Small Business Articles

Most real estate agents are using AI incorrectly because they’re trying to use one tool for every task. This video explains: What ChatGPT does best When to use Perplexity

Why Claude is powerful for document review

How Gemini fits into Google Workspace

Why NotebookLM is one of the safest AI tools for document-grounded workflows

You’ll also learn how agents combine all five tools into a complete AI workflow system for:

Buyer consultations

Listing preparation

HOA analysis Market research

Client communication Brokerage workflows

GetAI Academy™

Most real estate agents waste time opening multiple browser tabs before buyer showings and listing appointments.

Perplexity changes that workflow.

This video explains:

What Perplexity is

How it differs from ChatGPT

Why citation visibility matters

Seven practical research workflows for agents

How to use Perplexity responsibly in regulated real estate environments

Topics include:

Neighborhood research

School district research

Development projects

HOA research Market conditions

Competitive listing prep GetAI Academy™

Search is changing faster than most businesses realize.

It’s no longer just: → “How do I rank on Google?”

Now it’s: → “How do I get cited in AI answers?”

Platforms like ChatGPT, Google AI Overviews, and Perplexity are changing how buyers discover businesses.

And the rules are different.

Ranking alone isn’t enough.

You need:

Structured content

Entity signals

Clear, factual answers

Authority across topics

The businesses that adapt early will dominate visibility.

Everyone else will be playing catch-up.

We broke down how this works here: https://thinkdmg.com/ai-search-optimization/

Most businesses are still treating content like a blog.

Post → hope it ranks → move on.

That’s not how content works anymore.

The brands actually growing organic traffic are building:

Topic clusters

Authority signals

Content that answers real questions

Not random articles.

Content today needs to do two things:

Rank in search

Get cited by AI tools

That changes everything.

If you're still publishing without a strategy, you're wasting time.

Most small businesses don’t have a traffic problem — they have a visibility problem.

I’ve been digging into why so many local businesses struggle to get consistent leads from Google, and it usually comes down to this:

They’re targeting the wrong keywords

Their site isn’t structured properly

They’re not building any real authority

And now with AI search (ChatGPT, Google AI Overviews), it’s getting even harder — because it’s not just about ranking anymore. It’s about being trusted enough to be cited.

The businesses I’m seeing win right now are doing a few things differently:

Building actual topic authority (not random blog posts)

Structuring content so AI can understand it

Aligning SEO with how people actually search today

I put together a breakdown of how this works here: https://thinkdmg.com/seo-services/

https://thinkdmg.com/the-linkedin-ai-citation-playbook-nobodys-talking-about-how-to-earn-it-instead-of-game-it/

By now you’ve probably seen the headline: LinkedIn is the #2 most cited domain across ChatGPT Search, Perplexity, and Google AI Mode. Marketers are scrambling to “optimize for AI visibility,” vendors are selling new tools weekly, and your Slack channels are full of screenshots.

Here’s what the conversation is mostly missing: the difference between earning a citation and gaming one — and why that difference will determine whether your LinkedIn AI strategy compounds or collapses.

This article is the tactical follow-up to our pillar piece on LinkedIn and AI Search in 2026. If you haven’t read that yet, start there. What follows assumes you understand why visibility alone isn’t the goal. Here we’re going deep on how — specifically the three mechanics most LinkedIn AI guides never mention.

The Problem With Most LinkedIn AI Advice

Most of what’s being written right now about LinkedIn and AI search tells you some version of the same thing: post more, post consistently, write long-form articles, use educational content, build your follower count.

That advice isn’t wrong. The Semrush study of 89,000 cited LinkedIn URLs confirms that frequent posters, original content, and educational framing all correlate with AI citations.

But here’s the gap: that advice treats LinkedIn as a closed loop. Post on LinkedIn → get cited in AI → done.

The reality of how AI citation actually works is far more distributed than that. And if you only optimize inside LinkedIn’s walls, you’re leaving the majority of your citation potential untouched.

