Understand Your Consumer to Compete in an AI Sales World
By Aniket Deosthali
Roughly 56% of product searches still begin on Amazon, reflecting how firmly commerce habits have been shaped over the past two decades. In my experience, brands have built entire strategies around that reality, investing in search optimization, marketplace visibility, and performance marketing to meet consumers at the point of intent. For a long time, that system rewarded those who understood how to capture demand efficiently.
How consumers seek information is changing. More people turn to AI tools like ChatGPT to ask detailed questions, compare options, and gather recommendations before making a purchase. Instead of scanning pages of results, they receive synthesized answers. At that moment, the relationship between the brand and the consumer is no longer direct, but mediated.
That shift creates a new dynamic in which many commerce teams are trying to adapt quickly, seeking ways to appear in AI-generated responses, such as restructuring product pages for clearer parsing, adopting question-and-answer formats, and rewriting copy to be more easily interpreted by AI systems.
In practice, this often mirrors the early days of SEO, when optimization efforts prioritized how algorithms surfaced content over whether it genuinely reflected customer intent.
Today, OpenAI is emerging as a key beneficiary of this shift, as ChatGPT increasingly plays a role in decision support and early-stage discovery, helping users research, compare, and evaluate options before they act.
The role of consumer intent
What these strategies often miss is a deep understanding of consumer intent, defined as what a customer is actually trying to accomplish at a given moment, based on their needs, questions, and context.
Through its own digital properties, every brand already has access—via its website, mobile app, and e-commerce platform—to a rich set of signals, such as how people browse products, what they click on, and where they hesitate or drop off. The way people move through these experiences reflects what they are trying to accomplish.
This data is immediate, specific, and highly actionable, yet frequently overlooked as teams focus outward on rapidly evolving platforms.
AI-driven discovery also changes the nature of intent expression. For years, commerce strategies focused on high-volume keywords. What is emerging now is a long tail of highly nuanced, context-rich queries that reflect real situations.
Consumers are asking detailed questions that reveal not just what they want to buy, but why they need it, and when. Instead of searching for “best laptop bag,” a consumer might now ask whether a bag will fit under an airplane seat, survive a rainy commute, and still look professional in a meeting.
This shift from keyword-based queries to full-context questions reflects a deeper expression of intent that traditional search strategies were not designed to capture.
Understand your ideal customer’s needs
A meaningful gap exists between targeting a broad category and understanding a specific use case. A customer looking for shapewear after having a child and preparing to return to work is navigating a moment that is both practical and emotional. A brand recognizing that level of specificity can respond with messaging that feels relevant and considered, rather than generic.
In this example, customer messaging might shift from a generic “high-compression shapewear for everyday wear” to “light, supportive shaping designed for postpartum comfort as you return to work and daily routines.”
These kinds of insights are often drawn from first-party signals such as on-site search terms, browsing behavior across related product categories, repeat visits to sizing or fit pages, and cart or checkout drop-off points that indicate hesitation or comparison.
Many current approaches focus on optimizing how AI systems surface and interpret product information, such as structuring product feeds for organic visibility, rewriting product titles to better match how shoppers search, and refining product attributes for machine readability, rather than grounding those efforts in real customer behavior.
As AI continues to reshape commerce, experimentation with new platforms will remain important. What will matter most is whether brands stay anchored in a clear understanding of intent and use that as a guide across every channel, rather than optimizing in isolation for any single platform.
In a landscape where LLM intermediaries are gaining influence, that connection determines whether profits flow back to the brand or are captured elsewhere.
Aniket Deosthali is CEO and cofounder of EnviveAI.