Boost Leads with AI: Publish Smarter, Not Harder!

Find the platforms that matter

All AI models don’t read the same sites. Some tools pull more from news sites, others use forums or reference sites. We can find out which sites matter for our niche and then put content there.

There are two ways to learn which platforms AI tools use:

• Check research and lists from SEO and AI companies. For example, some reports show which domains appear most in Perplexity citations: Yahoo, MarketWatch, CNBC, Reddit and others depending on topic.
• Ask the AI tools directly. We can prompt them to list the sources they commonly use for a topic. We might need to ask multiple times or refine the prompt to get a clear list.

Examples from research show patterns. For e-commerce, Amazon and Wikipedia often show up. For finance, CNBC, Investopedia, and NerdWallet appear. For tech B2B queries, AI can source from Reddit, GeeksforGeeks, and security sites like Kaspersky. That means if we want AI to cite us for finance queries, we should publish on or get referenced by the finance sites AI reads.

How to ask AI which sites it uses (simple prompts)

We can get a quick list of sources by asking the AI. Use clear prompts and then follow up. Here are step-by-step prompts we can use:

1. Ask a general question: “For queries about [our industry], which websites do you most often use for citations?”
2. Ask for a ranked list: “List the top 20 domains you use for citations when answering about [topic].”
3. Verify by asking: “Which of those domains are news sites, forums, product sites, or reference sites?”
4. Repeat on multiple AI tools (e.g., Perplexity, ChatGPT) and compare results.

We may need to refine our wording. If the tool does not give a clear list, we can ask it to “explain how you select citations for [topic].” This often reveals patterns we can act on.

Types of branded content to publish

Not every piece of content must live on our main site. We can publish different content types across platforms. Here are actionable content types and where they fit:

• Short how-to articles and FAQs — publish on industry blogs, Medium, LinkedIn, and community sites.
• Press releases and news items — use PR sites that AI models read for timely citations.
• Product pages and listings — Amazon, product directories, marketplace pages.
• Location pages — local business directories, Yelp, Google Business Profile content, and local news sites.
• Forum posts and answers — Reddit, Stack Exchange, industry forums and Q&A sites.
• Guest posts and contributed articles — publish on sites that AI reads often within your industry.
• Profiles and reviews — company profile pages, Glassdoor, Trustpilot, and other review sites.

Always tie these pieces back to the company, product, or location we want AI to surface. A good post will mention the company name and link back to the main site or a product page.

How to get published on those platforms

Some platforms let anyone publish easily. Others need outreach. Here are low-risk ways to get content on them:

• Use guest post programs. Many blogs accept guest posts if the content helps their readers.
• Create helpful answers on forums. Don’t spam. Add value and include a short link or mention of your company when relevant.
• Use mainstream publication platforms like Medium and LinkedIn where we control the content.
• Set up proper profiles on major directories and product sites. Fill out all fields and upload images.
• Do press outreach for newsworthy items. Local news sites and trade press are often cited by AI models.

We must keep everything honest. AI systems prefer trustworthy sources. That means accurate facts, clear authorship, and proper citations. Avoid low-quality or manipulative tactics. High-quality publication increases the chance the AI will trust and cite our content.

How to structure content so AI will cite it

Write content so it answers common questions clearly. AI tools like to pull short, direct answers. Use headings, simple sentences, and bullet lists. Include the company name and location in the text. Include schema or structured data if the platform supports it.

Quick checklist for each piece of content:

• Start with the main question or topic.
• Give a short answer in the first 1–2 sentences.
• Include the company name and link back to the product or location.
• Add supporting details below the short answer.
• Use headings and bullet lists for easy parsing.

Measure and iterate

We must track which pieces earn AI citations and which drive traffic. Use these signals:

• Traffic spikes from known AI sources (tools often include query info or referrer tags).
• Incoming leads with notes like “found via AI” or “from Perplexity.”
• Search console impressions and any labeling that shows AI features.

When a piece yields leads, do more like it on the same platform. If a platform never shows up in AI answers, we can shift our effort elsewhere.

Sample plan we can follow in 4 weeks

1. Week 1 — Research: Ask 2–3 AI tools which domains they cite for our main keywords. Make a short list of 10 sites.
2. Week 2 — Content map: Plan 8 branded pieces (product pages, FAQs, guest posts, forum answers) and assign platforms from the list.
3. Week 3 — Publish: Post the content on those platforms. Make sure each piece ties back to the company and a product or location.
4. Week 4 — Measure: Check traffic and leads. Double down on the top-performing platform types.

Common mistakes to avoid

• Publishing only on the main website. That limits citation opportunities.
• Posting content that never mentions the company or product. The AI won’t link it back to us.
• Using low-quality link farms or spammy sites. AI models downgrade thin content and untrusted sources.
• Expecting instant results. This is a volume and placement game. It takes steady work.

Quick example prompts to use with AI tools

Here are two simple prompts we can paste into an AI tool to start research:

Prompt 1: “For queries about [our industry], list the top 20 websites you most often use as citations. Group them by type (news, reference, forum, product).”

Prompt 2: “Which domains do you cite most when answering questions about [topic]. Why do you choose those sources?”

If the answer is vague, ask follow-up questions. We may need to run the prompts in multiple AI tools and compare results.

What success looks like

We will know the plan works when AI tools start showing our domain or our published pages in their short answers. That means more direct traffic, more phone calls, and more leads. Over time, the goal is to make AI tools see our brand as a common, trusted source for the topics we care about.

FAQ

How many platforms should we publish on?

There is no fixed number. Start with 5–10 platforms that the AI tools actually read for your industry. Then expand as you get results.

Can we just post the same article everywhere?

Try to tailor the content slightly for each platform. Exact duplicates can be fine on some sites, but unique introductions or platform-specific details help. Always keep the company mention and links.

Is it safe to edit Wikipedia or high-profile sites?

Editing Wikipedia is possible but must follow their rules. Provide neutral, referenced content. Focus first on easier platforms like Medium, LinkedIn, and industry forums. Use high-profile sites when you have clear references and authority.

How long before we see leads from this work?

It varies. Sometimes AI citations show up quickly for new content. Other times it takes weeks. Track results and keep publishing. The more consistent we are, the faster the growth.

Will AI always use the same sites?

No. Models and their source lists change. That is why we check periodically and ask the AI tools which sources they use. Update our list every few months.

Conclusion

AI-driven traffic is real and growing. Our best move is to publish more branded content across the platforms that the language models read. Find those platforms by asking the AI and checking research. Publish clear, company-linked answers and pages. Track what works, and then repeat. We don’t need complicated tricks — we need steady, smart publishing where the machines are looking.

Start small, test, and scale. That is how we turn AI mentions into real, repeatable leads.