If you really want to win at AI search, stop thinking about LLMs altogether.

Instead, think about your best-fit customers.

Not just their needs or the problems they face — but the exact moments they decide they need to take action.

Academics call these “category entry points.” Most marketers call them “triggers.”
Either way, they’re essential for understanding what motivates someone to go looking for a solution like yours.

And when you can tap into these “triggers,” that’s when you start generating real sales conversations, signups, demos, and pipeline.

Tapping into this can be tricky with traditional SEO. Sure, you can build a landing page for ‘best accounting software for lawyers’ and drive to a landing page. That works.

But Google misses so much nuance and personalization that AI search is now able to capture.

Introducing the power of context (AI search style)

Because people will punch in a whole lot more details — context — into ChatGPT than they ever will in Google. And all the effort and detail that goes into that prompt is far more likely to convert than a 3–5 word phrase.

Instead of “best accounting software for lawyers”, you’ll see prompts like:

“I’m a new lawyer who isn’t tech savvy. I need an accounting tool that’s easy to set up but still helps me organize all my billings.”

Or:

“Our law firm uses QuickBooks but it’s too limited. I need something that supports retainers, recurring invoices, client trust accounting, and integrates with our existing workflows.”

Way more specific. Way more emotional. Way more reflect of purchasing intent.

And if you know that one of your ICPs is “lawyers overwhelmed by technology,” then you absolutely need your brand to appear in ChatGPT when someone describes that exact scenario.

But how do you know what they’re typing into ChatGPT in the first place?

That’s where voice-of-customer (VOC) data comes in.

How VOC data helps you determine what prompts will drive sales

If you want to understand what your audience types into AI search, don’t guess — listen.

Pull real VOC data from:

• Sales call transcripts
  
• Customer reviews
  
• Case studies
  
• Support tickets
  
• Discovery call notes
  
• Anywhere buyers describe their problems in their own words
  

Then, feed that data into ChatGPT with a simple instruction:

“List the problem AND the trigger — the moment they decided they needed to solve it — for each lead. Use their exact wording.”

This surfaces the emotional language buyers use when they decide to act:
“overwhelmed,” “hate,” “tired of,” “fed up,” “frustrated,” “can’t keep doing this.”

Cluster them. Sort them. Find the patterns.

Then map each one to:

1. A pain point
2. A natural AI-style prompt someone would type
3. A content angle an LLM is likely to cite

This becomes the foundation for content that actually attracts motivated buyers from ChatGPT, Perplexity, Gemini, and whatever else shows up next quarter.

What this actually looks like 

Let’s imagine a SaaS company called FlowTrack, a workflow automation tool for sales teams.

From a sales call transcript, a qualified lead said:

“We want our reps focused on closing deals — not wasting time chasing documents and reminders. We looked at bigger CRMs for help, but their automation doesn’t actually handle this part well.”

This single quote contains:

• Problem: reps are wasting time on manual follow-up
  
• Job-to-be-done: reduce rep busywork
  
• Trigger: evaluated other tools, discovered they don’t solve it
  

From that, you can create a prompt hypothesis:

“What are the best workflow automation tools that integrate with major CRMs and remove manual follow-up for sales teams?”

Then you create high-quality guest content for authoritative websites already being cited in AI search for related queries.

Inside each article, you:

• Explain the pain point factually
• Show how FlowTrack solves it (objectively and with proof)
• Include a natural, honest brand mention
• Answer the broader workflow question thoroughly

When you pair…

• A trusted publication
• A non-spammy, well-researched article
• Clear contextual cues
• Tight alignment with real buyer language
  

…LLMs reward it.

That’s how brands start getting cited for meaningful prompts within weeks — because the model finally has credible, context-rich information to pull from.

Why it helps brands more than the usual ‘listicle’ approach 

Most teams chasing AI visibility are leaning on the same shortcut: get mentioned in authoritative listicles.

And to be fair — it works right now.

High-authority listicles dominate AI citations because they get strong traffic, follow a format LLMs can easily parse, and are published on trusted domains. Brands can also buy or pitch their way into them, making quick visibility possible.

But everyone is doing it.

Listicles are crowding fast, categories are starting to look identical, and editors are overwhelmed with pitches. As LLMs evolve or begin discounting repetitive formats, the value of listicles will drop quickly.

Add in the fact that many placements are paid, journalist access is tightening, and low-quality sites are copying the format — and you get a familiar pattern:

A race to the middle.

Listicles will stay useful in the short term, but they’re not a sustainable moat — especially for startups or emerging products trying to stand out in AI search.

Our prediction: the long-tail is where the moat will be

Right now, the ‘listicle guest post’ tactic works insanely well.

But more brands are catching on.

More guest posts.

More publications.

More SaaS companies chasing the exact same prompts:

• “best <category> software”
  
• “top tools for X”
  
• “alternatives to <competitor>”
  

These will get crowded — quickly.

So here’s where the real advantage is shifting:

Long-tail prompts driven by VOC:

The exact triggers and scenarios your buyers describe:

Examples:

“I’m drowning in manual follow-ups. What’s the easiest tool for CRM automation that won’t break our current setup?”

“We generate thousands of cold or aging insurance leads — what’s the fastest way to re-engage them without hiring more reps?”

“I need onboarding automation for manufacturing software that keeps everything in one place instead of five systems.”

These prompts are:

• Highly specific
• Lower competition
• Higher intent
• Better converters than anything on Google

The companies who win at AI search won’t chase obvious prompts.

Instead of following the easy hacks, they will: 

• Mine VOC data deeply
• Map pain → trigger → prompt → content
• Create assets LLMs actually trust and cite
  

Balance is key today (including listicles). 

Long-tail will be the moat tomorrow.

If you want help identifying the triggers your buyers are already typing into ChatGPT — or getting your product cited in those answers within weeks — you know where to find me.