There are three moves that separate teams who are building durable AI visibility from teams who are just posting more:

Earn the citation — don’t manufacture it

Build the distribution flywheel beyond LinkedIn

Track the branded prompts your buyers are actually typing

Let’s go through each.

Move 1: Earn the Citation — Don’t Manufacture It

There’s a specific type of content flooding LinkedIn right now. You’ve seen it. The listicle dressed up as insight. The “10 things AI taught me about leadership” post. The agency blog that publishes 50 variations of “we are thought leaders” without ever demonstrating thought leadership. Auto-generated content published at volume, optimized for semantic signals, written for algorithms rather than people.

This content can generate citations. In the short term, it often does. And that’s exactly what makes it dangerous.

Wil Reynolds at Seer Interactive puts it bluntly: AI is summarizing the internet, and beliefs live in people’s heads. When AI cites your content, it pulls forward the language, framing, and conclusions in that content with roughly 0.60 semantic fidelity — meaning AI responses closely mirror what your LinkedIn content actually says. If what your LinkedIn content says is generic, optimized filler, that’s what AI will amplify about you.

You aren’t just optimizing for a ranking. You’re training AI’s opinion of your brand.Professional Network AI Citation Playbook

What Actually Gets Cited (And Why)

The Semrush data is instructive here. The most-cited LinkedIn content shares a consistent profile:

Original, not reshared. About 95% of cited posts are original content. Reshares account for just 5% of citations. AI rewards people who add something to the conversation, not people who pass it along.

Educational, not promotional. Over half of all cited content is knowledge or advice-driven. Content that explains how something works, shares a specific result, or documents a real process outperforms content that announces things.

Moderate engagement, high relevance. The median cited post has 15–25 reactions. The posts going viral are not the posts getting cited. AI retrieval is not a popularity contest — it rewards relevance to the query.

The example Semrush highlights is telling: one of the top-cited LinkedIn articles in their dataset is a piece where an author draws on firsthand experience to rank the best SEO newsletters and explain each recommendation. It wasn’t a viral post. It wasn’t produced at scale. It was specific, useful, and authoritative — and AI keeps surfacing it because it keeps being the right answer.

The Practical Test Before You Publish

Before you publish any piece of LinkedIn content ask: Would I send this to a client in a DM as a resource? Wil Reynolds frames this perfectly — look through your sent DMs with links. How many of them look like auto-generated listicles? Almost none. Because your reputation is on the line when you make a recommendation. Hold your content to that standard.

If the answer is no, rework it or don’t publish it. Speed-optimized content that doesn’t clear that bar is quietly eroding the brand equity your AI visibility depends on.

Move 2: Build the Distribution Flywheel Beyond LinkedIn

This is the single biggest gap in most LinkedIn AI visibility strategies, and the research makes the opportunity impossible to ignore.

The Citation Lift Study

Stacker partnered with AI visibility platform Scrunch on a study analyzing eight articles across five LLMs and 944 prompt-platform combinations. They measured citation rates for the same stories published only on brand domains versus those same stories distributed across trusted third-party news publishers.

That’s not a marginal improvement. That’s a structural one.

The mechanism is straightforward. When your content lives only on LinkedIn or your company blog, an AI model has one opportunity to encounter it. If your domain doesn’t carry strong topical authority for the query, that single touchpoint may not register.

When the same story appears across multiple trusted publisher domains — earned placements, syndicated articles, industry newsletters, contributed pieces — the model encounters that information pattern in multiple contexts. That repetition across authoritative sources is what signals to AI that this content is worth citing.

Syndicated-only citations are particularly instructive: in the Stacker study, 19.2% of citations came exclusively from third-party versions of the content — the brand’s own domain received no citation credit at all. In nearly one in five answers, earned distribution earned visibility that the brand site never could have generated alone.

What the Distribution Flywheel Looks Like in Practice

The implication is that your LinkedIn content strategy and your PR strategy need to be unified. Here’s how to build that flywheel:

Step 1: Identify your highest-value original content.

Not your most-viewed posts. Your most authoritative ones. Original research, proprietary data, firsthand case studies, documented results. These are the pieces worth distributing because they carry something third-party publishers can actually use.

