Consumer behavior is undergoing a quiet but decisive shift, and Gen Z is leading it. Recent research shows that 61% of Gen Z shoppers have already used AI tools to help with a purchase in the past year, and roughly a third now prefer AI platforms over traditional search engines when researching products, a figure that is rapidly closing in on the share still using search.¹ For brands, the implication is straightforward: the discovery journey that used to begin with a Google search increasingly begins with a conversation with an AI assistant instead.
This raises an obvious question: how does a brand actually become "present" on AI? The short answer is that a brand needs to distribute its information as structured, machine-readable content across as many credible sources as possible, so that AI retrieval systems can locate that content and surface it whenever a relevant question is asked. In industry terms, these questions are known as prompts, and understanding how people phrase them is the foundation of any AI visibility strategy.
No Brand Can Own Every Prompt
It isn't realistic, or even useful, for a brand to try to appear in response to every possible question. Instead, the smarter approach is to optimize for the prompts that matter most, by thinking carefully about who is asking, what they already know, and where they are in their journey toward choosing a product or service. To do that well, we start by segmenting users into three distinct types.
User Type 1 (U1): The Brand-Aware Researcher
This user already knows your brand by name. What they're looking for now is validation: reassurance that their instinct to consider you is a sound one before they commit to a purchase. Their prompts tend to be direct and comparative, such as "Tell me about Brand A" or "Is Brand A a good brand?" These users are close to a decision, and the content a brand publishes here needs to build credibility quickly: clear positioning, honest third-party validation, and evidence that backs up any claims.
User Type 2 (U2): The Solution Seeker
This user has already diagnosed their problem and knows, in general terms, what kind of solution they need. They are shopping with intent, but haven't yet picked a provider. A prompt like "My AC is broken, HVAC near me" is a good example. The user knows the fix (an HVAC technician) and is now looking for the right business to call. For this group, the opportunity is to make sure that when someone is actively looking for a solution you provide, your brand is one of the names the AI surfaces.
User Type 3 (U3): The Problem-Aware, Solution-Unaware User
This is the broadest and, in many ways, the most valuable group to reach early. These users know something is wrong, but they don't yet know what will fix it, and they're often not even sure what category of product or service they need. A prompt like "My car is making a weird noise" captures this well: the person is starting from a symptom, not a solution. They're effectively asking two questions at once, even if only the first is spoken aloud: first, "what's going on?" and second, "who can fix it for me?" Brands that publish genuinely useful diagnostic content have a real opportunity to enter the conversation at the earliest possible stage, before a competitor's name ever comes up.
Translating User Types Into a Content Strategy
At Annapurna Influence, our goal is to make sure a brand's content addresses all three user types, so that the AI retrieval layer has something authoritative to draw on no matter where in the journey a person happens to be. To do this systematically, we've built our content approach around three corresponding prompt types.
Type A: Answering the brand-aware researcher. This content is built for U1's direct, brand-name prompts, such as "Is Brand A a good brand?" The objective is to make sure that when an AI is asked to evaluate your brand by name, it has access to clear, structured, and favorable information to draw from, not just whatever it happens to find elsewhere.
Type B: Owning the solution. This content is built for U2, whose prompts describe a known solution category, like "best place for steaks." If your brand provides that solution, the goal is to produce solution-focused content specific and credible enough that the AI treats it as a citable source and surfaces your brand as one of the answers.
Type C: Getting there first, at the problem stage. This content is built for U3, whose prompts start from a symptom rather than a solution, such as "my car is making a weird noise." Here, the strategy is to publish content that explains the likely causes of the problem, and to naturally position your brand's product or service as the resolution. Done well, this lets a brand enter the customer's journey before they've even framed the question in terms of a product category, let alone a specific competitor.
Geolocation Adds a Critical Fourth Dimension
Location changes everything about how a prompt gets answered. Adding a geographic component narrows the field of relevant solutions considerably. Someone asking about HVAC repair "near me" isn't interested in a national brand with no local presence, and an AI assistant fielding that prompt will prioritize answers that are locally specific. Because of this, brands need to be just as deliberate about generating geo-tagged content as they are about the content itself. A national brand strategy that ignores location will lose out to smaller, hyper-local competitors whose content simply matches the way people actually ask these questions.
The Core Challenge: Competing With Incumbents
Large, well-funded companies present a real obstacle here. They already have enormous volumes of content in circulation, along with years of accumulated reviews (some glowing, some critical) from independent creators and publications. Competing against that scale requires a different kind of advantage, not just more content for its own sake.
The path forward is to out-execute on precision and freshness rather than sheer volume: producing geo-targeted, AI-optimized content; refreshing that content on a regular cadence so it stays current; and building a consistent publishing history that gives AI retrieval systems a deep, trustworthy well of material to draw from over time. This is precisely where we focus our work: helping brands establish and grow their footprint across the internet, so that when an AI retrieval layer goes looking for an answer, your brand's content is there waiting to be found.
Reference
-
Envive AI, "30 Gen Z AI Shopping Statistics for Ecommerce." https://www.envive.ai/post/gen-z-ai-shopping-statistics