As a leading MarTech expert, Aisha Amaira has built a career at the intersection of marketing, technology, and customer data. She has a unique vantage point on the seismic shifts occurring in digital discovery, where traditional search is giving way to a complex ecosystem of AI-driven answer engines. In this conversation, Aisha unpacks the urgent challenges and opportunities facing executives and marketers, exploring how to build trust with machines, redesign content for a zero-click world, and prepare for a future where AI anticipates our needs by analyzing the choices we don’t make.
The landscape of digital discovery is becoming incredibly fragmented, with various AI answer engines often providing conflicting information. For an executive feeling adrift in this uncertainty, what’s the first step toward gaining control, and how does that translate into a concrete new workflow for their SEO teams?
It begins with a fundamental mindset shift at the executive level. You have to stop thinking about a single front door to your business and start seeing the dozen new ones that just opened. With AI engines disagreeing with each other 62% of the time, as BrightEdge found, your brand’s visibility has become inherently unstable. The first step for an executive is to demand reporting that tracks “answer presence.” It’s no longer about where you rank; it’s about how often you appear, are cited, or are paraphrased inside ChatGPT, Perplexity, Gemini, and the others. For the SEO, this translates into a completely new operational rhythm. Their workflow is no longer just about keyword research and link building. It’s about deconstructing content into its smallest, most retrievable chunks. They need to be running monthly audits, asking, “How strong is this paragraph’s embedding? Is this specific definition being pulled into answers? Are we being cited correctly?” It becomes a game of evaluating content on a micro-level across a dozen different platforms, optimizing not for a single algorithm but for a chorus of disagreeing models.
You mentioned that content formatting is evolving into a direct ranking signal for machines. Could you elaborate on what “designing for machine retrieval” looks like in practice, moving beyond basic elements like H2s and bullet points?
Absolutely. Think about it from the machine’s perspective. It’s not reading for pleasure; it’s parsing for facts and relationships. A machine craves predictability and structure because that’s what makes information easy to embed and trust. Microsoft’s study of over 200,000 work sessions showed that the most common AI tasks were gathering, explaining, and rewriting information. Your content has to be a perfect feedstock for those tasks. Let’s say you have an article about a complex topic. To redesign it for a machine, you’d start by creating an explicit definition block right at the top, almost like a glossary entry. Then, for each key concept, you would use consistent structural patterns—perhaps a “Key Takeaways” box followed by a short Q&A section answering the most predictable follow-up questions. You would ensure that every entity, like a product name or a feature, is always referred to with the exact same terminology. This isn’t just about clean HTML; it’s about creating a semantic consistency that gives the model a high confidence score, making your content a reliable, go-to source it will favor over less organized competitors.
With the rise of on-device AI and wearables, we’re seeing a surge in private, contextual “micro-queries.” How does this fundamentally change the way we should think about content creation, especially when the goal is no longer to earn a click?
This is a critical pivot. The age of relying solely on the 2,000-word blog post to capture attention is fading. Those long-form pieces still have a role, but they aren’t optimized for a person asking their Meta Ray-Bans, “How do I fix this leak under my sink?” That query demands a lightweight, immediate, and self-contained answer. Content designed for edge device retrieval looks more like a stack of atomic cards than a long document. Each “card” might be a single definition, a two-sentence explanation, or a three-step instruction set, all meticulously tagged with structured metadata. The real challenge here is measurement. If a successful interaction means your content was used to generate an answer on a device screen without a visit, your traditional analytics are useless. The new metrics become about influence, not traffic. You’d measure your citation rate in answer engines, your share of voice for specific conversational queries, or the frequency your product images are used in visual search results. Success is being the trusted source for the AI, even if the end-user never knows your name.
The concept of authority seems to be shifting from human-centric signals like backlinks to machine-evaluated trust. What practical steps can a business take to build this new kind of authority, and can you give an example of how a simple inconsistency could undermine it?
Building machine-measured authority is all about creating an airtight, internally consistent universe of information. A machine establishes trust by verifying facts and observing patterns. The most powerful tool for this is a knowledge graph, which essentially maps out all the important entities related to your business—your products, services, people, locations—and defines the relationships between them. This becomes the source of truth that your content constantly reinforces. An SEO’s job shifts toward ensuring semantic coherence across every single touchpoint. For example, imagine a software company that calls its core feature a “Dynamic Dashboard” on its homepage, but the support documents refer to it as the “Analytics Hub,” and a press release calls it the “Insight Engine.” A human might figure it out, but a machine sees three different entities. This inconsistency erodes its confidence. It can’t be sure what the feature is, what it does, or how it relates to other products. As a result, when an AI like Perplexity is assembling an answer, it will favor a competitor with clear, stable terminology, because that competitor is simply a more reliable source.
