Jan 6 2026
AI-Generated Content Isn’t The Problem, Your Strategy Is

“If AI can write, why are we still paying writers?” For any CMO or senior manager on a budget, you’ve probably already had a version of this conversation. It’s a seductive idea. After all, humans are expensive and can take hours or even days to write a single article. So, why not replace them with clever machines and watch the costs go down while productivity goes up?

It’s understandable. Buffeted by years of high inflation, high interest rates, and disrupted supply chains, organizations around the world are cutting costs wherever they can. These days, instead of “cost cutting,” CFOs and executive teams prefer the term “cost transformation,” a new jargon for the same old problem.

Whatever you call it, marketing is one department that is definitely feeling the impact. According to Gartner, in 2020, the average marketing budget was 11% of overall company revenue. By 2023, this had fallen to 9.1%. Today, the average budget is 7.7%.

Of course, some organizations will have made these cuts under the assumption that AI makes larger teams and larger budgets unnecessary. I’ve already seen some companies slash their content teams to the bone; no doubt believing that all you need is a few people capable of crafting a decent prompt. Yet a different Gartner study found that 59% of CMOs say they lack the budget to execute their 2025 strategy. I guess they didn’t get the memo.

Meanwhile, some other organizations refuse to let AI near their content at all, for a variety of reasons. They might have concerns over quality control, data privacy, complexity, and so on. Or perhaps they’re hanging onto the belief that this AI thing is a fad or a bubble, and they don’t want to implement something that might come crashing down at any moment.

Both camps likely believe they’ve adopted the correct, rational, financially prudent approach to AI. Both are dangerously wrong. AI might not be the solution, but it’s also not the problem.

Beeching’s Axe

Spanish philosopher George Santayana once wrote: “Those who cannot remember the past are condemned to repeat it.” With that in mind, let me share a cautionary tale.

In the 1960s, British Railways (later British Rail) made one of the most short-sighted decisions in transport history. With the railway network hemorrhaging money, the Conservative Government appointed Dr. Richard Beeching, a physicist from ICI with no transport experience, as the new chairman of the British Transport Commission, tasked with cutting costs and making the railways profitable.

Beeching’s solution was simple; do away with all unprofitable routes, identified by assessing the passenger numbers and operational costs of each route in isolation. Between 1963 and 1970, Beeching’s cost-cutting axe led to the closure of 2,363 stations and over 5,000 miles of track (~30% of the rail network), with the loss of 67,700 jobs.

Decades later, the country is spending billions rebuilding some of those same routes. As it turned out, many of those “unprofitable” routes were vital not only to the health of the wider rail network, but also to the communities in those regions in ways that Beeching’s team of bean counters simply didn’t have the imagination to value.

I’m telling you this because, right now, a lot of businesses are carrying out their own version of the Beeching cuts.

The Data-Led Trap

There’s a crucial distinction between being data-led and data-informed. Understanding this could be the difference between implementing a sound content production strategy and repeating Beeching’s catastrophe.

Data-led thinking treats the available data as the complete picture. It looks for a pattern and adopts it as an undeniable truth that points towards a clear course of action. “AI generates content for a fraction of our current costs. Therefore, we should replace the writers.”

Data-informed thinking sets out to understand what might be behind the pattern, extrapolate what’s missing from the picture, and stress-test the conclusions. The data becomes a starting point for inquiry, not an endpoint for decisions. “What value isn’t captured in this data? What would replacing our writers with AI actually mean for the effectiveness of our content when our competitors can do the exact the same thing with the exact same tools?”

That last question is the real challenge facing companies considering AI-generated content, but the answer won’t be found in a spreadsheet. If you can use AI to generate your content with minimal human input, so can everyone else. Very soon, everyone is generating similar content on similar topics to target the same audiences, with recycled information and reheated “insights” drawn from the same online sources.

Why would ChatGPT somehow generate a better blog post for you than for anyone else asking for 1,200 words on the same topic? It wouldn’t. You need to add your own secret sauce.

There is no competitive advantage to be gained by relying on AI-generated content alone. None.

AI-generated content is not a silver bullet. It’s the minimum benchmark your content needs to significantly exceed if your brand and your content is to have any chance of standing out in today’s noisy online marketplace.

Unfortunately, while organizations know they need to have content, far too many senior decision-makers don’t fully understand why, never mind all the things an effective content strategy needs to accomplish.

Content Isn’t A Cost, It’s An Infrastructure

Marketing content is often looked down upon as somehow easier or less worthy than other forms of writing. Yet it arguably has the hardest job of all. Every article, ebook, LinkedIn post, brochure, and landing page has to tick off a veritable to-do list of strategic requirements.

Of course, your content needs to have something to say. It must work on an informational level, backed by solid research and journalism. However, each asset or article also has a strategic role to play: attracting audiences, nurturing prospects, or converting customers, while aligning with the brand’s carefully mapped out messaging at every stage.

Your content must build authority, earn trust, and demonstrate expertise. It must be memorable enough to aid brand awareness and recall, and distinctive enough to differentiate the brand from its competitors. It must be structured for search engines with the right entities, topics, and relationships, without losing the attention of busy humans who can click away at any second. Ideally, it should also include a couple of quote-worthy lines or interesting stats capable of attracting attention when the content is distributed on social media.

ChatGPT or Claude can certainly string a bunch of convincing sentences together. But if you think they can spin all those other plates for you at the same time, and to the same standard as a skilled content creator, you’re going to be disappointed. No matter how detailed and nuanced your prompt, something will always be missing. You’re still asking AI to synthesize something brilliant by recycling what’s already out there.

Which brings me to the most ironic part of this discussion. With the rapid adoption of AI-mediated search, your content now needs to become a source that large language models will confidently cite in responses to relevant queries.

Expecting AI to create content likely to be cited by AI is like watching a dog chasing its tail: futile and frustrating. If AI provided the information and insights contained in your content, it already has better, more authoritative sources. Why would AI cite content that contains little if any fresh information or insight?

If your goal is to increase your brand’s visibility in AI responses, then your content needs to offer what can’t easily be found elsewhere.

The Limitations Of Online Knowledge

Despite appearances, AI cannot think. It cannot understand, in the sense we usually mean it. As it currently stands, it cannot reason. It certainly cannot imagine. Words like these have emerged as common euphemisms for how AI generates responses, but they also set the wrong expectations.

AI also cannot use information that isn’t already available and crawlable online. While we like to think that somehow the internet is a massive store of the entirety of human knowledge, the reality is that it’s not even close.

So much of the world we live in simply cannot be captured as structured, digitized information. While AI can tell you when and where the next local collectables market is on, it can’t tell you which dealer has that hard-to-find comic you’ve been chasing for years. That’s the kind of information you can only find out by digging through lots of comic boxes on the day.

And then there are cultural histories and localized experiences that exist more in verbal traditions than in history books. AI can tell me plenty of stuff about the First World War. But if I ask it about the Iranian famine during WW1, it’s going to struggle because it’s not that well documented outside of Iranian history books. Most of my knowledge of the famine comes almost entirely from stories my great grandma told my mother, who then passed them on to me, like how she had to survive on just one almond per day. But you won’t find her stories in any book.

How can AI draw upon the wealth of personal experience and memories we all have? The greatest source of knowledge is human. It’s us. It’s always us.

But while AI can’t do your thinking for you, it can still help in many other ways.

→ Read More: Can You Use AI To Write For YMYL Sites? (Read The Evidence Before You Do)

You Still Need A Brain Behind The Bot

Let me be clear: I use AI every day. My team uses AI every day. You should, too. The problem isn’t the tool. The problem is treating the tool as a strategy, and an efficiency or cost reduction strategy at that. Of course, it isn’t only marketing teams hoping to reduce costs and boost productivity with generative AI. Another industry has already discovered that AI doesn’t actually replace anything.

A recent survey conducted by the Australian Financial Review (AFR) found that most law firms reported using AI tools. However, far from reducing headcount, 70% of surveyed firms increased their hiring of lawyers to vet, review, and sign off on AI-generated outputs.

This isn’t a failure in their AI strategy, because the strategy was never about reducing headcount. They’re using AI tools as digital assistants (research, drafting, document handling, etc.) to free up more time and headspace for the kinds of strategic and insightful thinking that generates real business value.

Similarly, AI isn’t a like-for-like replacement for your writers, designers, and other content creators. It’s a force multiplier for them, helping your team reduce the drudgery that can so often get in the way of the real work.

  • Summarizing complex information.
  • Transcribing interviews.
  • Creating outlines.
  • Drafting related content like social media posts.
  • Checking your content against the brand style guide to catch inconsistencies.

Some writers might even use AI to generate a very rough first draft of an article to get past that blank page. The key is to treat that copy as a starting point, not the finished article.

All these tasks are massive time-savers for content creators, freeing up more of their mental bandwidth for the high-value work AI simply can’t do as well.

AI can only synthesize content from existing information. It cannot create new knowledge or come up with fresh ideas. It cannot interview subject matter experts within your business to draw out hidden wisdom and insights. It cannot draw upon personal experiences or perspectives to make your content truly yours.

AI is also riddled with algorithmic biases, potentially skewing your content and your messaging without you even realizing. For example, the majority of AI training data is in the English language, creating a huge linguistic and cultural bias. It might require an experienced and knowledgeable eye to spot the subtle hallucinations or distortions.

While AI can certainly accelerate execution, you still need skilled, experienced creatives to do the real thinking and crafting.

You Don’t Know What You Have, Until It’s Gone

Until Beeching closed the line in 1969, the route between Edinburgh and Carlisle was a vital transport artery for the Scottish Borders. On paper, the line was unprofitable, at least according to Beeching’s simplistic methodology. However, the closure had massive knock-on effects, reducing access to jobs, education, and social services, as well as impacting tourism. Meanwhile, forcing people onto buses or into cars placed greater strain on other transport infrastructures.

