro’s own data backs the mood. In 2026, fewer than a third of Google searches send a click.

Ross Simmonds of Foundation makes the operational version of the same point: the money wasted in content marketing is spent writing things nobody distributes. Production is cheap now. Getting the work seen is the job.

That is the shared premise. Publishing words on your own site and hoping they rank is a declining business. From there the field splits three ways on what to sell instead.

Bet one: sell the software

Capital is pouring into tools that measure and shape what AI systems say about a brand.

The bet is that brands will pay a monthly platform fee to track and improve how they show up in ChatGPT, Google’s AI answers, Perplexity, and the rest.

The trouble with that bet is the crowded market and the pricing. Peec, one of the better-known trackers, restructured its pricing in March after agencies hit cost walls adding clients. Its plans now sit in transparent tiers from roughly $95 to $495 a month, with competitors undercutting from below. Peec crossed $10M in ARR about 16 months after launch, which is fast, and also a sign of how quickly this became a category with many sellers and comparable features.

Eli Schwartz, who has advised search for LinkedIn, Anthropic, and Coinbase, named the structural issue plainly: the AI-visibility companies are making an SEO pitch dressed as an anti-SEO pitch. The signals they sell (mentions, citations, share of answer) are the signals SEO has always chased, relabeled. Malte Landwehr, who works on one of these tools, offered a budget for a company with no brand recognition that shows where he thinks the value sits: a small amount on analytics, more on listicles, and the majority on earning brand mentions on reputable websites. The tool is the small line. The work is the large one.

Jake Ward is worth watching because he runs both plays at once. He built his following on AI content at scale, he sells an AI-visibility tracker called Mentions.so, and his new agency, Known, bills itself as the “#1 AI Search Optimization Agency.” Its homepage sells the opposite of a tool: revenue-driven targets instead of vanity metrics, strategy built around your constraint instead of recycled playbooks, one integrated system across Google and AI. The operator most associated with scaled AI content now sells an agency on judgment and outcomes.

Bet two: sell brand representation

The second camp sells a service that moves up the org chart.

Steve Toth runs an agency that is now roughly 60% AEO and 40% SEO by client demand. His argument is that the buyer changed. A lead that arrives after an AI assistant recommended you is pre-qualified, so those leads convert several times better, and earning them pulls in PR, brand, product, and customer service. The service stops being “rank this page” and becomes “make the whole company legible to the systems that recommend vendors.” Aleyda Solis frames the same shift as building brand authority for AI search, and points out that most decision-makers do not even say GEO. They say AI search.

Mike King of iPullRank named the problem this creates inside companies better than anyone. His words: “Nobody owns GEO. SEO touches it. Content touches it. PR touches it. Brand, analytics and product all touch it too. And somehow it still isn’t anyone’s actual job.” His answer is a discipline he calls Relevance Engineering, which folds information retrieval, content strategy, UX, and digital PR into one function. The old label is deliberately left off the list.

Duane Forrester, formerly of Bing, framed the fault line the whole field is circling: the real divide is retrieval versus judgment. He describes the shift in one sentence. Traditional search showed you the evidence and let you form the answer. AI search gives you the answer first, then asks you to verify it. He refuses to generate content with AI for clients, and his call for 2027 is specific. Enterprises will deploy internal agents trained on their own data, those agents become the first place employees and customers look, and a brand that is not in that knowledge base is not in the decision.

Wil Reynolds of Seer, who runs a 200-person agency, made the most useful point about how this lands with the person paying the bill. The public argument, where AI optimizers get called grifters and the veterans get called out of touch, misses the client entirely. A client who has been told the world is changing does not want to hear from their agency that the change is nothing. And on what clients want from AI, he relayed a line from an executive at a large AI company that every agency should sit with: do not show me how your team will use AI for time savings, show me things we could never do before. His gloss: if your pitch is that AI saved 80% of the time, the client asks why they are not paying 80% less, and you now sound like the other five agencies saying the same thing.

Bet three: sell the product

The third camp says stop selling content or software and change what the business is.