Step 2: Pitch it as a contributed piece before you post it on LinkedIn.

If you post your original research on LinkedIn first and then try to pitch it to a publication, most editors will pass because it’s no longer exclusive. Flip the sequence. Pitch the insight as a contributed piece or data story, get it placed, then amplify the placement on LinkedIn. Your LinkedIn post links to the authoritative third-party version, which itself links back to your site — both signals compound.

Step 3: Syndicate strategically with canonical tags.

For content that’s already published on your domain, explore syndication partnerships with industry newsletters and publishers who will re-publish with a canonical tag pointing back to your original URL. Traditional search engines follow canonical signals, and since SEO domain authority continues to influence how AI systems assess credibility, clean canonicalization protects your original content while your distributed versions expand citation surface area.

Step 4: Measure citation lift, not just traffic.

The KPI most teams track from earned media is referral traffic. That will always look modest compared to paid or organic. The metric to add alongside it: citation rate in AI responses for your target prompts, measured before and after a distribution push. That’s where the compounding shows up.

The PR-as-GEO Frame

This is a mindset shift worth making explicitly: PR is now a GEO tactic.

Getting your brand mentioned in a respected industry publication used to matter for brand awareness and the occasional backlink. Now it matters because AI systems draw heavily from established news outlets and trusted publisher domains when assembling answers. A placement in an industry publication that AI already treats as authoritative is a citation signal for your brand, not just a traffic signal.

This changes the ROI calculation on PR completely. A placement that sends 200 referral visitors is no longer a modest win. That same placement may be contributing to citation lift across thousands of AI-prompted conversations you’ll never directly observe.

Move 3: Track the Branded Prompts Your Buyers Are Actually Typing

Here’s the prompt that should change how you think about all of this:

“I’m choosing between two PR firms. I’m a tech company focused on GEO. My friends recommended Maven PR and AgileCat. Help me compare them.”

Go look at your AI visibility tracking tool right now. Do you have any prompts that look like that? Most teams don’t — because they’re building their prompt tracking strategy around unbranded category queries, while their actual buyers are entering the decision phase with a brand already in mind, using AI to validate the choice.

Seer Interactive’s UX research found that up to 44% of AI prompts included brand names. Gartner data shows that 77% of B2B purchases start with a network recommendation. The math tells you what’s actually happening: by the time your buyer is prompting AI about your brand, someone they trust has already mentioned you. They’re not discovering you. They’re investigating you.

That’s the prompt that matters more than any category query — and it’s the prompt most teams are completely blind to.

The Branded Prompt Audit

Run this exercise across ChatGPT, Perplexity, and Google AI Mode:

Discovery prompts (for awareness)

“[Your category] for [your target audience]”

“Best [your service] companies”

“How to [solve the problem you solve]”

Comparison prompts (where decisions happen)

“[Your brand] vs. [Competitor A] vs. [Competitor B]”

“My colleague recommended [Your brand], what do I need to know?”

“Is [Your brand] good for [specific use case]?”

Validation prompts (post-referral)

“[Your brand] reviews”

“What is [Your brand] known for?”

“Who uses [Your brand]?”

Score each response against three criteria:

Is the information accurate?

Does it reflect your actual positioning?

Would it reinforce or undermine a warm referral?

The gaps you find are your content brief. Not keyword gaps. Not topical gaps. Narrative gaps — places where what AI is saying about you doesn’t match what you want to be known for, or doesn’t match the level of credibility a buyer needs to move forward.  

Web Data vs. Training Data: A Gap Worth Tracking

Seer built a tool to compare how a brand appears in AI responses when web search is enabled versus when AI is drawing purely from training data. This distinction matters because:

Training data reflects what AI learned about your brand during model training — accumulated over time from all available public sources

Live web data reflects what AI can find right now when given access to search

If you perform significantly better when web search is enabled, that means your recent content and earned placements are working — but they haven’t yet influenced the model’s underlying knowledge of your brand. Your GEO strategy should include both: building current web presence that AI can retrieve today, and building the kind of durable, widely-distributed brand record that shapes training data over time.

