Conviva's CEO warns against "AI washing," where companies apply AI agents to poorly structured data. An agent cannot invent insights that aren't present in the source data. A strong data computation engine is the true prerequisite for effective AI, not a cosmetic front-end.

Startups should stop building customer personas on assumptions and surveys. Instead, use AI to analyze real-time behavioral data, creating dynamic profiles that update automatically. This shifts marketing from targeting who you think customers are to who they actually are based on their actions.

Treat product data as a reflection of human behavior. At DoorDash, realizing the order status page had 3x more views than the homepage revealed intense user anxiety ("hanger"). This insight, derived from a data outlier, directly led to the creation of live order tracking.

To avoid getting lost in data, PMs should first define the decision they need to make (e.g., improve ROI, increase usability). This goal then dictates which data to gather and from whom. Patterns should be grouped by desired user outcomes, not feature requests, creating a more strategic path to delivery.

Traditional funnels miss the nuance of individual buying journeys. Conviva's CEO argues for analyzing personal behavior patterns—like a "research shopper" toggling between cart and reviews—to understand user intent and boost conversion, rather than forcing users into a predefined sequence.

There are three levels of trust for customer data: CRM data (low), customer words (medium), and customer actions (high). Use AI to compile timelines of successful customer actions (e.g., product usage) to build reliable hypotheses about who to target next.

Most sales teams discard data from failed calls and dead ends. Capturing this "exhaust data" in a structured warehouse and analyzing it with AI provides rich insights into what *doesn't* work, which is as crucial for refining strategy as understanding what does.

Dashboards show data but not the 'so what.' While conversational AI helps answer user questions, the next evolution is proactive insight generation. Future AI tools will solve the 'we don't know what we don't know' problem by suggesting actions and surfacing opportunities marketers haven't thought to ask about.

Traditional automated dashboards are often ignored. AI-driven reporting is superior because it doesn't just present data; it actively analyzes it. The AI summarizes trends, generates relevant follow-up questions, and even attempts to answer them, ensuring that insights are never missed, even when stakeholders are busy.