AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's something that'll make you question everything you've heard about voice search optimization: while everyone was busy optimizing for "Hey Alexa" and "OK Google," I discovered something way more interesting working with AI-powered platforms.
Last year, while helping an e-commerce client scale their content to 20,000+ pages across 8 languages using AI, I stumbled into what I now call the "real voice search opportunity" - and it has nothing to do with traditional voice assistants.
The truth? Traditional voice search optimization is chasing yesterday's technology. While marketers are still debating whether to optimize for "pizza near me" or "where can I find pizza nearby," a completely different shift is happening that most people are missing.
After experimenting with AI content generation at scale and watching how modern search behavior actually works, I realized we've been solving the wrong problem. Here's what you'll learn from my experience:
Why traditional voice search tactics are becoming irrelevant
How conversational AI platforms are creating the real "voice search" opportunity
My framework for optimizing content for AI-powered discovery
The specific tactics that actually move the needle in 2025
How to build content that gets cited by AI systems without gaming the algorithm
If you're still thinking about voice search as "Siri optimization," this is going to change your entire approach to AI content strategy.
Industry Reality
What every marketer thinks they know about voice search
Walk into any marketing conference and you'll hear the same voice search advice repeated like gospel. The industry has been preaching the same playbook for years:
"Optimize for natural language queries." Everyone tells you to target long-tail keywords that sound like how people actually speak. "Best Italian restaurant downtown" instead of "Italian restaurant."
"Focus on local search intent." The conventional wisdom says voice search is primarily mobile and location-based, so you should optimize for "near me" queries and local SEO.
"Create FAQ-style content." Structure your content as questions and answers because that's supposedly how people interact with voice assistants.
"Target featured snippets." The theory is that voice assistants pull answers from position zero, so ranking for featured snippets equals voice search success.
"Optimize for conversational keywords." Use tools to find question-based keywords and create content around "how," "what," "where," and "why" queries.
This advice exists because it made sense in 2018 when Alexa and Google Home were the hot new thing. The SEO community extrapolated from limited data and created a framework that felt logical.
But here's the problem: this entire approach is based on outdated assumptions about how people actually use voice technology. While everyone was optimizing for smart speakers, the real voice search revolution was happening somewhere completely different.
The shift I discovered working with AI-powered content systems revealed that we've been chasing the wrong target entirely.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came while working on that massive AI content project for a Shopify e-commerce client. We had generated over 20,000 SEO-optimized pages across 8 languages, and I was deep in the data trying to understand which content performed best.
That's when I noticed something weird in the analytics. We were getting traffic from sources that didn't make sense - referrals that looked like they came from AI systems, not traditional search engines.
Around the same time, I was experimenting with different AI research tools for my own keyword strategy work. I'd abandoned the expensive SEO tool subscriptions and started using Perplexity Pro for research. The results were so much better that I rebuilt my entire keyword research process around it.
But here's what clicked: I wasn't just using Perplexity differently - I was searching differently. Instead of typing "best project management software," I was having actual conversations: "I need a project management tool for a remote team of 8 people, mostly designers, with integrations to Figma and Slack, under $500/month."
That's when it hit me. The real "voice search" isn't happening on Alexa. It's happening on conversational AI platforms where people are asking complex, nuanced questions and getting sophisticated answers.
I started tracking mentions of our clients' content across different AI platforms - ChatGPT, Claude, Perplexity, and others. Even in traditional e-commerce niches where you wouldn't expect LLM usage, we were seeing dozens of mentions per month.
This wasn't traditional voice search optimization paying off. This was AI systems naturally citing our content because it was genuinely useful and well-structured, not because we'd gamed some voice search algorithm that barely anyone was using.
Here's my playbook
What I ended up doing and the results.
Once I realized where the real opportunity was, I completely restructured how I approach content optimization. This isn't about gaming AI systems - it's about creating content that naturally aligns with how AI platforms process and cite information.
The foundation: Chunk-level thinking
Traditional SEO thinks in pages. AI systems think in passages. I started restructuring content so each section could stand alone as a valuable snippet. Instead of one long article about "project management," I'd create sections that each answered specific questions: "How to onboard remote team members," "Setting up project workflows for designers," "Integrating design tools with project management."