If you perform better from training data than from live web, that’s a different signal — your historical brand equity is strong but your recent content isn’t reinforcing it. Time to close that gap.

Putting the Three Moves Together

Here’s how these three moves compound on each other in practice:

A team doing Move 1 alone publishes quality original content on LinkedIn consistently. They earn some citations. They’re building credibility. But their citation surface area is capped by LinkedIn’s single-domain authority, and they have no visibility into how their brand is performing in the comparison prompts that precede purchases.

A team doing Moves 1 and 2 creates that same quality content and distributes it through earned media placements. Their citation rate is now potentially 4x what it would be from LinkedIn alone. AI encounters their content in more trusted contexts and surfaces it more frequently.

A team doing all three moves earns citations, distributes them across multiple authoritative domains, and tracks the branded prompts where buying decisions are actually being made. They know not just whether they’re being cited — but whether those citations are converting to trust, and whether their narrative in AI matches the brand they’re trying to build.

That third team isn’t just optimizing for AI visibility. They’re building a brand that compounds — one that earns word-of-mouth referrals, shows up accurately when AI is consulted, and reinforces the recommendation rather than undermining it.

Download Available – The AI Citation Playbook

A Note on the Long Game

There’s real tension in this space right now between short-term tactics that generate visible metrics quickly and long-term strategies that build something durable.

The short-term tactics aren’t without merit. Volume-based content can earn citations. Keyword-dense articles can generate AI impressions. If your goal is a screenshot for next quarter’s report, these approaches work.

But every piece of generic, algorithmically-optimized content you publish is training AI’s description of your brand. Every shortcut you take in content quality is a data point in the model’s understanding of what you stand for. And every citation earned by content that doesn’t actually represent your best work is a citation that might get you seen without getting you believed.

The teams that will win in AI search over the next three years aren’t the ones who move fastest. They’re the ones who build the most credible, widely-distributed, narratively-consistent body of work. The ones who treat citation lift not as a traffic hack but as the natural result of being the most authoritative source on the things they actually know best.

Earn the citation. Distribute the content. Track what buyers actually search. The playbook isn’t complicated. It’s just harder than it looks.

This is Part 2 in thinkdmg.com’s series on LinkedIn, AI search, and the future of brand visibility. Read the full foundation in Part 1: LinkedIn and AI Search in 2026 — The Complete Playbook.

AI Search & LinkedIn Strategy Series

Part 1: LinkedIn and AI Search in 2026 — The Complete Playbook for Visibility, Trust, and Getting Chosen

Part 2: The LinkedIn AI Citation Playbook Nobody’s Talking About — How to Earn It Instead of Game It

Part 3: Stop Optimizing for AI. Start Optimizing for the Person Who Will Prompt AI About You

Part 4: LinkedIn Gets You Seen. Here’s What Actually Gets You Chosen

Sources: Semrush LinkedIn AI Visibility Study (March 2026), Stacker/Scrunch Citation Lift Study (December 2025), Seer Interactive GEO Research (March 2026), Gartner B2B Buying Research.

Here’s what the conversation is mostly missing: the difference between earning a citation and gaming one — and why that difference will determine whether your LinkedIn AI strategy compounds or collapses.

LinkedIn visibility is rising fast in AI search. But visibility alone doesn’t convert.

In this video, we break down the real journey:

Seen → Believed → Chosen

And why most marketing strategies stop too early.

You’ll learn:

• Why AI visibility doesn’t guarantee trust

• What buyers actually do before they choose

• The difference between discovery content and validation content

LinkedIn is the #2 cited domain across ChatGPT Search, Perplexity, and Google AI Mode — showing up in roughly 11% of AI-generated responses.

Most of the conversation about this stops at: "post more on LinkedIn."

That's not wrong. But it's also not the question that matters.

The question that matters is: after AI cites you, then what?

The three-step journey almost everyone is only completing one step of:

Wil Reynolds at Seer Interactive frames the job of marketing as three words: Seen. Believed. Chosen.