While Beeching might have solved one narrowly defined problem, he had undermined the broader purpose of British Railways: the mobility of people in all parts of Great Britain. In effect, Beeching had shifted the consequences and cost pressures elsewhere.

The route was partially reopened in 2015 as The Borders Railway, costing an estimated £300 million to reinstate just 30 miles of line with seven stations.

Beeching’s cuts illustrate the folly of evaluating infrastructure (or content strategy) purely on narrow, short-term financial metrics.

Organizations that cut their teams in favor of AI are likely to find it isn’t so easy to reverse course and undo the damage a few years from now. Replacing your writers with AI risks eroding the connective tissue that characterizes your content ecosystem and anchors long-term performance: authority, context, nuance, trust, and brand identity.

Experienced content creators aren’t going to wait around for organizations to realize their true value. If enough of them leave the industry, and with fewer opportunities available for the next generation of creators to gain the necessary skills and experience, the talent pool is likely to shrink massively.

As with the Beeching cuts, rebuilding your content team is likely to cost you far more in the long term than you saved in the short term, particularly when you factor in the months or years of low-performing content in the meantime.

Know What You’re Cutting Before You Wield The Axe

According to your spreadsheet, AI-generated content may well be cheaper to produce. But the effectiveness of your content strategy doesn’t hinge on whether you can publish more for less. This isn’t a case of any old content will do.

So, beware of falling into the Beeching trap. Your content workflows might only seem “loss-making” on paper because the metrics you’re looking at don’t adequately capture all the ways your content delivers strategic value to your business.

Content is not a cost center. It never was. Content is the infrastructure of your brand’s discoverability, which makes it more important than ever in the AI era.

This isn’t a debate about “human vs. AI content.” It’s about equipping skilled people with the tools to help them create work worthy of being found, cited, and trusted.

So, before you start swinging the axe, ask yourself: Are you cutting waste, or are you dismantling the very system that makes your brand visible and credible in the first place?

More Resources:

Featured Image: IM Imagery/Shutterstock

Ecommerce MGMT 0 Comments
Jan 5 2026
16 Content Writing Tips From Experts To Survive 2026 via @sejournal, @beacarlota17

AI has created insecurity for content writers, where their jobs could be in jeopardy from machine-generated content at scale.

The reality is that many content writers may be displaced, but the ones that can survive and be in demand are the ones who embrace the current changes in search and adapt.

Writers can be irreplaceable by learning how to create valuable content that is optimized for large language model (LLM) inclusion. They can also stand out by creating the kind of content that takes advantage of LLM and machine content limitations.

To offer real, actionable advice, we reached out to a selection of the leading voices in producing content to ask how they’re doing it and what “high performing” content will mean by 2026.

How Should Writers Adapt Their Skills To Stay Relevant And Stand Out In The AI Era?

Our contributors agree: Staying relevant means honing the skills AI can’t replace and learning how to make it work for you, not against you.

1. Sharpen Ability To Synthesize Data And Write Detailed Prompts

Chelsea Alves opens with a reminder that experimentation defines this era: “The AI era is all about testing.” She advises writers to “pair their creative intuition with thoughtful analysis” and focus on what AI can’t replicate: “genuine insight, storytelling, and subject-matter expertise.”

“Writers should sharpen their ability to synthesize data and human context, not just summarize information.”

Alves also suggests a modern workflow where AI becomes a partner: “Writers will want to view AI as a collaborator, rather than as a competitor,” especially for “research acceleration (with human fact-checking), ideation, and outline while retaining full authorship over tone and empathy.”

But it only works when the writer knows how to communicate with AI effectively. “Feeding AI detailed, structured, and contextual prompts is imperative for it to deliver accurate and relevant results.”

Her closing warning points to a changing discovery environment: “Multi-format fluency is crucial,” because audiences now discover and engage with content through summaries, voice search, or in-platform experiences.

2. Position Yourself As A Strategic Thinker

Andy Betts shares what he learned: “Position yourself as the strategic thinker who uses AI to research and aid your ideas, not replace them.”

“There is a real opportunity for marketers who lean into their core creative instincts and foundational optimization skills to amplify messaging, positioning, and branding. That is the gap AI cannot fill.”

“I shifted from tactical content to strategic content that builds authenticity for AI citations and references,” Betts adds, and then points to the signal coming from the biggest players in the market: “OpenAI, Meta, Google, PayPal are all hiring content strategists to shape CEO narratives and storytelling. If you are willing to own the strategy piece, that is where you genuinely stand out.”

3. Double Down On Nuance, Strategy, And Voice

Heather Lloyd-Martin takes the long view: “Writers who thrive in the AI era will double down on what machines can’t replicate: nuance, strategy, and authentic voice.” Even as AI accelerates research, she reminds us that fundamentals haven’t changed.

“Great writing still sells, teaches, and builds trust.”

She shares techniques, including “studying conversion writing to weave micro-conversions into every piece. Hone your storytelling to capture attention in an increasingly automated feed. Get comfortable with ‘messy prompting’ AI, and discover how to use it as a research assistant/creative wingman. And most importantly, infuse every line with brand personality and point of view.”

For her, differentiation in 2026 won’t come from writing speed or awesome prompts, but from “insight, strategy, amazing storytelling, and knowing how to use the power of AI to make your content even better.”

4. Keep Writing For Humans

“Writers should not adapt; they should continue to produce human-level content written for humans,” argues Adam Riemer.

“AI can mimic, but it cannot replace. Humans being able to verify and fact-check, create content that they see being used is something only we can do.”

He adds, “Humans know what to look for and how to write for it. The biggest difference in skills is when we get stuck on wording, have character maximums or minimums, or need alternative word suggestions. This is where AI can shine.

→ Read More: This Is Why AI Won’t Take Your Job (Yet)

How Do You Write And Structure Content For LLM Visibility?

When asked how their writing has shifted in the age of LLMs, each expert landed on the same truth: the mechanics of good writing haven’t disappeared, but the structure carrying that writing has become far more intentional.

5. Structure For LLMs And Readers With Equal Intention

With the days of writing content for traditional SEO long gone, Alves advises writers to structure content with machine readability in mind. This means ensuring “clarity, consistency, and rich context so that LLMs can accurately summarize your content.”

“Topical relevance is more important than ever, as well as schema-informed structure.”

Instead of letting meaning build linearly, she now aims for “writing sentences that stand alone with context.”

For her, fundamentals are still as important. “Today’s writers must incorporate internal links, definitional clarity, and clear takeaways to make content easier for humans and machines to process.”

But she notes that writing for machines doesn’t mean flattening voice or storytelling. “It’s more about creating a layered content experience: a clear, structured skeleton for the machines and a compelling, emotive experience for the humans,” explains Alves.

6. Write Less, But Make Every Word Work Harder

For Betts, his core writing has not changed much, but his approach to structure has. “I am spending more time on prompts and briefs that guide AI rather than just writing final copy,” he explains.

“The content optimization tricks I have used for 25 years still work.”

But he’s learned to write deliberately for how AI understands context, nuance, and brand intent.

His take on what matters: “I always own the final output. That is non-negotiable.” And the real win? “It is writing smarter – less content overall, but each piece carries more weight. Your influence-per-word ratio goes up when you are directing AI instead of racing it.”

7. SEO Writing Fundamentals Still Matter, But Experiment With Structure

“I wrote about this on LinkedIn,” Lloyd-Martin shares. “Most ‘LLM writing rules’ echo what I’ve been discussing for over 20 years. Write clear, eye-catching subheads, and link to related pages. Understand search intent, and write engaging, standout content that answers questions and showcases your expertise.”

Where she experiments is structure. “I’ll add quick takeaways at the top to help both readers and machines grasp the value fast, or FAQs when they make sense.” AI now plays a role in deep research, too, “identifying where competitors earn LLM citations and where we don’t.”

But even with those tools, her north star hasn’t budged.

“AI may have enhanced my process, but it hasn’t changed how I write.”

8. Write For Your Audience & Make Sure AI Can Find Your Content

“LLM inclusion is based on tons of potential signals,” Riemer says, keeping the conversation grounded in audience experience.

“Writing for a human audience at the audience’s needs and skill levels is what everyone should do. If you write higher or lower, overcomplicate or simplify, or add more because of a concept being touted as ‘fan out queries,’ then you’re creating a horrible user experience.”

He adds that “AI isn’t your customer … do not change your writing because of it; write for your audience and make sure AI can find, understand, and reference it.”

→ Read More: How To Get Your Content (& Brand) Recommended By AI & LLMs

What Content Still Works And Will Invest In No Matter How Search Or AI Changes?

The conversation around content performance has shifted from volume to value. When asked what holds up no matter how search or AI shifts, our experts reveal a comforting throughline: content built on depth, trust, and expertise isn’t going anywhere.

9. Authority-Driven Frameworks Still Win

Alves opens the conversation by pointing out that AI hasn’t completely derailed the content frameworks that used to work. “Many content best practices remain the same, such as authority-driven frameworks like data storytelling, original research, and expert commentary.”

These, she notes, are more likely to be shared, trusted, and noticed by humans and search engines alike. “Case studies, proprietary data reports, and first-hand experiments continue to outperform derivative content, both in organic search and boosting brand credibility.”

She also sees the staying power in educational content built on empathy. Examples are tutorials, explainer videos, and playbooks that anticipate user needs and provide helpful, actionable next steps.

Alves also reminds us that even as AI delivers answers in an instant, audiences will still turn to trusted brands for depth and application.

“Consumers still favor and prefer human expertise over machines.”

10. Human Element Keeps Strategy Relevant

“I am betting big on frameworks only experienced writers understand,” Betts says, pointing to brand storytelling rooted in company knowledge, executive messaging that shapes how people perceive your organization, and content strategy demanding real human judgment about stakeholder needs.

He also stresses that creative writing, the deeply human element, does not go away.

“Positioning writing as a strategic function, not a production task, drives business outcomes.”

