Been in this space long Been in this space long enough to see what actually moves the needle vs. what just looks good in a pitch deck.
What's working
- Prompt monitoring is real. Knowing which queries trigger your brand in AI responses gives you actual signal to act on.
- Citation source tracking matters more than most people realize. If you know which pages AI engines pull from, you know exactly what to fix.
- The SEO + AEO combo is the biggest unlock right now. Your traditional SEO foundation directly affects how AI engines cite you. They're not separate strategies anymore, and the brands treating them as one are pulling ahead fast.
What's overhyped
"AI rank tracking" is mostly noise. There's no stable rank. Every response shifts based on user, context, and model version. Any tool selling you a fixed rank number is oversimplifying.
Content volume as a fix doesn't work. Publishing more doesn't make AI trust you more. Authority, structure, and citations do.enough to see what actually moves the needle vs. what just looks good in a pitch deck.
What's still missing
There's no stable playbook yet. Every major LLM update shifts how citations and mentions work. What gets you cited in ChatGPT this month might not work in Gemini next month. The ranking logic, the citation triggers, the mention patterns - they're all moving targets with zero transparency.
This means even the best tools can only show you a snapshot. The underlying algorithms change faster than any dashboard can keep up with.
Honestly, the most reliable method right now is still just testing a lot. Run more prompts, across more models, more often. See what sticks. There's no shortcut around it yet.
Tools like Visby AI are starting to help close the action gap - turning what you find into actual tasks instead of just data. But even then, the testing loop is something you can't fully automate away.