LinkedIn AI visibility hands you Seen. That's step one.

Most LinkedIn AI optimization advice stops there. But citation is not conversion. Visibility is not trust. And trust is not the same as being chosen.

The gap between step one and step three is where most LinkedIn AI strategies quietly fall apart — and almost nobody is talking about why.

What buyers actually do before they buy (Gartner + Seer UX research):

The recommendation. Someone in their network mentions your name. Gartner data: 77% of B2B purchases begin with a network recommendation — not a search.

The AI query. Before the first call, before the website visit — they open ChatGPT or Perplexity and type your brand name, or a comparison between you and a competitor. Seer's UX research found 44% of AI prompts include brand names.

The website visit. If AI gave them enough confidence, they arrive looking for proof — case studies, methodology, specific evidence that the recommendation was sound.

The conversation. They reach out already partly sold — or partly uncertain — depending on what steps 2 and 3 delivered.

Most LinkedIn AI optimization is built for strangers at stage zero — people who've never heard of you. That audience matters.

But the buyer at stage two — the warm referral doing AI due diligence — converts at dramatically higher rates. They arrived with trust already in the system. Your job at stage two isn't to sell them. It's to not unsell them.

And the content that keeps them sold at stage two is not always the same content that gets you cited at stage zero. That tension is the thing nobody in this space is talking about clearly enough.

The two content jobs most teams are conflating:

Job 1 — Discovery content: Built to get you found. Answers questions strangers are asking. Optimized for AI retrieval, LinkedIn algorithm, shareability. This is how you get seen. Without it, you don't get to step one.

Job 2 — Validation content: Built to get you believed. The case study with specific numbers and a named client who will stand behind it. The opinion piece where you take an actual position — not "here are both sides," but "here's what we think, and here's why." The methodology breakdown specific enough that a buyer can evaluate whether your approach fits their situation.

Validation content doesn't go viral. It doesn't always get cited in broad AI category queries. But it does the work that converts interest into trust and trust into revenue.

Most LinkedIn content calendars are 90% discovery, 10% validation — if validation appears at all. For brands already known in their category, that ratio should tip the other direction.

A fast diagnostic for your own content:

Look at your last 20 LinkedIn posts. For each one, ask: would I send this to a potential customer in a DM as a genuine resource?

Not as a broadcast. As a personal recommendation, with your reputation behind it.

The content that fails that test — the keyword-optimized articles that don't say anything new, the listicles every competitor has already published, the posts written to demonstrate posting frequency — that content is generating impressions and potentially AI citations. But it's not building the trust that makes those citations worth anything.

AI can surface almost any content. Only the DM-worthy content builds a brand that gets recommended.

The measurement gap most teams have:

Teams tracking AI citation rate are tracking a leading indicator, not a destination. What to add:

Branded search volume — is your brand being searched by name? Growth here signals actual word-of-mouth health

Direct traffic — people who type your URL directly have already made a decision about you

Branded comparison prompt performance — what does ChatGPT say when someone searches "your brand vs. competitor"? Is it accurate? Is it compelling?

Revenue from customers who mention a referral — that's the metric at the end of all of it

The honest version of the advice:

There's a lot of money flowing right now toward LinkedIn AI visibility — tools, agencies, service lines, job titles. Some of those answers are legitimate. Some are the SEO keyword game running the same play with different terminology, chasing AI citations the way an earlier generation chased backlinks.

The version that actually serves you over the next three years sounds less exciting:

Publish original content that earns trust. Distribute it through channels that have earned their own authority. Be consistent in what you stand for. Track whether buyers are being reinforced or undermined when they look you up after a recommendation.

That's not an AI strategy. That's a brand strategy. In 2026, they're the same thing.

AI is now the mechanism through which your reputation travels. You can optimize for that mechanism and produce citations — or you can build for it fundamentally and produce trust.

Only one of those produces customers.