He adds, “The briefs, editorial standards, and voice guidelines that train AI? Those matter now. That is where you multiply value.” And when it comes to measurement, he found that “measuring content by genuine business impact, not just visibility metrics, is where success truly lives.”

11. Refresh Old Content And Own Your Audience

Lloyd-Martin keeps her focus on the assets many brands overlook. “Refreshing older blogs, guides, and sales pages often delivers faster wins than starting from scratch, and you can repurpose the content for different platforms.” She’s seen clients gain quick lifts from updated headlines, improved internal links, and repromotion.

“Great content doesn’t expire; it just needs a little love every once in a while.”

And no matter how AI or search evolves, she always recommends maintaining an email list. For her, “owning your audience and showing up in their inbox will always be a smart, sustainable marketing move.”

12. The Same Rules Still Apply

Riemer takes the most streamlined approach: “Same as always, proper page structure and follow best practices.”

How Are You Proving Business Impact In Human-Written Content?

When the conversation turns to proving return on investment (ROI), our experts tie value to measurable outcomes rather than volume.

13. Provide Tangible Evidence Of Success

Alves leads with evidence: “As with any marketing endeavor, it’s crucial to provide tangible evidence of success.” For her, the indicators have shifted. “Traffic and clicks used to be a top indicator of success; however, engagement quality (scroll depth, time spent on page), assisted conversions, lead quality, and influence on customer journeys are now key indicators of content’s impact.”

She emphasizes attribution and visibility across the funnel. “It’s paramount to have visibility into how content is fueling conversions.”

She also looks at “trust metrics,” lending credence to brand credibility, share of voice, appearance in AI summaries, and mentions in third-party articles/media.

14. Prove The Business Value Of Content Leadership

Betts centers his measurement on strategic influence. “I have stopped counting words and started tracking what matters,” he explains. He focuses on the broader role content plays. “I focus on how my editorial direction shapes company communications, including AI-generated content.”

He points to what executive content drives: “recruitment quality, investor conversations, brand positioning that sticks.” He then highlights a shift in how stakeholders see content.

“It is no longer ‘content is overhead’. It is ‘content strategy multiplies organizational output while staying authentic.’ That wins investment.”

15. Tie Rankings To Conversions And Conversations

“Rankings and citations still matter, but I focus on what those metrics mean,” Lloyd-Martin shares, watching for these behavioral indicators: “Is the content attracting qualified visitors? Are they taking the next step, or bouncing? Traffic without reader resonance doesn’t drive revenue.”

She also pays close attention to how content shows up in conversations. “When sales teams mention prospects referencing our articles, that’s impact. For clients with limited rankings, especially local or service-based businesses, we analyze which pages are already positioning (often blogs) and expand visibility for queries that matter most.”

She notes that the goal is creating content that converts and builds authority. “AI can help you write faster, but it takes an experienced writer to understand if the output is any good and how to improve it.”

16. Let The Results (And The Legal Team) Speak For Themselves

Adam Riemer demonstrates impact through contrast and accountability. He explains, “Some clients want to see the before and after, so we share examples of sites that got tanked because of the AI content. In other cases, accuracy becomes the deciding factor. “We send the non-factual content to legal for review, and that puts an end to it.”

For his clients who want to use AI for content, he sets up policies in place for “fact-checking, conversion testing, and other controls that require human assistance.”

“This helps keep humans employed until companies realize AI should not be writing and generating content without human intervention.”

What Do You Predict Will Define High-Performing Content In 2026?

Our experts share what they believe will define content success next year. Hint: The loudest or the fastest won’t win. It’s value, depth, and authenticity that will set the bar.

Same Fundamentals, New Advantage

“Nothing new, it’s the same as it always has been,” says Riemer, describing the current landscape. He sees an opportunity created by declining content quality. “It’s just easier now to compete since a lot of companies are creating spammy AI and LLM-generated content.”

In his view, the core dynamic hasn’t changed. “Same as it was after article spinners lost their time in the spotlight for the same thing. You could take articles from 10 sites or have 10 writers write them, add the macros to insert and spin them, then spit out new content. Same quality as we’re seeing here with AI.”

Visibility Will Be Tied To Originality

Alves notes the saturation AI has created, but audiences and search engines will reward content that proves real-world insight can only be produced by humans. “This will look like data from proprietary sources, first-hand interviews, unique frameworks, or any content that demonstrates lived experience.”

But ultimately, she believes visibility will be tied to originality.

“Differentiation will be key and will hinge on human touch … Your content should sound unmistakably human in a sea of sameness.”

Clarity, Credibility, And Conversion Power Will Define Content Success

Lloyd-Martin believes “high-performing content will still be measured by the same timeless standard: business impact.” For her, the right questions remain constant: Does it attract the right audience, support the buyer journey, and drive conversions? Is that AI citation making you money, or is it just a vanity metric?

“Clarity, credibility, and conversion power will always define content success.”

Strategic Wisdom Is The New Content Currency

For Betts, “high-performing content will be strategically directed and authentically executed, AI- AI-assisted but human-guided.”

He foresees big companies course-correcting. “Expect those who cut experienced strategists and relied on AI writing to come back hiring content leaders.” For him, the differentiator is not speed but “strategic wisdom: knowing what to say and ensuring it reflects genuine human creativity and judgment.”

He also predicts: “The scarcest resource will not be content; it will be experienced judgment. Writers who master directing AI while maintaining creative integrity will command premium value.”

In Summary

AI may have changed how content gets discovered, but not what makes it valuable. The experts agree: The edge now lies in structure, strategy, substance, and human judgment.

LLMs surface what they can understand. Readers choose what they can trust. High-performing content in this era succeeds when it satisfies both.

A huge thank you to our contributors for sharing their time, experience, and insights.

Editor’s note: All interviews have been lightly edited for clarity, brevity, and adherence to our Editorial Guidelines. The views expressed by the interviewees in this column are theirs alone and do not necessarily represent the view of Search Engine Journal.

More Resources:

Featured Image: TierneyMJ/Shutterstock

Ecommerce MGMT 0 Comments
Nov 6 2025
Why AI Content All Sounds the Same & How SEO Pros Can Fix It via @sejournal, @mktbrew

This post was sponsored by Market Brew. The opinions expressed in this article are the sponsor’s own.

If your AI-generated articles don’t rank but sound fine, you’re not alone.

AI has made it effortless to produce content, but not to stand out in SERPs.

Across nearly every industry, brands are using generative AI tools like ChatGPT, Perplexity, Claude, and more to scale content production, only to discover that, to search engines, everything sounds the same.

But this guide will help you build E-E-A-T-friendly & AI-Overview-worthy content that boosts your AI Overview visibility, while giving you more control over your rankings.

Why Does All AI-Generated Content Sound The Same?

Most generative AI models write from the same training data, producing statistically “average” answers to predictable prompts.

The result is fluent, on-topic copy that is seen as interchangeable from one brand to the next.

To most readers, it may feel novel.

To search engines, your AI content may look redundant.

Algorithms can now detect when pages express the same ideas with minor wording differences. Those pages compete for the same meaning, and only one tends to win.

The challenge for SEOs isn’t writing faster, it’s writing differently.

That starts with understanding why search engines can tell the difference even when humans can’t.

How Do Search Engines & Answer Engines See My Content?

Here’s what Google actually sees when it looks at your page:

  • Search engines no longer evaluate content by surface keywords.
  • They map meaning.

Modern ranking systems translate your content into embeddings.

When two pages share nearly identical embeddings, the algorithm treats them as duplicates of meaning, similar to duplicate content.

That’s why AI-generated content blends together. The vocabulary may change, but the structure and message remain the same.

What Do Answer Engines Look For On Web Pages?

Beyond words, engines analyze the entire ecosystem of a page:

These structural cues help determine whether content is contextually distinct or just another derivative variant.

To stand out, SEOs have to shape the context that guides the model before it writes.

That’s where the Inspiration Stage comes in.

How To Teach AI To Write Like Your Brand, Not The Internet

Before you generate another article, feed the AI your brand’s DNA.

Language models can complete sentences, but can’t represent your brand, structure, or positioning unless you teach them.

Advanced teams solve this through context engineering, defining who the AI is writing for and how that content should behave in search.

The Inspiration Stage should combine three elements that together create brand-unique outputs.

Step 1 – Create A Brand Bible: Define Who You Are

The first step is identity.

A Brand Bible translates your company’s tone, values, and vocabulary into structured guidance the AI can reference. It tells the model how to express authority, empathy, or playfulness. And just as important, what NOT to say.

Without it, every post sounds like a tech press release.

With it, you get language that feels recognizably yours, even when produced at scale.

“The Brand Bible isn’t decoration: it’s a defensive wall against generic AI sameness.”

A great example: Market Brew’s Brand Bible Wizard

Step 2 – Create A Template URL: Structure How You Write

Great writing still needs great scaffolding.

By supplying a Template URL, a page whose structure already performs well, you give the model a layout to emulate: heading hierarchy, schema markup, internal link positions, and content rhythm.

Adding a Template Influence parameter can help the AI decide how closely to follow that structure. Lower settings would encourage creative variation; higher settings would preserve proven formatting for consistency across hundreds of pages.

Templates essentially become repeatable frameworks for ranking success.

An example of how to apply a template URL

Step 3 – Reverse-Engineer Your Competitor Fan-Out Prompts: Know the Landscape

Context also means competition. When you are creating AI content, it needs to be optimized for a series of keywords and prompts.

Fan-out prompts are a concept that maps the broader semantic territory around a keyword or topic. These are a network of related questions, entities, and themes that appear across the SERP.

In addition, fan-out prompts should be reverse-engineered from top competitors in that SERP.

Feeding this intelligence into the AI ensures your content strategically expands its coverage; something that the LLM search engines are hungry for.

“It’s not copying competitors, it’s reverse-engineering the structure of authority.”

Together, these three inputs create a contextual blueprint that transforms AI from a text generator into a brand and industry-aware author.

