Aisha Amaira is a MarTech expert with a profound focus on the intersection of customer data and emerging technologies. With an extensive background in CRM systems and customer data platforms, she specializes in translating complex technical shifts into actionable business growth. Her current work focuses on helping brands evolve their digital presence to remain visible and authoritative as traditional search engines transition into AI-driven discovery engines.
The following discussion explores the strategic shift from traditional SEO to AI Engine Optimization (AEO). Aisha breaks down how to identify core business challenges, the structural changes required for modern content, and how to navigate the “zero-click” environment where AI interfaces act as the primary gatekeepers of information.
Many teams prioritize adding structured data or chasing citations in ChatGPT, yet these efforts often fail when platforms shift. How do you distinguish a genuine business challenge from a mere channel problem, and what steps should a team take to ensure their strategy outlasts a single algorithm update?
The trap most teams fall into is treating AI search as a checklist of technical fixes rather than a shift in consumer behavior. A channel problem sounds like “we aren’t ranking in Perplexity,” whereas a genuine business challenge is “our brand is invisible during the research phase, allowing competitors to own the buyer’s mindshare before they ever visit a site.” To ensure a strategy survives the next update, you must anchor it in revenue-connected problems, such as brand visibility erosion or pipeline protection. We use a three-part framework: define the core challenge in one sentence, develop a unique approach that only your brand can execute, and only then list the tactics. By focusing on the “why” instead of the “where,” your team stays focused on the business outcome even if a specific LLM changes its crawling patterns overnight.
Research suggests that nearly half of AI citations are pulled from the initial 30% of a webpage. What are the practical trade-offs of “front-loading” answers over providing deep context, and how does this structural shift specifically impact the way you rewrite high-performing legacy content?
Data from “The Science of How AI Pays Attention” reveals that 44% of citations are pulled from that first 30% of the page, which creates a significant “ski-ramp” effect for visibility. The trade-off is moving away from the traditional “narrative build” where the conclusion sits at the bottom, and instead adopting an “inverted pyramid” style that leads with hard claims, definitions, and data. When we look at high-performing legacy content, the process involves a “front-loading” audit: we rewrite the first three paragraphs to provide the direct answer immediately. This might feel like you are sacrificing user dwell time, but in an AI-first world, if the model doesn’t find the answer in those opening lines, it likely won’t cite you at all, meaning the user never arrives in the first place. It is a shift from writing for “the read” to writing for “the citation.”
Users often research within AI interfaces before ever reaching a website, making traditional analytics less effective for tracking intent. How do you identify which specific queries are actually driving your revenue pipeline in a zero-click environment, and what methods do you use to map these to real-world behaviors?
In a zero-click environment, traditional click-through rates become secondary to influence tracking. We identify revenue-driving queries by starting with the “pain points” the sales team hears on actual calls and turning those into the specific questions buyers ask ChatGPT or Gemini. We then analyze 250 sessions of real AI Mode behavior to see where the brand appears—or fails to appear—in those conversational paths. By mapping these “revenue-connected” queries, we can see if we are actually part of the consideration set during the pre-site journey. This requires looking at referral data and customer surveys rather than just counting sessions, as the value lies in being the “authoritative source” the AI uses to answer a buyer’s query.
AI models frequently cite third-party platforms like Reddit or G2 to define market categories and recommend solutions. How can a brand effectively leverage executive expertise and community signals to influence these external citations, and what metrics best demonstrate the impact of “authority multiplication” on market share?
We call this “authority multiplication,” and it’s about moving beyond your own domain to seed the entire ecosystem that LLMs crawl. A brand can influence these citations by getting executives on high-authority podcasts and securing strategic bylines in publications that AI models treat as foundational knowledge. Additionally, amplifying community signals through case studies and user-generated content on sites like G2 ensures that when an AI looks for “the best solution for X,” it sees a consistent pattern of third-party validation. Success isn’t measured by a single ranking, but by “citation share” across platforms like AirOps or SearchGPT. When you see a 40-60% increase in citations over six months, you can correlate that to the 15-20% of assisted conversions that now happen within the AI interface itself.
Traditional traffic forecasts are increasingly unreliable in a landscape dominated by AI Overviews and conversational bots. When presenting to leadership, how do you structure scenario planning to justify investment, and what specific “stage gates” should be included to make these experimental budgets reversible?
Presenting a fixed traffic forecast today is essentially presenting fiction, so I advise shifting to scenario planning—conservative, moderate, and aggressive. For example, a conservative plan might allocate 30% of capacity to authority building to protect a specific percentage of assisted conversions. To make these budgets palatable for executives, we build in “stage gates,” which are specific decision points at the three or six-month mark where we evaluate if the “bet” is paying off. This makes the investment reversible and less risky because you aren’t asking for an open-ended commitment; you are asking for a timed experiment with clear metrics like citation baseline growth or entity reinforcement. It transforms the conversation from “give us money for SEO” to “here is a calculated investment in market share protection.”
A robust strategy typically breaks down into a core challenge, a unique approach, and specific actions. How do you ensure your chosen approach is something only your brand can execute, and what is the process for re-evaluating these actions during a quarterly review as platform capabilities evolve?
Your approach must leverage assets your competitors can’t easily replicate, such as product-led content derived from your own proprietary data or the unique expertise of your founders. If your strategy is just “write more blogs,” anyone can copy that; if it’s “using our internal database of 35,000 data points to define the industry benchmark,” you’ve built a moat. During our quarterly reviews, we ask four critical questions: what changed in AI search, what did our tests teach us, do our tactics still serve the approach, and is the approach still solving the core business challenge? This rhythm ensures we don’t get stuck in a “task list” mentality while the technology is moving at such a high velocity. It allows us to pivot the “actions” while keeping the “challenge” and “approach” stable.
What is your forecast for AI SEO?
My forecast is that AI SEO will move away from “optimizing for keywords” and toward “optimizing for entities and influence.” We are entering an era where the most successful brands won’t necessarily have the most traffic, but they will have the highest “mention share” within the models that people trust to make decisions for them. We will see a massive consolidation of content, where “less is more”—a concept I call “SEOzempic”—focusing on high-authority, deeply targeted pages rather than thousands of thin, low-value blog posts. Ultimately, the winners will be those who treat LLMs as a new type of customer who needs to be educated with clear, structured, and authoritative data so that it can, in turn, recommend the brand to the human user.