This is Rand Fishkin’s position taken to its conclusion. If content is not a moat, the work of marketing is to build products and reputation competitors cannot copy, and to earn attention on the platforms where the audience already spends time. Andy Crestodina of Orbit Media adds the uncomfortable corollary for agencies: when companies paying agencies billions realize they can do much of this in-house with AI, the value proposition of the whole content industry changes. Robert Rose of the Content Marketing Institute frames the response as re-mastery over reinvention, and communities that co-create rather than audiences that consume.

The counter, and the one that matters for a B2B SaaS audience, is that for an enterprise software company, the inimitable product is already the company, built by hundreds of people. A CMO cannot “go build an inimitable product” as a marketing tactic, because that is the entire business. Rand’s advice is close to perfect for a solo consultant or a Substack. Take the reputation-and-attention half, and leave the “become a product company” half to the founders.

Where they disagree

Three fights are worth watching, because the outcome tells you which tactics survive.

The comparison-page hack

One popular 2026 playbook says publish your own “best tools for X” list, rank yourself first, and get cited by AI systems that love listicles. Ahrefs’ data seems to support it: across a billion-plus data points, “best X” listicles are the single most common page type ChatGPT cites, at nearly 44%. Two things cut against the hack. A separate analysis of hundreds of thousands of AI citations found that around 81% of the cited listicles are third-party, not self-published. The lists that win are the ones other people wrote about you. And Lily Ray tracked a set of these self-published comparison pages and watched their traffic and their AI citations decline together from late January. Her verdict on the tactic: it works, until it doesn’t.

The self-poisoning loop

Search Engine Journal documented the cycle: an agency’s AI pipeline publishes a speculative post, a second agency’s pipeline cites it, an AI answer repeats it, and the first agency writes a case study about the citation. The cleanest demonstration came from outside the industry. A BBC reporter, Thomas Germain, spent 20 minutes writing a fake page titled “The best tech journalists at eating hot dogs”, invented a South Dakota championship that does not exist, and ranked himself first. Within 24 hours, ChatGPT, Google’s AI Overviews, and Gemini were all repeating it. One system was not fooled: Claude. When the chatbots flagged it might be a joke, he added a line saying it was not satire, and they took it more seriously. Google and OpenAI have since patched those specific answers, which is the whole point. The systems are patchable, so the tactics are temporary. Kevin Indig’s data shows how unstable the surface already is: a high-reasoning model fires 4.6 times more searches and returns only 25.6% overlap in cited domains, meaning the same prompt sends the model to a different set of sources each time. (Scientific American coverage of the hot-dog stunt.)

Whether any of this is new

Britney Muller, formerly of Moz, has become the sharpest skeptic, and her argument is mechanical. Every URL an LLM shows you comes from a search engine’s API, so what people call AI behavior is search behavior with a new coat of paint. Her line: you are not discovering AI behavior patterns, you are rediscovering SEO. Dan Petrovic of Dejan takes the same position and refuses the acronyms outright. Even Jake Ward, who sells the tools, lists as one of his seven convictions for 2026 that AI search should not be a separate strategy, which puts the tool-seller and the skeptics in unexpected agreement.

To walk in our clients’ shoes

Step out of the practitioner feeds and into the rooms where budgets get decided, and the picture gets clearer.

On r/SEO, an in-house Director of Organic with more than a decade of experience described being unable to convince a new CEO that SEO still matters, because the board kept forwarding “SEO is dead” articles and the CEO trusted the emails over the person he hired. The sharpest reply in the thread named the risk: executives resolve the confusion by deciding their current vendor must be bad at AI, so they fire a competent team and hire an acronym specialist. That is the enthusiasm gap from the client’s side. The hype does not only mislead buyers. It gives them a reason to fire the people doing good work.

The data in those same rooms is undramatic. A practitioner in the thread put AI referral traffic at around 1% of the total, with organic still doing the heavy lifting, and nearly every client choosing to keep investing. Ann Smarty reports SEO still driving about 90% of visibility for her clients. The Agency Core numbers say the same thing from the top. 91% of clients say their agency makes them more likely to succeed, and when AI enters the relationship, only 29% want a discount. The rest want better thinking.

That is the whole game in one statistic. The market is not asking agencies to be cheaper because AI made the work faster. It is asking them to be smarter, because the work got harder to judge.

