Someone is asking an AI chat whether rooftop solar makes financial sense for their house. They’ve shared that they live in Phoenix, they pay $280 a month for electricity, and that they want to know if the federal tax credit still applies after last year’s policy changes. Next question: lease or buy? Then: how does net metering work in their utility district? They haven’t called an installer. They haven’t Googled a single brand name. They haven’t done anything a traditional advertising data stack would register.

But they’re about to spend $30,000.

The signal gap

This example reflects a new gap in advertising. It’s not budget size or creative. It’s between advertisers that can see upstream data and the ones that can’t. For two decades, winning meant scale and speed: more reach, faster bidding, better ROAS. But now, every advertiser can rely on reach and highly optimized real-time bidding. The advantage now is seeing earlier into a decision, not reaching more people. The performance edge no longer comes from tracking the most users. It comes from reading signals others cannot see.

Discovery has already moved

Consumer discovery has already fragmented in ways that make this urgent. AI assistants now account for 56% of global search volume each month, according to Graphite.io research. EMARKETER forecasts that 133 million US adults (nearly 40% of the population) will use generative AI this year. Meanwhile, OpenAI began selling ads in ChatGPT in February.

AI-native conversations differ. The solar shopper didn’t type “best solar installer Phoenix.” Instead, they reasoned out loud across questions aligning to their financial priorities, policy uncertainty, and timeline. This real-time decision process is conversational intent in action. It surfaces earlier in the buying journey than traditional signals and carries high-fidelity context—before the consumer reaches a search engine, comparison site, or paid media.

Brands have always wanted to appear at that moment. They just couldn’t see it.

New conversational intent signals call for a new approach

Conversational intent signals are not a replacement for search intent data. Rather, the opportunity is to unify intent signals from AI chats and search with other data sources. Verve recently became the first ad tech platform to achieve this by integrating intent signals from AI-native conversations into an intelligence layer with search data and zero-party polling data. It’s platform-agnostic, with large language model signals coming from opted-in users who’ve consented to share pseudonymized AI chat activity via the apps they already used. This creates audience- and segment-level intelligence that advertisers can activate across channels.

Search data shows where a consumer is in the funnel. Conversational signals show how they got there and what’s actually driving consideration. Zero-party polling data, where consumers directly declare preferences and purchase plans, shows what they intend to do next.

No single signal gives the full picture. Combined, they show how a purchase decision actually develops, in real time, not just where someone landed. The shift also reflects the transition from identity-based tracking to signal-based prediction.

The data set underlying conversational intent grows every time someone opens an AI tool to think something through, already happening billions of times a day. Brands that learn to read those signals now will compound that advantage into sharper models, richer audiences, and more accurate predictive targeting. Brands that wait miss the chance to optimize against the high-fidelity signals that shape the entire path to purchase.