The most significant error when approaching conversational AI is not a specific tactical mistake, but a lack of action. Delaying entry into this new channel is more damaging than launching an imperfect campaign, as action creates the data needed for iteration and learning, which provides a competitive advantage.
Marketers over-index on vanity metrics while underappreciating the strategic value of time. The ability to launch campaigns at the "speed of culture" provides a significant competitive arbitrage. Teams should measure and actively work to reduce the time it takes to go from idea to a live campaign.
The goal for advertising in AI shouldn't just be to avoid disruption. The aim is to create ads so valuable and helpful that users would prefer the experience *with* the ads. This shifts the focus from simple relevance to actively enhancing the user's task or solving their immediate problem.
As users delegate tasks to AI agents, a new targeting framework emerges. Instead of targeting based on keywords or past behavior, brands can target users based on the specific task they are trying to accomplish (e.g., "write a report," "plan a trip"). This allows for hyper-relevant, solution-oriented advertising.
A novel way to measure ad effectiveness in LLMs is "attention shift"—analyzing how much an ad pivots the conversation's topic toward the brand. This metric, derived from vector analysis of messages before and after an ad, captures influence beyond traditional clicks or impressions, reflecting deeper engagement.
Conversational ads offer an unprecedented one-on-one channel for brands to interact with customers at scale. The resulting data—customer questions, complaints, and feedback—is a goldmine for product development and other business functions, potentially exceeding the value of immediate customer acquisition.