Understanding layered intent is the key to staying visible in an AI-first search world.
A few weeks ago, I wrote about the need for a VP of Answers and described how AI systems are reshaping what it means to be “the best” in search. It was sparked by a real-life moment: I was researching the best travel CO2 monitor and noticed something peculiar. The product I ultimately purchased, a reliable, well-built device that was well-researched, reading reviews and product specs, wasn’t even mentioned. Why? Not because it was bad but because it didn’t provide the right signals for AI systems to recognize it as a valid option. Yesterday, I posted another mini-rant about the need for companies to more deeply understand consumer intent and how to connect with them across their product search journey.
I want to dig deeper into this first step of the answer process that few discuss, “dissecting the intent behind the question or query.” This shift is happening across every product category, especially those where the definition of “best” is subjective. The culprit (or hero, depending on your point of view) is the evolution of search from keyword-matching to semantic interpretation—the shift from search to synthesis.
What Is the Shift from Search to Synthesis?
In traditional search, the algorithm’s job was to find the best-matching pages for the words you typed. That model rewarded keyword optimization and left users to sift through links to find what they needed. But in today’s AI-powered environments, what Google calls AI Overviews, and others call Answer Engines, the goal is no longer retrieval. It’s resolution.
Synthesis combines data from multiple sources, interprets the user’s intent, and delivers a single, integrated answer.
Instead of showing you 10 blue links, AI synthesizes the answer you were really after—even if your question was vague or incomplete.
This is the shift: Search was about matching. Synthesis is about meaning.
It’s a subtle but powerful evolution—and if your content isn’t structured to support synthesis, it risks being excluded altogether.
Let’s unpack what that means through a deceptively simple question I recently asked trying to find a new SCUBA diving regular to use when I travel. Being Google-trained in query formation, I used the following rather than a long, complex AI query in multiple AI agents:
“What is the best travel regulator?”
Dissecting the Intent Behind the Question
At face value, that’s a five-word query. Google treats it as such by giving me a set of ads and organic results for the best regular scuba regulators despite specifically asking for travel regulators. Reddit gives me a discussion about them, and another mentions travel regulators in the snippet by extracting it from a reviewer’s comment. Then there is Mike’s Dive store, which has a page with five paragraphs of highly optimized SEO content stuffed with travel phrase variations. This is followed by a shopping cluster full of regulators without indicating why they are presented.
The same question presented to Chatgpt it gave me an overview and a table of recommended products with their features and why they are great for travel. Below, the recommendations provided a detailed list of tips for selecting one and considerations for traveling.
The difference between the two sets of results is exactly the problem. Traditional results suggest options for getting my answer where the AI system dissected my request, made assumptions, looked for context clues, and developed evaluation criteria to answer my question and help me understand why they are the best options and how to do further evaluation.
Step 1: Disambiguate the Object
“Regulator” could mean a voltage adapter, a gas line component, or a scuba diving regulator. AI must resolve this ambiguity quickly by:
- Analyzing your recent search behavior
- Cross-referencing popular associations with “travel regulator”
- Looking at seasonality (diving gear spikes in summer)
- Or, if you’re Google, applying deep user-profile signals
Outcome: Based on search patterns, AI assumes you want a scuba diving regulator.
Step 2: Interpret “Travel”
Now, “travel” isn’t just a descriptor—it’s a constraint. You’re not just diving; you’re flying and diving. That suggests you’re looking for:
- Lightweight gear that won’t blow your baggage allowance
- Compact design that packs easily
- Durable build that survives airlines and saltwater
- Global serviceability if something goes wrong in Fiji or Bonaire
These aren’t just nice-to-haves. They are intent signals—what AI is trained to infer behind the query.
Step Three: Define “Best”
Here’s the messy part: “Best” is the most overloaded and abused word in search. It’s ambiguous, highly commercial, and has become the SEO world’s favorite bait. Thousands of sites churn out content trying to rank for it, some with real testing, others simply parroting what’s already out there. For AI systems, this creates a challenge: how to cut through the noise and surface results that are genuinely helpful, trustworthy, and relevant to the user’s actual needs.
So, to answer my request for the “best,” it has to sift through various potential variables. Does the best mean:
- The most technically advanced regulator?
