What the Rise of ChatGPT Search Behaviour Meant for Product Discovery

Over 2023 and into 2024, a new habit crept into how people researched products. Alongside conventional search, many began turning to conversational AI tools such as ChatGPT to ask questions, compare options and generate shortlists. For product businesses, this raised a practical question worth examining carefully: if buyers were increasingly using an AI assistant to help decide what to consider, how could a business make sure it was part of that consideration?

It is important to approach this topic with accuracy rather than hype. Conversational AI tools behave differently from search engines, their capabilities and data sources changed frequently, and no party outside the companies building them could describe their behaviour with certainty. What follows distinguishes what could reasonably be observed about this behaviour from the strategic interpretation a product business might sensibly draw from it.

How conversational research differed from search

A traditional search returns a list of results and leaves the user to evaluate them. A conversational AI tool, by contrast, tends to return a synthesised answer — an explanation, a comparison, or a shortlist — drawn from the information available to it. The user asks in natural language, often follows up with more questions, and treats the exchange as a dialogue rather than a lookup.

For product discovery, this shifted the moment of influence. Where a searcher once scanned results and formed their own view, a buyer using a conversational tool received a pre-digested view they could then interrogate. A product that was clearly and credibly represented in the information these tools could draw upon had a better chance of appearing in that synthesised answer than one that was poorly documented online.

Why clarity and evidence mattered more

Because conversational tools synthesise rather than list, they favour information that is clear, specific and credible enough to be summarised confidently. Vague marketing language gave these tools little to work with, whereas concrete detail — what a product does, what it is suited to, how it compares — gave them substance to draw upon.

This raised the value of the way a product business could build evidence into commercial content for AI-era search. Specifications, use cases, real applications and honest comparisons were exactly the kind of material a conversational tool could use to represent a product accurately. Businesses that buried this information, or never published it clearly, made themselves harder to include.

Hidden information became a liability

Many product businesses, especially technical ones, held their most useful information in forms that were hard for any automated system to use — locked inside PDF brochures, spec sheets that never made it onto the website, or knowledge that lived only in sales conversations. In a world where buyers increasingly relied on tools that read the web, this hidden information became a genuine liability.

Learning to turn technical specifications into searchable commercial content addressed this directly. Bringing product detail into clear, accessible web pages made it available not only to traditional search but to the conversational tools buyers were beginning to use. The same work improved the experience for human buyers, which made it worthwhile regardless of how AI tools evolved.

Helping tools understand what a product is

Conversational tools, like search systems, try to understand the relationships between a business, its products and the problems they solve. A product represented inconsistently or ambiguously across a website was harder to place correctly than one whose purpose, category and applications were stated clearly and consistently.

Considering how AI systems understand the relationships between your brands, products and industries helped product businesses identify where their online presence was confusing. Clarifying these connections improved the chance that a conversational tool would associate a product correctly with the needs it actually met, rather than overlooking it or misrepresenting it.

The limits of what businesses could control

It is worth being honest about the limits here. No product business could guarantee that a conversational AI tool would recommend it, and any agency claiming to offer such a guarantee was overstating what was knowable. These tools drew on many sources, changed frequently, and made their own judgements in ways no outside party fully understood.

What a business could control was the clarity, accuracy and credibility of its own information across the web. This was not a trick to game a system but a durable investment: a business that was well-documented, consistent and credible gave every tool — conversational or conventional — a sound basis for representing it. That was the realistic goal, and it was a worthwhile one.

Brand still shaped the outcome

Even in conversational research, brand mattered. A buyer who received a shortlist from an AI tool still had to choose, and a recognisable, trusted name carried weight in that choice. Moreover, a buyer often verified an AI tool's suggestions with their own search or a trusted colleague, and a strong brand fared better through that scrutiny than an unknown one.

The reasons brand searches matter more than ever for established B2B companies extended naturally into conversational discovery. A product might be surfaced by an AI tool, but it was the brand that turned that surfacing into serious consideration. Investment in reputation therefore complemented, rather than competed with, the work of making information accessible.

A practical response for product businesses

The sensible response drew these threads together. Product businesses that documented their products clearly, published specifications and use cases as accessible content, stated their categories and applications consistently, and invested in a credible brand made themselves easier to discover, represent and recommend — whether the discovery happened through conventional search or a conversational tool.

None of this required predicting how any specific AI tool would behave. Each action improved the business's presence across every plausible future, which is exactly why it was worth doing. An evidence-led approach to SEO, GEO and AIO gave product businesses a way to adapt to conversational discovery without gambling on the specifics of any single tool.

The buyer's changing expectations

Beyond the mechanics of how tools worked, conversational research subtly reshaped what buyers expected. Someone accustomed to asking an AI assistant a question and receiving a clear, direct answer began to bring that expectation to every interaction, including visits to a product website. Vague, meandering pages that forced a buyer to hunt for basic facts felt increasingly out of step with the directness of a conversational exchange.

For product businesses, this meant the standard for their own content rose. A buyer who had just asked an AI tool for a plain comparison of options was unlikely to tolerate a website that answered the same question with marketing slogans. The businesses that adapted well made their own pages as clear and direct as a good conversational answer — stating plainly what a product did, who it suited, and how it compared — rather than hiding that clarity behind promotional language.

Consistency across every source

Because conversational tools drew on information from across the web, inconsistency became a particular problem. If a product was described one way on the company website, another way in a directory, and a third way in old marketing material still floating online, a tool synthesising these sources had to reconcile contradictions, and might do so poorly. A buyer receiving a muddled answer was unlikely to pursue the product further.

The remedy was consistency. A product business that described its products in the same clear terms everywhere it appeared gave conversational tools a coherent picture to work from. This required a degree of housekeeping — reviewing where the business was represented online and ensuring the descriptions aligned — but the payoff was a more accurate and more favourable representation across the sources buyers and their tools relied upon.

Treating conversational discovery as one channel among several

Finally, it was important not to over-rotate towards conversational discovery at the expense of everything else. It was one emerging behaviour among several, and most buyers still used a mix of conventional search, video, community discussion and direct visits alongside any conversational research. A product business that abandoned proven channels to chase the newest one risked weakening its overall presence.

The wiser posture treated conversational discovery as an additional surface to be ready for, served by the same clear, credible, well-documented content that helped everywhere else. This is what made the work so worthwhile: it was not a speculative bet on a single tool but an improvement that strengthened the business across the entire, distributed way buyers now researched products. Businesses that held this balanced view adapted to conversational discovery without destabilising the rest of their strategy.

There was also a competitive dimension worth noting. Because many product businesses were slow to document their products clearly and consistently, those that did so early gained a real advantage in conversational discovery. When a tool synthesised an answer from the available information, the business that had published clear specifications, honest use cases and consistent descriptions simply gave the tool more to work with than a rival whose details remained locked in brochures or scattered across contradictory sources. The advantage did not come from any secret technique; it came from the unglamorous discipline of making genuinely useful information easy to find and hard to misinterpret. Product businesses that recognised this treated documentation not as an administrative chore but as a form of discoverability that paid dividends across every channel their buyers used.

Conclusion

The rise of ChatGPT-style search behaviour changed product discovery by inserting a synthesising layer between the buyer and the raw information. For product businesses, the realistic response was not to chase guarantees no one could offer, but to become genuinely easy to understand: clear, well-documented, consistent and credible across the web, with a brand strong enough to turn discovery into consideration. Businesses that did this were well placed however conversational research developed, because the qualities it rewarded were the same ones that had always made a product easy to find and trust.