Market Brew’s implementation of reverse engineering fan-out prompts

How To Incorporate Human-Touch Into AI Content

If your AI tool spits out finished drafts with no checkpoints, you’ve lost control of what high-quality content is.

That’s a problem for teams who need to verify accuracy, tone, or compliance.

Breaking generation into transparent stages solves this.

Incorporate checkpoints where humans can review, edit, or re-queue the content at each stage:

  • Research.
  • Outline.
  • Draft.
  • Refinement.

Metrics for readability, link balance, and brand tone become visible in real time.

This “human-in-the-loop” design keeps creative control where it belongs.

Instead of replacing editors, AI becomes their analytical assistant: showing how each change affects the structure beneath the words.

“The best AI systems don’t replace editors, they give them x-ray vision into every step of the process.”

How To Build Content The Way Search Engines Read It

Modern SEO focuses on predictive quality signals: indicators that content is likely to perform before it ever ranks.

These include:

  • Semantic alignment: how closely the page’s embeddings match target intent clusters.
  • Structural integrity: whether headings, schema, and links follow proven ranking frameworks.
  • Brand consistency and clarity: tone and terminology that match the brand bible without losing readability.

Tracking these signals during creation turns optimization into a real-time discipline.

Teams can refine strategy based on measurable structure, not just traffic graphs weeks later.

That’s the essence of predictive SEO: understanding success before the SERP reflects it.

The Easy Way To Create High-Visibility Content For Modern SERPs

Top SEO teams are already using the Content Booster approach.

Market Brew’s Content Booster is one such example.

It embeds AI writing directly within a search engine simulation, using the same mechanics that evaluate pages to guide creation.

Writers begin by loading their Brand Bible, selecting a Template URL, and enabling reverse-engineered fan-out prompts.

Next, the internal and external linking strategy is defined, which uses a search engine model’s link scoring system, plus its entity-based text classifier as a guide to place the most valuable links possible.

This is bolstered by a “friends/foes” section that allows writers to define quoting / linking opportunities to friendly sites, and “foe” sites where external linking should be avoided.

The Content Booster then produces and evaluates a 7-stage content pipeline, each driven by thousands of AI agents.

Stage Function What You Get
0. Brand Bible Upload your brand assets and site; Market Brew learns your tone, voice, and banned terms. Every piece written in your unique brand style.
1. Opportunity & Strategy Define your target keyword or prompt, tone, audience, and linking strategy. A strategic blueprint tied to real search intent.
2. Brief & Structure Creates an SEO-optimized outline using semantic clusters and entity graphs. Perfectly structured brief ready for generation.
3. Draft Generation AI produces content constrained by embeddings and brand parameters. A first draft aligned with ranking behavior, not just text patterns.
4. Optimization & Alignment Uses cosine similarity and Market Brew’s ranking model to score each section. Data-driven tuning for maximum topical alignment.
5. Internal Linking & Entity Enrichment Adds schema markup, entity tags, and smart internal links. Optimized crawl flow and contextual authority.
6. Quality & Compliance Checks grammar, plagiarism, accessibility, and brand voice. Ready-to-publish content that meets editorial and SEO standards.

Editors can inspect or refine content at any stage, ensuring human direction without losing automation.

Instead of waiting months to measure results, teams see predictive metrics: like fan-out coverage, audience/persona compliance, semantic similarity, link distribution, embedding clusters and more. The moment a draft is generated.

This isn’t about outsourcing creativity.

It’s about giving SEO professionals the same visibility and control that search engineers already have.

Your Next Steps

If you teach your AI to think like your best strategist, sameness stops being a problem.

Every brand now has access to the same linguistic engine; the only differentiator is context.

The future of SEO belongs to those who blend human creativity with algorithmic understanding, who teach their models to think like search engines while sounding unmistakably human.

By anchoring AI in brand, structure, and competition, and by measuring predictive quality instead of reactive outcomes, SEOs can finally close the gap between what we publish and what algorithms reward.

“The era of AI sameness is already here. The brands that thrive will be the ones that teach their AI to sound human and think like a search engine.”

Ready to see how predictive SEO works in action?

Explore the free trial of Market Brew’s Light Brew system — where you can model how search engines interpret your content and test AI writing workflows before publishing.

Get Started Here

Image Credits

Featured Image: Image by Market Brew. Used with permission.

Ecommerce MGMT 0 Comments
Nov 3 2025
Can You Use AI To Write For YMYL Sites? (Read The Evidence Before You Do) via @sejournal, @MattGSouthern

Your Money or Your Life (YMYL) covers topics that affect people’s health, financial stability, safety, or general welfare, and rightly so Google applies measurably stricter algorithmic standards to these topics.

AI writing tools might promise to scale content production, but as writing for YMYL requires more consideration and author credibility than other content, can an LLM write content that is acceptable for this niche?

The bottom line is that AI systems fail at YMYL content, offering bland sameness where unique expertise and authority matter the most. AI produces unsupported medical claims 50% of the time, and hallucinates court holdings 75% of the time.

This article examines how Google enforces YMYL standards, shows evidence where AI fails, and why publishers relying on genuine expertise are positioning themselves for long-term success.

Google Treats YMYL Content With Algorithmic Scrutiny

Google’s Search Quality Rater Guidelines state that “for pages about clear YMYL topics, we have very high Page Quality rating standards” and these pages “require the most scrutiny.” The guidelines define YMYL as topics that “could significantly impact the health, financial stability, or safety of people.”

The algorithmic weight difference is documented. Google’s guidance states that for YMYL queries, the search engine gives “more weight in our ranking systems to factors like our understanding of the authoritativeness, expertise, or trustworthiness of the pages.”

The March 2024 core update demonstrated this differential treatment. Google announced expectations for a 40% reduction in low-quality content. YMYL websites in finance and healthcare were among the hardest hit.

The Quality Rater Guidelines create a two-tier system. Regular content can achieve “medium quality” with everyday expertise. YMYL content requires “extremely high” E-E-A-T levels. Content with inadequate E-E-A-T receives the “Lowest” designation, Google’s most severe quality judgment.

Given these heightened standards, AI-generated content faces a challenge in meeting them.

It might be an industry joke that the early hallucinations from ChatGPT advised people to eat stones, but it does highlight a very serious issue. Users depend on the quality of the results they read online, and not everyone is capable of deciphering fact from fiction.

AI Error Rates Make It Unsuitable For YMYL Topics

A Stanford HAI study from February 2024 tested GPT-4 with Retrieval-Augmented Generation (RAG).

Results: 30% of individual statements were unsupported. Nearly 50% of responses contained at least one unsupported statement. Google’s Gemini Pro achieved 10% fully supported responses.

These aren’t minor discrepancies. GPT-4 RAG gave treatment instructions for the wrong type of medical equipment. That kind of error could harm patients during emergencies.

Money.com tested ChatGPT Search on 100 financial questions in November 2024. Only 65% correct, 29% incomplete or misleading, and 6% wrong.

The system sourced answers from less-reliable personal blogs, failed to mention rule changes, and didn’t discourage “timing the market.”

Stanford’s RegLab study testing over 200,000 legal queries found hallucination rates ranging from 69% to 88% for state-of-the-art models.

Models hallucinate at least 75% of the time on court holdings. The AI Hallucination Cases Database tracks 439 legal decisions where AI produced hallucinated content in court filings.

Men’s Journal published its first AI-generated health article in February 2023. Dr. Bradley Anawalt of University of Washington Medical Center identified 18 specific errors.

He described “persistent factual mistakes and mischaracterizations of medical science,” including equating different medical terms, claiming unsupported links between diet and symptoms, and providing unfounded health warnings.

The article was “flagrantly wrong about basic medical topics” while having “enough proximity to scientific evidence to have the ring of truth.” That combination is dangerous. People can’t spot the errors because they sound plausible.

But even when AI gets the facts right, it fails in a different way.

Google Prioritizes What AI Can’t Provide

In December 2022, Google added “Experience” as the first pillar of its evaluation framework, expanding E-A-T to E-E-A-T.

Google’s guidance now asks whether content “clearly demonstrate first-hand expertise and a depth of knowledge (for example, expertise that comes from having used a product or service, or visiting a place).”

This question directly targets AI’s limitations. AI can produce technically accurate content that reads like a medical textbook or legal reference. What it can’t produce is practitioner insight. The kind that comes from treating patients daily or representing defendants in court.

The difference shows in the content. AI might be able to give you a definition of temporomandibular joint disorder (TMJ). A specialist who treats TMJ patients can demonstrate expertise by answering real questions people ask.

What does recovery look like? What mistakes do patients commonly make? When should you see a specialist versus your general dentist? That’s the “Experience” in E-E-A-T, a demonstrated understanding of real-world scenarios and patient needs.

Google’s content quality questions explicitly reward this. The company encourages you to ask “Does the content provide original information, reporting, research, or analysis?” and “Does the content provide insightful analysis or interesting information that is beyond the obvious?”

The search company warns against “mainly summarizing what others have to say without adding much value.” That’s precisely how large language models function.

This lack of originality creates another problem. When everyone uses the same tools, content becomes indistinguishable.

AI’s Design Guarantees Content Homogenization

UCLA research documents what researchers term a “death spiral of homogenization.” AI systems default toward population-scale mean preferences because LLMs predict the most statistically probable next word.

Oxford and Cambridge researchers demonstrated this in nature. When they trained an AI model on different dog breeds, the system increasingly produced only common breeds, eventually resulting in “Model Collapse.”

A Science Advances study found that “generative AI enhances individual creativity but reduces the collective diversity of novel content.” Writers are individually better off, but collectively produce a narrower scope of content.

For YMYL topics where differentiation and unique expertise provide competitive advantage, this convergence is damaging. If three financial advisors use ChatGPT to generate investment guidance on the same topic, their content will be remarkably similar. That offers no reason for Google or users to prefer one over another.

Google’s March 2024 update focused on “scaled content abuse” and “generic/undifferentiated content” that repeats widely available information without new insights.

