AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's something that's going to sound crazy: I've spent the last two years obsessing over voice search optimization, and guess what? It's basically dead.
Not because people stopped talking to their devices, but because something way more powerful took over. While everyone was busy optimizing for "Hey Google" and "Alexa," ChatGPT and Claude quietly became the new voice search. Think about it - when was the last time you asked Siri something complex versus typing it into ChatGPT?
The shift happened so fast that most businesses are still playing catch-up. They're optimizing for traditional voice queries while their customers are having full conversations with AI assistants about their problems.
Here's what you'll learn from my experience diving deep into this shift:
Why traditional voice search optimization is now a waste of time
How ChatGPT and AI assistants actually "rank" content (spoiler: it's not keywords)
The content structure that gets you mentioned in AI responses
Real examples of what works (and what doesn't) for AI-driven visibility
How to track and measure your success in the new landscape
This isn't about gaming another algorithm - it's about understanding how people actually search for solutions now.
Industry Reality
What the SEO world is still teaching
Walk into any SEO conference or read the latest "voice search optimization" guide, and you'll hear the same tired advice:
Focus on long-tail conversational keywords. They tell you to optimize for phrases like "What's the best project management software for small teams?" because that's how people talk to Alexa.
Create FAQ-style content. Every SEO guru recommends structuring your content as questions and answers, assuming voice assistants will read your FAQ section verbatim.
Optimize for featured snippets. The theory goes that if Google shows your content in position zero, voice assistants will read it aloud.
Use schema markup religiously. Mark up every piece of content with structured data so voice assistants can understand it better.
Target "near me" and local intent. Since many voice searches are location-based, the advice is to double down on local SEO.
This conventional wisdom exists because it worked... three years ago. When voice search was primarily about quick facts and local business lookups. When Siri would just read the first Google result out loud.
But here's where it falls short: People aren't using voice search the same way anymore. They're not asking their smart speakers for business recommendations. They're having detailed conversations with ChatGPT about their specific problems, asking follow-up questions, and getting personalized advice.
The SEO world is optimizing for the old game while a completely new game has started.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
About six months ago, I was working with a B2B SaaS client who was frustrated with their voice search strategy. They'd spent months optimizing for conversational keywords, had perfect FAQ sections, and their featured snippet game was strong. But they weren't seeing any meaningful traffic or conversions from voice search.
That's when I started paying attention to my own behavior. I realized I hadn't used "Hey Google" for anything meaningful in months. Instead, I was constantly asking ChatGPT things like "Help me compare project management tools for a 20-person remote team that needs time tracking and client portals."
The conversation would continue: "What about pricing?" "How's the learning curve?" "Any integrations with Slack?" This wasn't voice search - it was AI consultation.
I started tracking this pattern with other people. Colleagues, clients, even my non-tech friends. Everyone was doing the same thing. We'd moved from asking devices simple questions to having complex problem-solving conversations with AI.
That's when it hit me: The future of "voice search" isn't about ranking for voice queries. It's about being the source that AI assistants recommend when people ask for help.
So I started experimenting. Instead of optimizing for traditional voice search, I began focusing on what I call "AI mention optimization" - creating content that ChatGPT, Claude, and other AI assistants would naturally reference when people asked relevant questions.
Here's my playbook
What I ended up doing and the results.
After realizing traditional voice search was dead, I developed what I call the "AI Mention Strategy." It's completely different from anything you'll read in SEO guides.
Step 1: Think Problems, Not Keywords
Instead of optimizing for "best CRM software," I started creating content around specific problem scenarios: "How to manage customer data when your team is growing from 10 to 50 people and spreadsheets aren't cutting it anymore."
AI assistants love context. They don't just match keywords - they understand situations. So I started writing content that addressed complete scenarios with all the nuance and complexity that real people face.
Step 2: Create Comprehensive, Standalone Sections
Here's something I discovered: AI assistants don't just pull featured snippets. They synthesize information from multiple sources. But they need each section of your content to be self-contained and complete.
I restructured content so each section could answer a specific question entirely on its own. Not just the headline answer, but the reasoning, the context, and the implications.
Step 3: Focus on Authority Through Specificity
Generic advice gets ignored by AI. But specific, detailed insights get referenced. Instead of saying "Email marketing is important," I'd write "For SaaS companies with 100-500 customers, triggered emails based on usage patterns convert 3x better than monthly newsletters because..."
The specificity signals expertise to AI systems. They're more likely to reference content that demonstrates deep, practical knowledge.
Step 4: Write for the Follow-Up Question
This was the game-changer. In traditional SEO, you optimize for the main query. In AI mention optimization, you also answer the obvious follow-up questions within the same content.
If someone asks about project management tools, they'll inevitably ask about pricing, implementation time, team size suitability, and integration options. I started including all of that in comprehensive guides.
Content Structure
Each section must be complete and contextual - no fragments or partial answers
Specificity Signals
Detailed insights with numbers and scenarios perform better than generic advice
Follow-Up Integration
Answer the obvious next questions within the same content piece
Authority Markers
Include reasoning and implications - not just the what but the why
The results have been interesting, though they're harder to track than traditional SEO metrics. You can't exactly monitor "ChatGPT rankings." But there are signals:
Increased Referral Traffic with Unusual Patterns: I started seeing traffic spikes that didn't correlate with Google rankings or social media. People were clearly finding the content through some other discovery method.
Higher Quality Leads: The people who found us through these methods were much more qualified. They'd already done their research, understood their problems clearly, and were ready for specific solutions.
More Complex Inquiries: Instead of generic "tell me about your services" emails, we got detailed questions that showed people had absorbed substantial information before reaching out.
The most telling indicator: when I started asking ChatGPT questions related to our expertise areas, our content began appearing in the responses. Not every time, but frequently enough to notice the pattern.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here's what I learned about the death of traditional voice search and the rise of AI consultation:
Context beats keywords every time. AI assistants understand situations and problems, not just search terms. Write for complete scenarios, not keyword density.
Depth signals authority. Shallow content gets ignored. AI systems seem to favor sources that demonstrate comprehensive understanding of topics.
Conversation structure works. Content that flows like a natural consultation - anticipating questions and providing complete answers - performs better.
You can't optimize for AI like you optimize for Google. There's no keyword research tool for ChatGPT mentions. It's about creating genuinely useful content that AI would naturally want to reference.
Traditional voice search metrics are meaningless now. Stop tracking featured snippets and start paying attention to referral patterns and lead quality.
The shift happened quietly but completely. Most businesses are still optimizing for 2020's voice search while their customers have moved on to AI conversations.
This is about trust, not tricks. AI assistants recommend sources they "trust" - meaning content that consistently provides accurate, helpful information.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to adapt:
Create problem-scenario content instead of feature lists
Answer implementation questions in product descriptions
Include specific use cases with team sizes and contexts
For your Ecommerce store
For ecommerce stores adapting to AI-driven discovery:
Write detailed buying guides with specific use cases
Include sizing, compatibility, and comparison information
Address common concerns and questions in product content