LinkedIn is now the #2 most cited source in AI-generated answers across ChatGPT, Google AI Mode, and Perplexity. But here’s the problem: visibility alone doesn’t get you customers. In this video, we break down what most businesses are getting wrong about AI visibility—and why trust, not content volume, is what actually determines whether you get chosen. You’ll learn:

Why LinkedIn is dominating AI search results

What “semantic similarity” means (and why it matters)

The difference between being seen vs believed vs chosen

Why 44% of AI prompts include brand names

How earned media can increase AI citations by 325%

The real strategy behind AI visibility that most companies ignore

This isn’t about publishing more content. It’s about building something worth being cited—and trusted.

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🏢 About Digital Marketing Group:

There's a stat making the rounds: LinkedIn shows up in roughly 11% of AI-generated responses across ChatGPT Search, Perplexity, and Google AI Mode — making it the #2 cited domain overall, ahead of Wikipedia and every major news publisher.

Most people share the number and stop there.

Here's what the data actually tells you to do with it.

What Semrush found (89,000 cited LinkedIn URLs, 325,000 prompts):

Citation rates vary dramatically by platform: ChatGPT Search cites LinkedIn 14.3% of the time. Google AI Mode: 13.5%. Perplexity: only 5.3%.

95% of cited posts are original content. Reshares account for 5%.

Articles in the 500–2,000 word range dominate citations (50–66% of cited content).

Feed posts perform best at 50–299 words.

The median cited post has 15–25 reactions. AI citation is not a popularity contest — it rewards relevance, not virality.

Authors who post 5+ times per month account for ~75% of citations. Accounts with fewer than 500 followers are cited at nearly the same rate as accounts with 2,000+.

The company vs. individual split nobody talks about:

Perplexity cites Company Pages 59% of the time.

ChatGPT and Google AI Mode cite individual profiles 59% of the time.

If your entire LinkedIn strategy lives on your Company Page, you're invisible on two of the three major AI platforms. If it's all personal brand, you're leaving Perplexity citations on the table. You need both.

The distribution data most teams are completely ignoring:

Stacker/Scrunch ran a citation lift study across 5 LLMs and 944 prompt-platform combinations. They compared brand-only content to the same content distributed across trusted third-party publishers.

Brand-only citation rate: 7.6%

With earned distribution: 34%

That's a 325% lift

The mechanism isn't complicated: when content only lives on your LinkedIn or your blog, an AI has one shot to encounter it. When it lives across multiple trusted domains, the model sees it in multiple contexts — and that pattern signals authority in a way a single source can't.

The part that changes how you think about all of this:

Being cited is not the same as being chosen.

Seer Interactive's GEO research found that up to 44% of AI prompts include brand names — meaning a huge portion of AI search activity happens after a recommendation, not before it. The buyer isn't searching "best PR firms." They're searching "my colleague recommended [Brand A] and [Brand B] — help me compare them."

Gartner data backs this up: 77% of B2B purchases begin with a network recommendation.

By the time someone types your brand name into an AI, the sale is half-made — or half-lost. What that AI says about you in that moment either reinforces the referral or introduces doubt.

Most marketing teams are tracking category keyword prompts while buyers are already in the decision stage.

Practical takeaways:

Run branded prompts across ChatGPT, Perplexity, and Google AI Mode right now. Read what comes back. Does it match your actual positioning?

Publish original articles (500–2,000 words), not listicles built for AI impressions. Educational intent wins.

Post consistently (5x/month minimum). Frequency and expertise beat follower count.

Use both Company Page and individual thought leadership — they feed different AI platforms.

Treat your best LinkedIn content as pitchable to industry publications. Your PR strategy and your LinkedIn strategy are now the same strategy.

Track branded search volume and direct traffic alongside AI citation rate. Visibility without trust doesn't convert.

One thing worth being honest about:

There's a whole industry of tools selling AI visibility at speed — keyword-dense articles, semantic clusters, auto-generated variations. That content can generate citations. But AI repeats your framing with ~0.60 semantic fidelity. Generic content gets amplified generically. It teaches the model nothing about what makes you worth choosing.

The visibility gain is real. The trust gap it creates won't show up in your dashboard — until it costs you a deal.