Still optimizing for keywords in 2026? Here's why conversational queries are eating our lunch in AI search

You’re absolutely right, and here’s the technical reason why:

How AI processes queries differently:

Keyword search (traditional):

• Matches exact words
• Counts keyword frequency
• Checks keyword placement
• Limited understanding of meaning

Conversational search (AI):

• Uses NLP to understand intent
• Analyzes semantic meaning
• Considers context and follow-ups
• Matches meaning, not just words

Why this changes everything:

When someone asks ChatGPT “What’s the best CRM for a 10-person sales team with a limited budget?” the AI understands:

• They want a CRM
• For a small sales team
• With budget constraints

Then it looks for content that ANSWERS that question, not just content with those keywords scattered throughout.

The content that wins:

Content structured as Q&A, with conversational headings, that directly answers questions. AI systems can extract answers from this format much more easily.

Voice search has been training users for this for years.

The voice search connection:

When people talk to Alexa, Siri, or Google Assistant, they naturally use conversational language:

• “What’s the weather today?”
• “How do I fix a leaky faucet?”
• “What’s the best restaurant near me?”

Nobody says “weather today” or “leaky faucet fix” out loud. Voice search normalized conversational queries.

AI search is the next step:

Now users apply the same conversational behavior to ChatGPT, Perplexity, etc. They ask complete questions because that feels natural.

Content strategy implication:

Write your content as if you’re answering someone who asked you a question out loud. Because that’s literally how people are querying AI systems.

As someone who does keyword research for a living, I’ve had to evolve my approach.

Old keyword research:

• Find high-volume, low-difficulty keywords
• Group by topic clusters
• Target 1 keyword per page

New conversational research:

• Find the QUESTIONS people ask about topics
• Understand the follow-up questions
• Map the conversation flow

Tools that help:

• AnswerThePublic for question discovery
• Reddit/Quora for actual user questions
• AI platforms themselves (ask ChatGPT what questions people ask about X)

The new output:

Instead of a keyword list, I now deliver a “conversation map” that shows:

1. Primary question users ask
2. Follow-up questions
3. Related questions
4. Edge case questions

This gives content teams the framework for conversational content.

There’s a conversion angle to this too.

Conversational content converts better:

Users who find content through conversational AI queries are often further along in their decision-making. They asked a specific question because they have a specific need.

Our data:

Why it works:

Conversational queries express explicit intent. Someone asking “What’s the best project management tool for remote teams under $20/month?” knows exactly what they want. If your content answers that question and you meet the criteria, the conversion is natural.

Keyword traffic is often more exploratory. Conversational traffic is more intentional.

We’ve rebuilt our content process around conversational optimization.

The new content brief template:

1. Primary question (what users are asking)
2. Follow-up questions (what they ask next)
3. Answer outline (direct answers first, then detail)
4. Conversational headers (questions as H2s)
5. FAQ section (additional questions)

Example transformation:

Old: “CRM Integration Guide: Everything You Need to Know” New: “How Do I Integrate My CRM with Other Tools? A Step-by-Step Guide”

The new format explicitly answers the question users are asking.

Client results:

Clients who’ve adopted conversational content:

• 3.2x increase in AI citations
• 1.8x increase in featured snippets
• 2.4x increase in voice search visibility

The ROI is clear.

Technical writing perspective: conversational content is actually clearer.

Why conversational works better for humans too:

• Question-based headers help scanning
• Direct answers reduce cognitive load
• Natural language is easier to read
• Q&A format matches how people think

Before (keyword-optimized):

“CRM Software Features for Small Business Operations”

After (conversational):

“What CRM features do small businesses actually need?”

The conversational version is clearer about what the content covers. It’s better UX AND better for AI.

The myth:

Some people think conversational = dumbed down. It’s not. It’s clearer communication that respects the reader’s time.

Local search has always been conversational. Now it’s everywhere.

Local search example:

Nobody types “dentist Chicago downtown reviews” - they ask “Who’s the best dentist near downtown Chicago?”

Local SEO has optimized for this for years. Now general SEO needs to catch up.

What local SEO taught us:

1. Answer the specific question
2. Include location context naturally
3. Anticipate follow-ups (“Do they accept my insurance?”)
4. Use natural language throughout

These principles now apply to ALL search, not just local.

This thread has reinforced what we’re seeing and given us a clear path forward.

Key takeaways:

1. Conversational queries are the new normal for AI search
2. There’s no trade-off - conversational works for both AI and traditional search
3. The structure is: question header -> direct answer -> supporting detail
4. Keyword research evolves to “question research”
5. Voice search prepared users for conversational AI search

What we’re implementing:

1. Restructure existing content with question-based headers
2. Move to “conversation maps” instead of keyword lists
3. Use direct answers in the first sentence of each section
4. Create more Q&A and FAQ content
5. Track conversational query performance with Am I Cited

The bigger picture:

Keywords aren’t dead, but they’re no longer the primary organizing principle. User questions are. The brands that adapt to conversational-first content will dominate AI search.

Thanks everyone for the practical insights.