Each chunk needed to be self-contained but still flow naturally within the larger piece. The magic happens when AI systems can extract any section and it still makes complete sense.
Answer synthesis optimization
AI systems don't just pull quotes - they synthesize information from multiple sources. I started organizing content with logical structure that made synthesis easy. Clear headings, numbered steps, and obvious cause-effect relationships.
For my e-commerce client, instead of generic product descriptions, we created content that explained the "why" behind each product feature. "This material is waterproof because..." "The battery lasts 48 hours, which means..." AI systems love context and reasoning.
Citation-worthy authority building
AI systems cite sources they trust. I focused on creating content with clear expertise signals: specific metrics, detailed processes, admitted limitations, and links to authoritative sources. The goal wasn't to sound smart - it was to sound reliable.
I also started paying attention to how information was attributed. Instead of vague claims like "studies show," I'd include specific sources: "According to Shopify's 2024 Commerce Report..." AI systems can verify these claims, making the content more citation-worthy.
Multi-modal content integration
While traditional voice search ignored visuals, AI systems process everything. I integrated charts, tables, and structured data not just for SEO, but because AI systems use these elements to understand content better.
For the Shopify client, product comparison tables weren't just for users - they helped AI systems understand relationships between products and make better recommendations.
The compound effect
The breakthrough came when I realized this approach worked across all channels. Content optimized for AI citation also performed better in traditional search, social sharing, and email marketing. We weren't just optimizing for one platform - we were creating fundamentally better content.
Content Structure
Organizing information for AI comprehension and synthesis
Context Depth
Providing the ""why"" behind every claim and recommendation
Authority Signals
Building trust through specific metrics and verifiable sources
Multi-Platform Thinking
Creating content that works across AI systems and traditional channels
The results spoke for themselves, but not in the way I expected. While traditional voice search metrics remained basically flat (because, honestly, who's really using Alexa for research?), we saw massive improvements in areas that actually matter for business.
AI platform mentions increased dramatically. For the e-commerce client, we went from occasional mentions to dozens per month across different AI systems. More importantly, these mentions were contextually relevant and drove qualified traffic.
The content performed better across all channels. AI-optimized content naturally scored higher in traditional search because it was more comprehensive and better structured. Social shares increased because the content was more scannable and quotable.
But the biggest win was efficiency. Instead of creating separate content for voice search, traditional SEO, and social media, we had one content creation process that worked everywhere.
Traffic quality improved significantly. People finding our content through AI platforms were already pre-qualified and engaged. They'd often arrive with specific questions and convert at higher rates than traditional search traffic.
The compound effect was undeniable. Better content structure led to better user experience, which led to better engagement metrics, which led to better search rankings. It was a virtuous cycle that traditional voice search optimization could never create.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this approach across multiple clients and seeing consistent results, here are the lessons that matter:
Stop chasing voice assistants, start thinking conversational AI. The real opportunity isn't optimizing for "Hey Alexa" - it's creating content that AI systems naturally want to cite and reference.
Quality beats optimization every time. AI systems are sophisticated enough to recognize genuinely useful content. Gaming tactics that worked in early SEO don't work here.
Structure is more important than keywords. How you organize information matters more than which specific phrases you target. AI systems understand context and relationships, not just keyword density.
Expertise signals are crucial. AI systems cite sources they can verify and trust. Specific metrics, clear sources, and admitted limitations build credibility.
One strategy, multiple benefits. Content optimized for AI citation naturally performs better across all channels. You're not adding work - you're doing better work.
Traditional voice search metrics are vanity metrics. Focus on AI platform mentions, content synthesis, and qualified traffic rather than smart speaker queries.
The best defense is great offense. Instead of trying to game AI systems, create content so useful that AI systems want to reference it. Build authority, don't chase algorithms.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement this approach:
Create detailed use-case content that explains the "why" behind each feature
Structure documentation so each section can stand alone as a complete answer
Include specific metrics and case studies that AI systems can verify
Focus on problem-solution content rather than feature lists
For your Ecommerce store
For e-commerce stores implementing this strategy:
Create product content that explains context and use cases, not just specifications
Structure comparison content that helps AI systems understand product relationships
Include buying guides that address specific customer scenarios
Optimize for conversion-focused content that AI systems want to recommend