The idea of “agent-to-agent commerce” is fascinating, where our content essentially becomes an instruction manual for AI assistants. For a local service business, like a plumber, what would that unambiguous, machine-readable content actually look like?
For an AI agent to confidently book a plumber, it needs to operate with zero ambiguity. It can’t “guess” or “assume.” The plumber’s website would need to transform from a marketing brochure into a structured data feed. Think less about persuasive copy and more about a clear, logical API for their services. This would mean providing explicit, machine-readable data on their service area, not as a sentence like “serving the greater metro area,” but as a list of ZIP codes. Pricing couldn’t be “call for a quote”; it would have to be a set of clear rules: a flat fee for diagnostics, an hourly rate for labor with defined start/end triggers, and a specific surcharge for emergency after-hours calls. Most importantly, availability would have to be tied to a real-time calendar API. The AI agent’s decision tree is binary: “Is the plumber available at 3 PM on Tuesday? Yes/No. Is the customer’s address within the defined service area? Yes/No.” If your information is vague, the agent will simply move on to the next provider who offers the certainty it needs to complete its task.
If zero-click environments like ChatGPT and Gemini are our new primary competitors, the traditional marketing dashboard becomes obsolete. What new key performance indicators should a CMO be laser-focused on to measure success in this new reality?
The CMO’s dashboard needs a complete overhaul. Vanity metrics like “organic traffic” and “keyword rankings” are becoming lagging indicators of influence, not leading ones. The new North Star metric is what I call “Answer Presence.” This single KPI would measure the percentage of time your brand is cited, mentioned, or used as a primary source across the top five AI answer engines for your most critical commercial queries. Subsidiary metrics would include “Share of Answer”—how much of the synthesized response is based on your content—and “Sentiment Analysis” of how your brand is described by the AI. We had a client in the finance space who saw their web traffic stagnate but noticed in their manual audits that their clear, concise definitions for complex financial terms were being used almost verbatim in AI answers. They doubled down on creating this “machine-friendly” content, and while their traffic didn’t spike, their inbound lead quality from users who did click through improved dramatically. They were winning influence before the search even began, effectively becoming the default dictionary for their niche.
Your prediction about “Latent Choice Signals” is a powerful one—the idea that AI will learn from our avoidance and hesitation. What are some of the subtle, early indicators of this “cognitive friction” that businesses can start looking for today on their own properties?
This is the invisible force that will begin to shape discovery, and the signals are already present, hiding in plain sight within our existing analytics. Cognitive friction is the hesitation a user feels but never articulates. Think about a product page where users consistently hover their mouse over the pricing section for several seconds before leaving the page—that’s a hesitation signal. Or a sign-up form where a high percentage of users abandon it after reaching the field that asks for a phone number. The system can see these patterns at scale. A business can get ahead of this by looking for behavioral outliers. Analyze session recordings to see where users scroll up and down repeatedly, indicating confusion. Look for pages with high time-on-page but an extremely low conversion rate; people are reading but not gaining the confidence to act. These are all micro-indicators of friction. As AI gets more integrated into the OS, it won’t even need to be on your site to see this. It will see the user rephrase their query after getting a suggestion to visit your page or simply close the assistant. These are the silent rejections that will become the most powerful ranking factor of all.
What is your forecast for the future of digital discovery?
My forecast is that discovery will become almost entirely ambient and synthesized. The act of “searching” as a distinct, intentional session will feel archaic. Instead, discovery will be woven into the fabric of our operating systems, our wearables, and our daily conversations with assistants. We won’t go looking for answers; answers will be presented to us proactively based on context and these latent choice signals. For brands, this means the battlefield for visibility moves from the search results page to the model’s training data and retrieval preferences. The winners won’t be those with the best SEO tricks, but those who have become the most trusted, consistent, and frictionless source of information in their domain—so much so that the AI doesn’t just recommend them, it thinks with their data. The ultimate goal will be to become the foundational knowledge source that an AI uses to understand your entire industry.