In the rapidly shifting world of digital marketing, many are discovering a frustrating new reality: the content that resonates most with their human audience is often completely invisible to AI-driven search engines. To unravel this paradox, we sat down with Aisha Amaira, a MarTech expert who specializes in the intersection of technology, marketing, and customer insight. Aisha’s work on customer data platforms and marketing technology gives her a unique perspective on how businesses must adapt to a world where discovery is increasingly mediated by AI.
This conversation explores the critical “Utility Gap”—the growing chasm between what people consider relevant and what AI models deem useful. We delve into why even factually perfect content can be ignored or, worse, actively harm an AI’s ability to generate a good answer. Aisha breaks down how to re-engineer content to avoid the “lost in the middle” problem, measure AI performance without expensive tools, and transition from simple content writing to the more disciplined practice of content engineering.
Many find their best content, which customers love, is completely invisible in AI answers. Why does this divergence between human relevance and model utility happen, and what is a key factor teams often misdiagnose when their content fails to appear in AI-generated results?
That feeling of seeing your best work vanish is a truly jarring experience, and it’s at the heart of what I call the Utility Gap. The core of the problem is that we’re still judging our content by a single, universal standard of “quality,” but that standard no longer exists. A human reads to understand; we tolerate a warm-up, we appreciate a good story, and we’ll happily scroll through a page to find the one paragraph that solves our problem. An AI system, on the other hand, doesn’t read. It retrieves chunks of information and looks for usable signals to complete a task. It doesn’t need your narrative, just the extractable facts. The biggest misdiagnosis I see is teams assuming that if content fails, it must be a writing or credibility problem. They’ll spend weeks polishing prose that’s already excellent for people, when the real issue is that the content isn’t structured to be useful for a machine.
Beyond just being ignored, some content can actively harm an AI’s answer quality. How can a well-written, factually correct page become a “distracting passage” for a model, and what does this mean for the way we should structure complex information like guides or tutorials?
This is one of the most counterintuitive aspects of the Utility Gap. A page can be perfectly accurate and well-written but still act as a “distracting passage” that pulls a model off-track. This often happens when we mix everything together in one dense block of text—the main guidance, the exceptions for edge cases, and maybe some product messaging. A human reader can navigate that complexity, but for a retrieval system, it’s just noise. That density increases the risk of the model latching onto the wrong detail. It means we have to be much more deliberate in how we structure information. For a tutorial, for instance, you should clearly separate the core guidance from the exceptions. State the main path upfront, then handle the edge cases in a distinct section. It’s about creating clean, unambiguous signals that don’t force the model to guess what’s most important.
Research shows models often struggle to use information placed in the middle of a document, even with long-context capabilities. How should this “lost in the middle” problem change content strategy, and can you share a specific example of how to re-engineer a page to fix it?
The “lost in the middle” phenomenon is a perfect example of how human intuition doesn’t map to model behavior. We assume if the answer is on the page, the model will find it, but research clearly shows that performance degrades sharply when key information is buried in the middle. This has to fundamentally change how we think about page structure. We can no longer afford to have a slow ramp-up. For instance, imagine a guide on choosing a software plan. The original version might have a nice narrative introduction, then discuss the features of several plans, and only in the middle does it state the most critical decision rule, like “The Pro plan is the only option for teams needing compliance-grade security.” That key constraint is now functionally invisible. To re-engineer it, you’d pull that statement right to the top, maybe even into a summary box, or repeat it in a tighter form near the beginning. You have to treat mid-page content as fragile and ensure decision-critical information is in a prime location.
We see AI platforms providing different paths for the same user intent, like one pushing to a marketplace and another to a directory. How does a model decide which path is most “useful” for a user, and what can a brand do when its high-quality content loses to a competitor’s framing?
This is where the Utility Gap shows up as real-world behavior and impacts the bottom line. Research on this has shown a 62% divergence in some industries. For a query like “how to find a doctor,” one AI might favor a marketplace like Zocdoc, while another points to hospital directories. The model isn’t making a judgment on “quality” in the way we do; it’s selecting what it determines is the most efficient path for task completion. A directory or aggregator might be seen as more useful for that specific action-oriented query than a single provider’s high-quality blog post. When your content loses to a competitor’s framing, you can’t just make your page “better” in a traditional sense. You have to analyze what makes the winning path more useful to the model. Does it present information more directly? Is it structured as a choice-driven tool rather than a narrative article? You may need to create content that frames the solution in a way that is more aligned with task completion, rather than just information delivery.
For teams wanting to measure this performance gap without enterprise tools, what types of revenue-critical user intents should they start with? Could you walk us through a simple, practical scoring system to track whether AI answers are consistently routing users toward or away from their solutions?
You absolutely don’t need a huge budget to start measuring this. The key is consistency and focusing on what matters. I recommend starting with about 10 intents that are directly tied to revenue or retention—queries about choosing a product, comparing options, or solving a critical problem. Then, you run those exact prompts on the AI platforms your customers actually use. Each time, you capture four simple things: which sources are cited, if your brand is mentioned at all, if your preferred page appears, and whether the answer steers the user toward you or away. From there, you use a simple scoring system. A ‘4’ means your content is clearly driving the answer. A ‘3’ means you appear, but in a minor role. A ‘2’ means you’re absent and a third party dominates. And a ‘1’ means the answer actually contradicts your guidance or sends users to a competitor. This simple baseline, tracked monthly, shows you if you’re closing the gap or just rewriting words for no reason.
Moving from just writing to “content engineering” seems crucial. What does it mean to write “anchorable statements” versus narrative prose? Please provide a before-and-after example that illustrates how to make a piece of content more extractable for a retrieval system.
“Content engineering” is the perfect term for it because it’s a shift from art to architecture. It’s about building content that is structurally sound for machine consumption. Writing “anchorable statements” is a core part of this. Instead of a flowing, narrative sentence, you create a stable, declarative claim that a model can easily extract. For example, narrative prose might say: “While our system offers a wide range of capabilities, we’ve often found that users who are concerned about security tend to gravitate toward the enterprise-level features.” It’s nice, but it’s hedged. An anchorable version would be: “The enterprise plan provides security features compliant with industry standards. These features are not available in the basic or pro plans.” It’s direct, it states a constraint, and it’s easily extractable. The “before” reads well to a human; the “after” is usable for an AI assembling an answer. That’s the difference.
What is your forecast for content strategy?
My forecast is that the era of assuming quality is portable is definitively over. The future of content strategy runs in two parallel modes: you must continue creating wonderful, engaging content for humans, but you must also engineer that content to be highly usable for models. These needs are not always identical. This fundamentally changes roles. A content writer can no longer treat structure as a simple formatting task; structure is now a core part of performance. And an SEO can no longer just focus on technical hygiene and optimizing around the edges of content. They have to understand how the content itself behaves when it’s deconstructed and reassembled by an AI. The organizations that thrive will be the ones who stop debating whether AI answers are different and start treating model-relative utility as a measurable gap that they can close, intent by intent.