So, how does Google determine whether content truly comes from the expert whose name appears on it?

How Google Verifies Author Expertise

Google doesn’t just look at content in isolation. The search engine builds connections in its knowledge graph to verify that authors have the expertise they claim.

For established experts, this verification is robust. Medical professionals with publications on Google Scholar, attorneys with bar registrations, financial advisors with FINRA records all have verifiable digital footprints. Google can connect an author’s name to their credentials, publications, speaking engagements, and professional affiliations.

This creates patterns Google can recognize. Your writing style, terminology choices, sentence structure, and topic focus form a signature. When content published under your name deviates from that pattern, it raises questions about authenticity.

Building genuine authority requires consistency, so it helps to reference past work and demonstrate ongoing engagement with your field. Link author bylines to detailed bio pages. Include credentials, jurisdictions, areas of specialization, and links to verifiable professional profiles (state medical boards, bar associations, academic institutions).

Most importantly, have experts write or thoroughly review content published under their names. Not just fact-checking, but ensuring the voice, perspective, and insights reflect their expertise.

The reason these verification systems matter goes beyond rankings.

The Real-World Stakes Of YMYL Misinformation

A 2019 University of Baltimore study calculated that misinformation costs the global economy $78 billion annually. Deepfake financial fraud affected 50% of businesses in 2024, with an average loss of $450,000 per incident.

The stakes differ from other content types. Non-YMYL errors cause user inconvenience. YMYL errors cause injury, financial mistakes, and erosion of institutional trust.

U.S. federal law prescribes up to 5 years in prison for spreading false information that causes harm, 20 years if someone suffers severe bodily injury, and life imprisonment if someone dies as a result. Between 2011 and 2022, 78 countries passed misinformation laws.

Validation matters more for YMYL because consequences cascade and compound.

Medical decisions delayed by misinformation can worsen conditions beyond recovery. Poor investment choices create lasting economic hardship. Wrong legal advice can result in loss of rights. These outcomes are irreversible.

Understanding these stakes helps explain what readers are looking for when they search YMYL topics.

What Readers Want From YMYL Content

People don’t open YMYL content to read textbook definitions they could find on Wikipedia. They want to connect with practitioners who understand their situation.

They want to know what questions other patients ask. What typically works. What to expect during treatment. What red flags to watch for. These insights come from years of practice, not from training data.

Readers can tell when content comes from genuine experience versus when it’s been assembled from other articles. When a doctor says “the most common mistake I see patients make is…” that carries weight AI-generated advice can’t match.

The authenticity matters for trust. In YMYL topics where people make decisions affecting their health, finances, or legal standing, they need confidence that guidance comes from someone who has navigated these situations before.

This understanding of what readers want should inform your strategy.

The Strategic Choice

Organizations producing YMYL content face a decision. Invest in genuine expertise and unique perspectives, or risk algorithmic penalties and reputational damage.

The addition of “Experience” to E-A-T in 2022 targeted AI’s inability to have first-hand experience. The Helpful Content Update penalized “summarizing what others have to say without adding much value,” an exact description of LLM functionality.

When Google enforces stricter YMYL standards and AI error rates are 18-88%, the risks outweigh the benefits.

Experts don’t need AI to write their content. They need help organizing their knowledge, structuring their insights, and making their expertise accessible. That’s a different role than generating content itself.

Looking Ahead

The value in YMYL content comes from knowledge that can’t be scraped from existing sources.

It comes from the surgeon who knows what questions patients ask before every procedure. The financial advisor who has guided clients through recessions. The attorney who has seen which arguments work in front of which judges.

The publishers who treat YMYL content as a volume game, whether through AI or human content farms, are facing a difficult path. The ones who treat it as a credibility signal have a sustainable model.

You can use AI as a tool in your process. You can’t use it as a replacement for human expertise.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Oct 22 2025
How And Why Google Rewrites Your Hard-Earned Headlines

TL;DR

  1. Google can and does rewrite headlines and titles frequently. Almost anything on your page could be used.
  2. The title is not all that matters. The entirety of your page – from the title to the on-page content – should remove ambiguity.
  3. The title tag is the most important term. Stick to 12 words and 600 pixels to avoid truncation and maximize value from each word.
  4. Google uses three rough concepts – Semantic title and content alignment, satisfactory click behavior, and searcher intent alignment – for this.
Image Credit: Harry Clarkson-Bennett

This is based on the Google leak documentation and Shaun Anderson’s excellent article on title tag rewriting. I’ve jazzed it to make it more news and publisher-specific.

“On average, five times as many people read the headline as read the body copy.”
David Ogilvy

No idea if that’s true or not.

I’m sure it’s some old-age advertising BS. But alongside the featured image, it is our shop window. Headlines are the gatekeepers. They need to be clickable, work for humans and machines, and prioritize clarity.

So, when you’ve spent a long time crafting a headline for your own story, why-oh-why does Google mess you around?

I’m sure you get a ton of questions from other people in the SEO team and the wider newsroom (or the legal team) about this.

Something like:

Why is our on-page headline being pulled into the SERP?

Or

We can just have the same on-page headline and title tag, can’t we? Why does it matter?

You could rinse and repeat this conversation and theory for almost anything. Meta descriptions are the most obvious situation, where some research shows they’re rewritten 70% of the time. The answer will, unfortunately, always be that, because Google can and does do what it wants.

But it helps to know the what and the why when having these conversations.

Mark Williams-Cook and team did some research to show that up to 80% of meta descriptions were being rewritten and the rewriting increased traffic. Maybe the machine knows best after all.

Why Does Google Rewrite Title Tags?

The search giant uses document understanding, query matching, content rewriting, and user engagement data to determine when a title or H1 should be changed in SERPs.

It rewrites them because it knows what is best satisfying users in real time. An area of search where we as publishers are at the bleeding edge. When you have access to that much data and you take a share of ad revenue, it would be a little obtuse not to optimize for clicks in real-time.

Image Credit: Harry Clarkson-Bennett

Does Length Matter?

No innuendos, please; this is a professional newsletter.

Google’s official documentation doesn’t define a limit for title tags. I think it’s just based on the title becoming truncated. Given Google now rewrites so much, longer, more keyword-rich and descriptive titles, longer titles could help with ranking in Top Stories and traditional search results.

According to Gary Illyes, there is real value in having longer title tags:

“The title tag (length), is an externally made-up metric. Technically there’s a limit, but it’s not a small number…

Try to keep it precise to the page, but I wouldn’t think about whether it’s long enough…”

Sara Taher ran some interesting analysis (albeit on evergreen content only) that showed the average title length falls between 42-46 characters. If titles are too long, Google will probably cut them off or rewrite them. Precision matters for evergreen search.

What Are The Key Determinants?

Based on the Google leak and Shaun’s analysis, I’d say there are three concepts at play Google uses to determine whether a title should be rewritten. I have made this up, by the way, so feel free to use your own.

  • Semantic title and content alignment.
  • Satisfactory click behavior.
  • Searcher intent alignment.

Semantic Title And Content Alignment

This is undoubtedly the most prominent section. Your on-page content and title/headline have to align.

This is why clickbait content and content written directly for Google Discover is so risky. Because you’re writing a cheque that you can’t cash. Create content specifically for a platform like Discover, and you will erode your quality signals over time.

Image Credit: Harry Clarkson-Bennett

The titlematchScore, h1ContentScore, and spammyTitleDetection review the base quality of a headline based on the page’s content and query intent. Mismatched titles, headlines, and keyword-stuffed versions are, at best, rewritten.

At worst, they downgrade the quality of your site algorithmically.

The titleMatchAnchorText ensures our title tags and header(s) are compared to internal and external anchors and evaluated in comparison to the hierarchy of the page (the headingHierarchyScore).

Finally, the “best” title is chosen from on-page elements via the snippetTitleExtraction. While Google primarily uses the title or H1 tag, any visible element can be used if it “best represents the page.”

Satisfactory Click Behavior

Much more straightforward. Exactly how Google uses user engagement signals (think of Navboost’s good vs bad click signals) to best cultivate a SERP for a particular term and cohort of people.

Image Credit: Harry Clarkson-Bennett

The titleClickSatisfaction metric combines click data at a query level with on-page engagement data (think scroll depth, time on page, on-page interactions, pogo-sticking).

So, ranking adjustments are made if Google believes the title used in the SERP is underperforming against your prior performance and the competition. So, the title you see could be one of many tests happening simultaneously, I suspect.

For those unfamiliar with Navboost, it is one of Google’s primary ranking engines. It’s based on user interaction signals, like clicks, hovers, scrolls, and swipes, over 13 months to refine rankings.

For news publishers, Glue helps rank content in real time for fresh, real-time events. Source and page level authority. It’s a fundamental part of how news SEO really works.

Searcher Intent Alignment

Searcher intent really matters when it comes to page titles. And Google knows this far better than we do. So, if the content on your page (headings, paragraphs, images, et al.) and search intent isn’t reflected by your page title, it’s gone.

Image Credit: Harry Clarkson-Bennett

Once a page title has been identified as not fit for purpose, the pageTitleRewriter metric is designed to rewrite “unhelpful or misleading page titles.”

And page titles are rewritten at a query level. The queryIntentTitleAlignment measures how the page title aligns with searcher intent. Once this is established, the page alignment and query intent are reviewed to ensure the title best reflects the page at a query level.

Then the queryDependentTitleSelection adjusts the title based on the specifics of the search and searcher. Primarily at the query and location-level. The best contextual match is picked.

