The landscape of product discovery is shifting beneath our feet. While companies pour resources into SEO, social media marketing, and traditional advertising, a quiet revolution is taking place: AI systems are becoming the first customers to “meet” your product.
The New Front Door
LLMs, AI assistants, and search agents are increasingly serving as intermediaries between your product and potential customers. When someone asks ChatGPT for restaurant recommendations, queries Claude about software tools, or uses an AI shopping assistant, these systems make split-second decisions about whether to surface your product. The question is: will they understand what you’re offering well enough to recommend it?
Why AIO Might Be Different From SEO – For Now
SEO was always at Google’s whim. Algorithm updates tanked traffic overnight. The entire industry was built around gaming a single gatekeeper who kept their ranking factors opaque and monetised every query. It was painful and I moved away from SEO as a service offering back in the day when I was running my agency – I preferred product-led outcomes so I’m welcoming what’s in store here (for now!)
AI optimisation is currently more about actual clarity. AI systems try to give accurate, helpful answers based on genuine fit. The “optimisation” is just making your product understandable.
This won’t last forever. As AI platforms monetise, we’ll see paid placements, partnership deals, and sophisticated gaming tactics. The question is: how long do we have?
Why It Still Matters More
Even acknowledging future corruption, there are reasons for optimism:
- Better alignment: Clarity helps both AI and humans. No keyword stuffing required.
- Multiple platforms: Claude, ChatGPT, Perplexity – competition might keep things honest longer.
- Immediate feedback: Users can instantly test if recommendations fit. Bad suggestions get caught faster.
- Different models: Not all AI platforms are ad-based. Subscription models have less incentive to corrupt recommendations.
That said, biases already exist. AI systems favour well-documented products, established brands, and English-language content. The playing field isn’t level.
Three Principles for AI-Readable Products
1. Clarity Over Cleverness
“Revolutionising collaboration” means nothing. Try: “Real-time document collaboration with video chat and version control for remote teams of 10-100 people.”
2. Structured Data
FAQs, feature lists, comparison tables, transparent pricing. These help AI and humans alike.
3. Comprehensive Context
Problems solved, use cases, outcomes. Case studies and testimonials give AI systems the full picture.
What To Do
- Audit: Can an AI accurately explain what you do? Apply the grunt test: Show your homepage to someone for 5 seconds. Can they answer the three questions? If not, fix it.
- Build foundations: Clear documentation serves every channel
- Test regularly: Ask AI assistants to describe your product
- Stay balanced: Don’t abandon other channels
The Honest Assessment
AI optimisation isn’t magic. It has biases, can be gamed, and will likely get commercialised. Well-funded companies still have advantages.
But right now, the incentives are better aligned than they’ve been in decades. Clarity helps. Structure serves users. Comprehensive information wins recommendations.
This might be temporary. But it’s also a genuine window where product-led companies can win on actual merit rather than who’s best at gaming algorithms.
Invest in clarity. Make your product understandable. These tactics work regardless of how discovery evolves. And whilst AI systems are still trying to be helpful rather than extracting maximum revenue, you might actually win on what you’ve built.