- The most affordable for casual divers?
- The one with the smoothest breathing at depth?
- The one most frequently recommended by dive instructors?
- The most reviewed on Amazon?
AI systems try to resolve or “fan out” these sub-questions behind the scenes. They look for consensus among trusted sources, check for product specifications, scan for experience-based reviews, and infer patterns from what users tend to click or engage with.
Why a Great Product Might Still Be Missing
Even if a regulator checks all the boxes, it might not appear in AI Overviews or “best of” lists. Here’s why:
It lacks narrative context.
AI values experience-based content, which is why they mine sites like Redditt. “I traveled with the Apeks XL4+ and loved how light it was in my carry-on” is more valuable than a spec sheet alone.
Specs are missing or buried.
If weight, materials, or temperature range aren’t stated clearly, AI can’t compare them.
It’s not positioned as “travel-friendly.”
Even if it is compact and light if no one says that outright, AI won’t infer it.
There’s no review consensus.
A regulator might be solid, but it won’t rank in a recommendation model if it’s not mentioned in forums, gear review sites, or trusted scuba publications.
Structured data is absent.
AI benefits from structured inputs (schema, comparison tables, feature callouts). Without them, it struggles to “see” what makes your product stand out.
Let’s address the common pushback: “AI agents don’t use schema.” That’s simply not true. Both Google and Bing have confirmed that structured data—including schema.org markup—helps their systems interpret and contextualize content more accurately. While it’s true that schema alone won’t win you a spot in an AI answer, its absence can make it harder for machines to “see” what your content is actually about. And it’s not just about markup—it’s about structure. Tables, labeled feature lists, comparison grids, and semantic clarity all help AI systems synthesize answers. Schema may not be “cool,” but it’s a clear signal of organized, intentional content—and in a world of ambiguous queries, that’s a real advantage. Oddly, if your content is structured well enough to support schema, it’s already positioned to do well in AI synthesis.
What This Means for Brands and Retailers
We’re entering an era where content must be both human-readable and machine-interpretable. Here’s what retailers and manufacturers can do:
For Retailers:
- Curate Content for Decision Paths
- Build product category pages that explicitly call out decision factors like weight, breathing effort, or cold-water readiness.
- Use checklists, comparison charts, and use-case guides (e.g., “Best Regulators for Travelers Under 2 lbs”).
2. Implement Structured Data and Schema Markup
Publish Real-Use Scenarios
- Include travel stories or packing tips that mention specific products and demonstrate their value in context. AI favors “lived experience” narratives.
For Manufacturers:
1. Rethink Product Pages
- Don’t just sell. Educate. Explain why a feature matters. e.g., “Titanium second stage reduces weight and corrosion risk—ideal for travelers.”
2. Build Comparison Tools
- Let users compare across use cases: warm vs. cold water, local diving vs. travel, beginner vs. advanced.
3. Leverage Awards and Accolades
- Add it to your site if you were rated the best travel regulator or anything by any reputable review platform like ScubaLab.
- Add anything true that will boost the credibility, certification of validation of features can help boost it if that is a criteria for analysis.
4. Syndicate Trusted Reviews
- Get your product into diving magazines, instructors, and forums. The more your product is discussed in trusted ecosystems, the more signals AI receives. That is the power of product or brand ambassadors.
In one head-to-head review, three regulators were suggested. Funny enough, I have two of the three because they checked similar boxes to what AI used to make their recommendation. I have done thousands of dives on both and would highly recommend them. None of the manufacturers have much other than the everyday engineering marvels; they have a reputation for quality and functionality. The most expensive version, the one I don’t have for that reason, infers it is the perfect one for tropical vacations. Their site is not shown in the results, but SCUBA magazines and forums are listed because they break out the functionality into normalized and relevant terms.
Final Thoughts
This shift from search to synthesis isn’t just an algorithmic upgrade—it’s a new philosophy of content evaluation. AI doesn’t just fetch results. It filters them through layers of inferred human intent.
Suppose your content doesn’t support that synthesis, and you fail to make your product easy to understand, compare, and believe in. In that case, you risk being invisible, even if your product is objectively excellent. It’s no longer about showing up for keywords. It’s about showing up for needs.