Suggestions For Publishers

I’ll try to do this (in a vague order of precedence):

  1. Make your title stand out. Be clickable. Front-load entities. Use power words, numbers, or punctuation where applicable.
  2. Stick to 12 words and 600 pixels to avoid truncation and maximize value from each word.
  3. Your title tag better represent the content on your page effectively for people and machines.
  4. Avoid keyword stuffing. Entities in headlines = good. Search revolves around entities. People, places, and organizations are the bedrock of search and news in particular. Just don’t overdo it.
  5. Do not lean too heavily into clickbait headlines. There’s a temptation to do more for Discover at the minute. The headlines on that platform tend to sail a little too close to the clickbait wind.
  6. Make sure your title best reflects the user intent and keep things simple. The benefit of search is that people are directly looking for an answer. Titles don’t always have to be wildly clicky, especially with evergreen content. Simple, direct language helps pass titleLanguageClarity checks and reduces truncation
  7. Utilize secondary (H2s) and tertiary (H3s) headings on your page. This has multiple benefits. A well broken-up page encourages quality user engagement. It increases the chances of your article ranking for longer-tail queries. And, it helps provide the relevant context to your page for Google.
  8. Monitor CTR and run headline testing on-site. If you have the capacity to run headline testing in real-time, fantastic. If not, I suggest taking headline and CTR data at scale and building a model that helps you understand what makes a headline clickable at a subfolder or topic level. Do emotional, first-person headlines with a front-loaded entity perform best in /politics, for example?
  9. Control your internal anchor text. Particularly important for evergreen content. But even with news, there are five headlines to pay attention to. And internal links (and their anchors) are a pivotal one. The matching anchor text reinforces trust in the topic.

If you are looking into developing your Discover profile, I would recommend testing the OG title if you want to test “clickier” headlines that aren’t visible on page.

Final Thoughts

So, the goal isn’t just to have a well-crafted headline. The goal is to have a brilliant set of titles – clickable, entity and keyword rich, highly relevant. As Shaun says, it’s to create a constellation of signals – the , the </p> <h1>, the URL, the intro paragraph – that remove all ambiguity.</h1> <p>

As ever, clicks are an immensely powerful signal. Google has more data points than I’ve had hot dinners, so had a pretty good ideas what will do well. But real clicks can override this. The goldmineNavboostFactor is proof that click behavior influences which title is displayed.

The title tag is the most important headline on the page when it comes to search. More so than the

. But they have to work together. To draw people in and engage them instantly.

But it all matters. Removing ambiguity is always a good thing. Particularly in a world of AI slop.

More Resources: 

This post was originally published on Leadership In SEO.

Featured Image: Billion Photos/Shutterstock

Ecommerce MGMT 0 Comments
Sep 24 2025
And The Truth? This Writing Style Screams AI via @sejournal, @cshel

Six months ago, you could spot AI-generated text by its polished grammar, rigid essay structure, suspicious fondness for em dashes – and, of course, the inevitable emoji bullets (🔥🚀✨). The real giveaway, at least to my eye and ear, isn’t the emojis or the punctuation. It’s the cadence.

AI writing has a rhythm problem. The sentences are clipped. Overly dramatic. Split into one-line paragraphs that feel more like infomercials than journalism.

“The truth? This wasn’t SEO causation. It was a stock market correction.”
“They were left behind. They were angry. They weren’t your people.”

On the page, this is nails-on-chalkboard grating. It doesn’t read as conversational. It reads as performative. In my opinion, this is, without a doubt, AI’s most recognizable stylistic fingerprint.

A Brief History Of The AI Cadence

This rhythm predates AI. It has been the language of speechwriters, preachers, and copywriters long before GPT entered the chat. Think Reagan’s addresses, Clinton’s campaign rallies, Obama’s campaign speeches, Churchill’s wartime broadcasts, and Blair’s conference speeches. Each leaned on rhythm and repetition to generate a great deal of emotion out of a speck of substance. Pair that with Captain Kirk’s famously staccato delivery, televangelists’ sermons, or TED Talks built around dramatic pauses, and you see how cadence can make small or mundane ideas feel powerful and deep.

That style used to stay in its lane. Where print valued density and clarity, speech valued brevity and rhythm. Readers could re-read; listeners could not. Editors enforced writing standards and styles and the economics of print rewarded information density over theatrics. As a result, this cadence lived solely in spoken word. It lived in speeches and sales copy, and not in essays and articles.

AI collapsed those boundaries. Because LLMs cannot (or chose to not) differentiate between a stump speech, a YouTube transcript, and a white paper, they overindex patterns designed to persuade aloud and repurpose them for the written page. Now, we are inundated with technical articles that read like motivational talks.

Why AIs Default To This Cadence

The AI cadence is not an accident – it’s a reflection of what models were most heavily trained on. Large language models have been fed a disproportionate amount of spoken-word material: transcripts of speeches, news reports, debates, interviews, webinars, podcasts, and video scripts. These aren’t “written texts” in the traditional sense; they are spoken performances converted into text.

Why so much spoken-word data? Because it’s cheap and plentiful. Back when I was running my ISP, I loved radio and TV for advertising and news mentions because it was far less expensive than buying or winning space in print. Broadcasters had 24 hours a day to fill, and local stations were always desperate for content. Print, on the other hand, is expensive. Every page of a newspaper, magazine, or book costs money to produce, and publishers limit content to what is necessary or affordable. As a result, far more hours of audio and video have been produced than carefully edited prose — and much of that material ends up transcribed. Those transcripts give the models a vast mountain of “written-down speech” compared to a relatively smaller body of curated, edited text.

The difference is subtle but important: a transcript is in a written medium, but it is not writing in a written style. It preserves the cadence of spoken delivery — short bursts, rhetorical pauses, fragments. Models overindex this rhythm because it dominates the dataset.

Even when prompted to avoid it, the models can’t resist drifting back into this rhythm. They might manage a few sentences of varied prose, but the gravitational pull of the AI cadence always drags them back. It’s now the default groove burned into their training.

The Em Dash Problem

That overindexing also explains a related AI tell: the sudden overuse of em dashes. In polished writing, dashes were historically used sparingly for emphasis or interruption. In speech, however, pauses are constant. Transcripts often mark those pauses with dashes. For a model swimming in transcripts, the dash becomes a default punctuation mark, because it functions as the written equivalent of a spoken pause. The result is copy littered with dashes – not because the ideas require them, but because the training data normalized them.

Punctuation As Breath

Punctuation has always been about more than grammar. Periods, commas, and dashes are signals for how we pause and where we breathe. They are like rests in music, telling the reader when to stop, inhale, and reset before continuing. Well-edited prose balances those pauses so the rhythm feels natural.

The AI cadence breaks this balance. When every thought is chopped into fragments, you’re effectively told to breathe after every line. Reading an article like this feels like hyperventilating: shallow breaths, constant interruptions, no sustained flow. It makes everything sound catastrophic, urgent, or world-shattering, even when the subject matter is mundane. Gentle readers, not every sentence or every idea warrants that level of drama.

Where this leaves us is that when models generate text, they parrot back the structures they’ve seen most often: speech rhythms and speech punctuation, presented as though they were the standard for written communication. They are not. They’re salesmanship with line breaks and pauses dressed up as prose.

Why Readers React To It

This cadence feels powerful at first. It mimics natural speech. It creates rhythm. It feels dramatic without requiring depth. That’s why it pops in feeds.

However, the longer it is stretched out, like in long-form content, or the more a reader is exposed to the same cadence over and over and over again, the power you once felt collapses into disdain. This breathy, short-sentence delivery leads to:

  • Oversimplification which flattens nuance.
  • Repetition that manipulates more than it informs.
  • Every line to demand attention ensuring none of them earn it.
  • Readers to suspect style is substituting for substance.

Here is the deeper problem: when everything is delivered as if it were earth-shattering, readers begin to doubt the authenticity of the message itself. It’s Syndrome’s hypothesis in The Incredibles: “When everyone is super, no one is.” If every sentence screams urgency, then nothing actually carries weight.

Historically, this kind of relentless, crisis-driven cadence has also been a manipulation tactic. Political demagogues, televangelists, and snake-oil salesmen leaned on hyperbole precisely because they lacked evidence. When AI reproduces that same rhythm on the page, it inherits the credibility problem too. Readers may not articulate it consciously, but they feel it: if you have to shout every line, maybe you don’t have enough substance to stand on quietly.

Just as keyword stuffing once became a hallmark of low-quality SEO, this cadence is already becoming the hallmark of low-quality AI. Readers recognize the rhythm before they absorb the message. When the medium distracts from the message, trust erodes.

A Tale Of Two Paragraphs

AI cadence in practice:

“The algorithm changed.
Sites lost traffic.
Panic spread.
And the industry?
It declared SEO dead – again.”

Now, the same idea written for readers:

“When the algorithm changed, many sites saw a drop in traffic. The panic was predictable. Within days, familiar headlines declared SEO dead once again. The cycle repeats every few years, and every few years it proves wrong.”

The difference here is obvious: one is an infomercial and the other is writing.

How To Spot It

Editors and readers can train themselves to notice:

  • Long runs of one-sentence paragraphs.
  • Rhetorical questions with no depth (often beginning with conjunctions like And or But…
  • Sentence fragments pretending to be profound.
  • Sermon-like pacing that seems to expect a chorus of ‘amens’ (or applause, if you’re lucky)…

Simply put, once you have seen it, you cannot unsee it: it is the literary equivalent of a laugh track.

How To Write Like A Human Again

How do we remedy this situation? Short of, I suppose, doing our own writing?

  • Vary sentence length instead of defaulting to extremes.
  • Use rhetorical questions sparingly – only when they genuinely add depth.
  • Group related ideas into paragraphs; readers can handle more than one sentence at a time. Unless you are writing FOR toddlers, do not treat your readers as though they ARE toddlers.
  • Prioritize clarity and voice over performative drama. Note here that the goal isn’t to sound casual at all costs, but to sound intentional, rational, and backed by data.

Why It Matters For SEOs And Marketers

AI writing tools are embedded in nearly every workflow. Left unchecked, they will flood the web with copy that reads like an endless sales pitch. Professionals must edit not just for facts but for voice.

That means:

  • Training teams to recognize and break the AI cadence.
  • Creating style guides that emphasize varied sentence and paragraph structure.
  • Editing AI drafts with rhythm in mind, not just keywords.
  • Writing for humans who read – not just platforms that skim.