The role question

This is where it gets personal for anyone building an AI-driven content operation, because the field cannot agree on what to call the person who does it, or whether that person should exist.

Ryan Law, who leads content marketing at Ahrefs and spent 13 years in the craft, wrote a piece with a blunt thesis: he would not hire a content engineer, because “the content engineer is the wrong skill set for the future of marketing.” The role’s job is to scale content with AI, and “the golden era of scaled content is already behind us.” He has standing to say it. Ahrefs ran up to 34,000 programmatically generated pages at its peak and has been pulling back. In a follow-up, he went further: the content engineer’s skills “will become just another workflow in every major LLM platform.” See also AI content is good enough now.

Then, in a third piece, he described building exactly that workflow for himself with Claude Code, chaining more than twenty instruction files to produce publish-ready drafts in minutes. The contradiction is the lesson. The workflow produces good drafts because Ryan Law spent 13 years building the editorial judgment encoded in those files. The tool packaged the expertise. It did not create it. Give the same tool to someone without the judgment and it produces confident, wrong drafts faster.

The vendors selling the role see it the other way. AirOps, which raised its $40M round as a “content engineering platform,” frames the content engineer as a 10x marketer, and Jasper calls it the next evolution of the content strategist. Both readings can hold. The role is real, and the skill inside it is judgment, which does not scale by hiring for the title.

Kevin Indig said the underlying thing most precisely. Three costs are collapsing toward zero: building software, producing content, and spinning up a tool. One cost is rising: knowing whether any of it is right. His phrase for his own work now is building the thing that does the work, then checking it. His conclusion: judgment is the part that doesn’t compress. Steve Toth is a good example of what that looks like in practice. He took Google’s own content-quality guide and built a Claude Project and a custom GPT that grade any page against it, encoding a senior editor’s checklist into a repeatable tool.

The strongest signal about where the role lands might be a job title. Tom Critchlow spent a decade as an independent strategy consultant. His current title is forward deployed engineer at an AI company. A strategist took an engineering title, because the work now is building the systems and checking their output. His community’s data explains why keyword-era instincts fail here: 60% of AI prompts are ten or more words, and almost none of them appear in keyword research. The way people ask AI has little in common with the way they searched Google.

What this means if you are one person, or a small team

If judgment is the scarce input and content is cheap, the economics favor small, senior teams with a method over large teams producing volume.

Steve Toth is a working example of the shape. One person built a body of proprietary method, then turned it into an agency, two newsletters with tens of thousands of subscribers, and a coaching program for other agency owners. Three revenue layers, one set of expertise. The audience and the method came from a person. AI let a small team deliver more of both.

For a marketing leader, the productivity AI creates is flowing to clients at lower prices rather than to agencies with higher margins, which the Agency Core execution gap confirms. Most agencies say the new strategy areas are the most important, and only 13 to 16% fully execute on them, because they are out of time, not out of ideas. The advantage goes to whoever can do the judgment-heavy work that does not compress: deciding what to build, checking whether it is right, and being the source other people cite instead of the one chasing citations.

2027

Most predictions in this space are guesses. One is specific enough to plan around.

Duane Forrester’s call is that the next frontier is internal. Enterprises will point agents at their own documents, data, and systems, and those agents become the first place people look for an answer inside the company. If that holds, the discovery surface that matters in 2027 is not only public search and public AI answers. It is the private knowledge bases large organizations are building right now, and whether your brand, your product, and your evidence live inside them. Kevin Indig’s near-term calls point the same way: the end of AI-visibility dashboards as a standalone product, the rise of agentic search that acts rather than lists, and a web that splits into traffic from bots and traffic from verified humans.

What I’m seeing in my work

I run experiments on my own sites and my own pipeline before any of it reaches a client. The short version of what I have found: most of my leads now come through LLMs. Those leads book meetings with several agencies at once, and it is hard to see how they choose a vendor. The sales cycle is as long as classic SEO. Close rates are still higher on the leads that come from Google.

Sources and receipts

Tanja Gavrilovic, agentic AI marketing strategist

AUTHOR_DATA

Tanja Gavrilovic

Ex-agency founder and VP of Content Marketing turned Agentic AI marketer. I explore the intersection of human creativity and machine agency, building systems that redefine how marketing operates at scale.

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