Respecting the reader’s time and intelligence is, in the end, the real optimization.

Is There Ever A Place For This Style?

Yes, of course, but like most things, in moderation. Staccato writing is effective for:

  • Ad copy where space is limited.
  • Video scripts where pacing drives attention. (Your LinkedIn vertical videos and IG Reels? Have at it. This is where the staccato AI cadence shines.)
  • The occasional LinkedIn post engineered for scanning.

However, should this become the default writing style for articles, blogs, or essays? Abso-effing-lutely not. It cheapens the content and undermines credibility.

In Closing

AI has introduced more than just new tools. It has also normalized certain stylistic tics that don’t belong in most forms of writing. Among these, the AI cadence problem is the most recognizable and the most damaging when left unchecked.

Writers, editors, and marketers need to treat the presence of AI cadence in their writings the same way we treated keyword stuffing a decade ago: as a major red flag. The difference between human and AI writing isn’t just factual accuracy. It’s rhythm, intent, and voice.

The real divide isn’t human versus machine. It’s generic versus intentional. Intentional writing that is structured for clarity, rooted in substance, and respectful of the reader will always stand out.

More Resources:

Featured Image: N Universe/Shutterstock

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Sep 3 2025
Make AI Writing Work for Your Content & SERP Visibility Strategy [Webinar] via @sejournal, @hethr_campbell

Are your AI writing tools helping or hurting your SEO performance?

Join Nadege Chaffaut and Crystie Bowe from Conductor on September 17, 2025, for a practical webinar on creating AI-informed content that ranks and builds trust.

You’ll Learn How To:

  • Engineer prompts that produce high-quality content
  • Keep your SEO visibility and credibility intact at scale
  • Build authorship and expertise into AI content workflows

Why You Can’t Miss This Session

AI can be a competitive advantage when used the right way. This webinar will give you the frameworks and tactics to scale content that actually performs.

Register Now

Sign up to get actionable strategies for AI content. Can’t make it live? Register anyway, and we’ll send you the full recording.

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Aug 8 2025
How To Create Your Instagram Content Plan (With Free Template) via @sejournal, @donutcaramel13

Are your Instagram posts struggling to gain traction?

With over 2 billion monthly active users, standing out on the platform requires strategy and content planning.

A content plan is an essential blueprint to help you keep your posts aligned with your strategy and your overall marketing goals.

Posting without a plan can just be a wasted effort without clear direction.

To support your brand’s conversion and success, this guide has a free Instagram content plan template and helpful tips that you can customise for your brand.

So, let’s try to capture some of those users.

1. Create Your Content Calendar

A well-structured plan is your roadmap to guide your path, help you meet your goals, and schedule campaigns effectively.

For this purpose, our template comes with an Overview tab and monthly planning tabs with flexible weekly layouts to give you a bird’s-eye view of your content.

It will help you know when you’ve met your goal and can readjust and analyze ways to improve your content strategy for your next one.

Plus, an Instagram content plan can keep ideas, budgets, themes, and marketing initiatives categorized. It also helps you identify any content gaps and build consistency – a key to Instagram success.

Start by downloading your Instagram content plan and make a copy for yourself.

Instagram Content Planner: Make a copyScreenshot by author, July 2025

Begin with the Overview tab by outlining campaign cycles, including key conversion goals, strategic themes, and content pillars with associated budgets.

Move to the main weekly sheet to refine execution. Decide topics and post type, craft appropriate captions, align with campaign types, and define CTAs that support your marketing funnel objectives.

Finally, after you have the above laid out and initial captions, you can move to the next step: Create or assign the necessary key visuals or assets.

Breaking content planning into smaller, actionable steps makes it easier to create a content calendar.

Bonus Tip: Sync With Existing Marketing Initiatives

With a helpful overview or dashboard (included in our Instagram Content Plan), you can map out your seasonal themes, align your topics with days you’re posting, and have your captions and hashtags ready to easily copy and paste when you’re ready to schedule your content.

Instagram Content Planner: OverviewScreenshot by author, July 2025

If you already have some marketing initiatives, it’s the perfect time to incorporate them into your marketing campaign. For example, maybe you have a new product release.

You can then build a content series around it. Tease the product release with a few posts, run a giveaway, feature an influencer using the product in a video, and highlight key benefits throughout.

Events and holidays offer opportunities to boost engagement and attract new customers. They are another fun and positive way to get customers talking about your brand. Holiday giveaways or deals are another way to grow brand awareness and gain followers.

If you have an event coming up, you can create a campaign hyping the event and discussing the speakers involved, products that will be there, or awesome grab bags you’re giving away at the event.

We recommend pairing our Marketing Calendar for 2025 when creating your Instagram content plan to tie in your creative campaigns with holidays and seasonal themes for the week, month, or even quarter.

2. Define Your Goals

Once you have your content template and before you plan your posts, what you want to do is create your Instagram goals.

What do you want to accomplish? Is it to grow your audience, drive more engagement, or increase product sign-ups?

Once you know this, you can set the key performance indicators (KPIs) to mark different points of analysis you want to observe along with your Instagram campaign.

For example, you want to grow your audience by 20% by the end of the campaign cycle, or you wish to increase your engagement rate to at least 0.43%.

After you select your conversion goals, it’s beneficial to break down your goal into milestones you would like to reach.

This way, you can map out the type of content needed for each and track your progress using the KPIs you’ve set above.

Instagram Content Planner Screenshot from author, July 2025

Ask yourself: What milestones can you mark to achieve that goal along the way? What types of content, topics, or content series can you create to increase engagement?

Write down all the goals you think your brand can reasonably achieve (Pro tip: the trick is to make it SMART).

3. Keep Your Theme And Tone Consistent

If you want to keep your posts engaging, ensure visual and tonal consistency by developing a brand guide. You’ll also want to maintain a cohesive theme across all posts, including style, typography, and color palette.

For inspiration, you can look at your website, content, and logos to help create the proper tone and theme for your posts.

Think about the look of your content for both pictures and videos, and consider a consistent angle or filter to set the right tone and look for your content.

It’s also vital to create standard operating procedures (SOPs) about your messaging, whether for captions, comments, or responses to direct messages, because chances are, multiple people are managing the account.

How you respond to consumers on Instagram matters, especially if you have multiple people responding to comments and messages, to ensure it’s within the brand’s tone.

4. Showcase Your Creativity: Instagram Post Types (With Examples)

Instagram is more than just an aesthetic photo-sharing app. It’s a significant platform that can showcase your product in different formats to entertain, engage, and educate audiences.

There are various ways to create content for Instagram that can highlight your brand and increase engagement.

Let’s talk through them for best practices for each use case:

Photos

Pictures are a great way to showcase products’ USPs, share thought leadership quotes, relatable memes, or announce new feature updates.

It’s also great for posing questions that you can answer in your image caption, or promoting deals or giveaways through the use of compelling captions.

  • Example: HubSpot’s AI-generated meme of its customer service rep as a toy figure catches attention and serves as a conversation starter.

Carousels

What can your company do when you have multiple photos from your high-end photoshoot but don’t want to post them into a grid or oversaturate your feed? Try beautifully crafted carousels to ensure return on investment (ROI).

Carousels have been a mainstay on Instagram since 2015. It is a collection of 10 photos you can post all at once, now expandable up to 20.  

To entice your audience, make it interesting to swipe right with chronological storytelling, collage/magazine cutout elements, text overlays, or a narrative.

  • Example: Clickup’s photo of its new AI calendar features text overlay, seamlessly transitioning between both static and dynamic photos and tutorial short videos.

Reels

Next, videos are an excellent way to show sneak peeks of something coming up or create product teasers. You can also use videos for behind-the-scenes content to build product hype.

Consider using Instagram Reels, or short videos, to showcase products, share stories, and grow your audience.

By the way, Instagram discontinued IGTV, or Instagram TV, back in 2021, but you can post longer videos in-feed. Brands use these to go more in-depth into describing a particular topic.

Stories

Meanwhile, Stories are photos or videos that last 24 hours (unless you add them to your highlights on your profile), where you can share posts from your profile or post new content.

It’s a popular way to gain more followers and engage with consumers.

  • Example: Even if Stories expire after 24 hours, they still remain valuable. Sprout Social curated its Stories into its “Trending” highlights, showcasing key events and social media insights, such as the Oscars, Coachella, and Art Basel.

User Generated Content (UGC)

User-generated content, or content created by influencers, customers, or other users, is a great way to extend your reach to different audiences and further promote your products.

People are more intrigued to learn about a new product if it’s promoted by someone they already follow. Likewise, it can help build trust with consumers new to your brand if they see a post by a customer who already loves it.

  • Example: Slack featured its No. 1 “Slacker,” Rox (a senior social media manager at Gozney), as a fun UGC post, where she apparently sent the most Slack messages in a year.

But what content goes viral? It can be beneficial to look at what your competitors are posting on Instagram and put your brand’s unique twist on it.

5. Craft Compelling Captions And CTAs

While it’s great to have high-quality pictures and engaging videos, the captions and call to action still matter.

If you hooked the consumer with your picture or video, you still want to reel them in with your caption and CTA.

IG Content Planner: Fill out columns including CTA Screenshot by author, July 2025

It’s essential to craft the right CTA to ensure consumers follow your page, engage with your post, or purchase your product.

Consider A/B testing to identify the right approach for your campaigns. A compelling call-to-action is clear, concise, and written in an active voice.

6. Choose The Correct Hashtags

Researching and choosing the right hashtags is crucial to ensure your posts reach the intended audience and some new ones that might be interested in your niche and brand.

Hashtags allow your content to reach users beyond your profile’s following as you create content for specific hashtags. Note which posts perform particularly well.

That way, you can create future posts for specific hashtags that will increase your content’s visibility to a broader audience, helping you achieve more brand awareness.

7. Know The Best Time To Post

Planning posts ahead of time can help alleviate some stress from social media strategy.

You can use Meta Business Suites to schedule posts for Facebook and Instagram and set posts for a week or a couple of weeks.

If you’re unsure when to post, here are suggested days and times where analysis points to where you’ll get the most engagement and views.

It would be beneficial to do some research specific to your industry to see the best time and day for you to make your posts.

One important thing to keep in mind when you’re planning your content is the upcoming holidays.

Are you going to post celebrating the holiday, use the holiday to do a promotion or give away, or choose not to post on that day altogether?

No matter what you pick, keeping holidays in mind is crucial.

8. Measure Results And Adjust

Instagram Insights, both on the app and through Meta Business Suites, can show how many views a post gets and statistics on the engagement with the posts to help you see which types of content are working best. You can see your content’s likes, shares, comments, and saves.

Brands can also use Insights to get metrics on the paid activity. Insights are a great way to see trends so that you can adjust your content strategy.

You’ll also be able to see metrics about your followers to see how many you’re receiving, the age of your followers, and information on when they are most active online. This way, you can adjust your post times to ensure you are better at reaching your audience.

Aside from Instagram Insights, explore methods for measuring social media impact beyond vanity metrics to gain deeper insight into customer sentiment and overall brand performance.

Wrapping Up

If your Instagram isn’t getting results, it may be due to a lack of planning.

Don’t miss the opportunity to tie your conversion goals, marketing campaigns, trends, holidays, and creative campaigns together and give it the well-planned, in-advance budget it deserves.

It can only help, not hurt, to create a proactive content plan for your social media team to stay aligned, maintain consistency, and deliver measurable results.

Achieving your goals by developing an Instagram-specific content calendar guided by current marketing objectives and data-driven themes will help your brand engage on the platform.

Download our Instagram content plan and start being more effective with your Instagram strategy.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

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Aug 7 2025
6 AI Marketing Myths That Are Costing You Money [Webinar] via @sejournal, @duchessjenm

Stop letting AI drain your budget. Learn how to make it work for you.

Think AI can fully run your marketing strategy on autopilot? 

Or that AI-generated content should deliver instant results? 

It is time to bust the AI myths that are slowing you down and costing you money.

Join Bailey Beckham, Senior Partner Marketing Manager at CallRail, and Jennifer McDonald, Senior Marketing Manager at Search Engine Journal, on August 21, 2025, for an exclusive webinar. Get the insights you need to stop wasting time and money and start leveraging AI the right way.

In this session, you will learn:

Why this session is essential:

AI tools can’t run your strategy on autopilot. You need to make smarter decisions, ask the right questions, and guide your AI tools to work for you, not against you. 

This webinar will help you unlock AI’s full potential and optimize your content to improve your marketing performance.

Register now to learn how to get your content loved by AI, LLMs, and most importantly, your audience. Can’t attend live? Don’t worry, sign up anyway, and we will send you the on-demand recording.

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Jul 21 2025
How To Get Your Content (& Brand) Recommended By AI & LLMs via @sejournal, @andreasvoniatis

The game has changed, and quite recently, too.

Generative engine optimization (GEO), AI Overviews (AIOs), or just an extension of SEO (now being dubbed on LinkedIn as Search Everywhere Optimization) – which acronym is correct?

I’d argue it’s GEO, as you’ll see why. And if you’ve ever built your own large language model from scratch like I did in 2020, you’ll know why.

We’ve all seen various frightening (for some) data on how click-through rates have now dropped off the cliff with Google AIOs, how LLMs like ChatGPT are eroding Google’s share of search – basically “SEO is dead” – so I won’t repeat them here.

What I will cover are first principles to get your content (along with your company) recommended by AI and LLMs alike.

Everything I disclose here is based on real-world experiences of AI search successes achieved with clients.

Using an example I can talk about, I’ll go with Boundless as seen below.

Screenshot by author, July 2025

Tell The World Something New

Imagine the dread a PR agency might feel if it signed up a new business client only to find they haven’t got anything newsworthy to promote to the media – a tough sell. Traditional SEO content is a bit like that.

We’ve all seen and done the rather tired ultimate content guide to [insert your target topic] playbooks, which attempt to turn your website into the Wikipedia (a key data source for ChatGPT, it seems) of whatever industry you happen to be in.

And let’s face it, it worked so well, it ruined the internet, according to The Verge.

The fundamental problem with that type of SEO content is that it has no information gain. When trillions of webpages all follow the same “best practice” playbook, they’re not telling the world anything genuinely new.

You only have to look at the Information Gain patent by Google to underscore the importance of content possessing value, i.e., your content must tell the world (via the internet) something new.

BoundlessHQ commissioned a survey on remote work, asking ‘Ideally, where would you like to work from if it were your choice?’

The results provided a set of data and this kind of content is high effort, unique, and value-adding enough to get cited in AI search results.

Of course, it shouldn’t take AI to produce this kind of content in the first place, as that would be good SEO content marketing in any case. AI has simply forced our hand (more on that later).

After all, if your content isn’t unique, why would journalists mention you? Bloggers link back to you? People share or bookmark your page? AI retrain its models using your content or cite your brand?

You get the idea.

For improved AI visibility, include your data sources and research methods with their limitations, as this level of transparency makes your content more verifiable to AI.

Also, updating your data more regularly than annually will indicate reliability to AI as a trusted information source for citation. What LLM doesn’t want more recent data?

SEO May Not Be Dead, But Keywords Definitely Are

Keywords don’t tell you who’s actually searching. They just tell you what terms trigger ads in Google.

Your content could be appealing to students, retirees, or anyone. That’s not targeting; that’s one size fits all. And in the AI age, one size definitely doesn’t fit all.

So, kiss goodbye to content guides written in one form of English, which win traffic across all English-speaking regions.

AI has created more jobs for marketers, so to win the same traffic as before, you’ll need to create the same content as before for those English-speaking regions.

Keyword tools also allegedly tell you the search volumes your keywords are getting (if you still want them, we don’t).

So, if you’re planning your content strategy on keyword research, stop. You’re optimizing for the wrong search engine.

What you can do instead is a robust market research based on the raw data sources used by LLMs (not the LLM outputs themselves). For example, Grok uses X (Twitter), ChatGPT has publishing partnerships, and so on.

The discussions are the real topics to place your content strategy around, and their volume is the real content demand.

AI Inputs, Not AI Outputs

I’m seeing some discussions (recommendations even) that creating data-driven or research-based content works for getting AI recommendations.

Given the dearth of true data-driven content that AI craves, enjoy it while it lasts, as that will only work in the short term.

AI has raised the content bar, meaning people are specific in their search patterns, such is their confidence in the technology.

Therefore, content marketers will rise to the challenge to produce more targeted, substantial content.

But, even if you are using LLMs in “deep” mode on a premium subscription to inject more substance and value into your content, that simply won’t make the AI’s quality cut.

Expecting such fanciful results is like asking AI to rehydrate itself using its sweat.

The results of AI are derivative, diluted, and hallucinatory by nature. The hallucinatory nature is one of the reasons why I don’t fear LLMs leading to artificial general intelligence (AGI), but that’s another conversation.

Because of the value degradation of the results, AI will not want to risk degrading its models on content founded on AI outputs for fear of becoming dumber.

To create content that AI prefers, you need to be using the same data sources that feed AI engines. It’s long been known that Google started its LLM project over a decade ago when it started training its models on Google Books and other literature.

While most of us won’t have the budget for an X.com data firehose, you can still find creative ways (like we have), such as taking out surveys with robust sample sizes.

Some meaningful press coverage, media mentions, and good backlinks will be significant enough to shift AI into seeing the value of your content, being judged good enough to retrain its models and update its worldview.

And by data-mining the same data sources, you can start structuring content as direct answers to questions.

You’ll also find your content is written to be more conversational to match the search patterns used by your target buyers when they prompt for solutions.

SEO Basics Still Matter

GEO and SEO are not the same. The reverse engineering of search engine results pages to direct content strategy and formulation was effective because rank position is a regression problem.

In AI, there is no rank; there are only winners and losers.

However, there are some heavy overlaps that won’t go away and are even more critical than ever.

Unlike SEO, where more word count was generally more, AI faces the additional constraints of rising energy costs and shortages of computer chips.

That means content needs to be even more efficient than it is for search engines for AI to break down and parse meaning before it can determine its value.

So, by all means:

  • Code pages for faster loading and quicker processing.
  • Deploy schema for adding context to the content.
  • Build a conversational answer-first content architecture.
  • Use HTML anchor jump links to different sections of your content.
  • Open your content to LLM crawling and use llms.txt file.
  • Provide programmatic content access, RSS feeds, or other.

These practices are more points of hygiene to help make your content more discoverable. They may not be a game changer for getting your organization cited by AI, but if you can crush GEO, you’ll crush SEO.

Human, Not AI-Written

AI engines don’t cite boring rehashes. They’re too busy doing that job for us and instead cite sources for their rehash instead.

Now, I have heard arguments say that if the quality of the content (let’s assume it even includes information gain) is on point, then AI shouldn’t care whether it was written by AI or a human.

I’d argue otherwise. Because the last thing any LLM creator wants is their LLM to be retrained on content generated by AI.

While it’s unlikely that generative outputs are tagged in any way, it’s pretty obvious to humans when content is AI-written, and it’s also pretty obvious statistically to AI engines, too.

LLMs will have certain tropes that are common to AI-generated writing, like “The future of … “.

LLMs won’t default to generating lived personal experiences or spontaneously generating subtle humour without heavy creative prompting.

So, don’t do it. Keep your content written by humans.

The Future Is A New Targeted Substantial Value

Getting your content and your company recommended by AI means it needs to tell the world something new.

Make sure it offers information gain based on substantive, non-LLM-derived research (enough to make it worthy of LLM model inclusion), nailing the SEO basics, and keeping it human-written.

The question now becomes, “What can you do to produce high-effort content good enough for AI without costing the earth?”

More Resources:

Featured Image: Collagery/Shutterstock

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