Presented by TwilioThe customer data infrastructure powering most enterprises was architected for a world that no longer exists: one where marketing interactions could be captured and processed in batches, where campaign timing was measured in days (not milliseconds), and where “personalization” meant inserting a first name into an email template.Conversational AI has shattered those assumptions.AI agents need to know what a customer just said, the tone they used, their emotional state, and their complete history with a brand instantly to provide relevant guidance and effective resolution. This fast-moving stream of conversational signals (tone, urgency, intent, sentiment) represents a fundamentally different category of customer data. Yet the systems most enterprises rely on today were never designed to capture or deliver it at the speed modern customer experiences demand.The conversational AI context gapThe consequences of this architectural mismatch are already visible in customer satisfaction data. Twilio’s Inside the Conversational AI Revolution report reveals that more than half (54%) of consumers report AI rarely has context from their past interactions, and only 15% feel that human agents receive the full story after an AI handoff. The result: customer experiences defined by repetition, friction, and disjointed handoffs.The problem isn’t a lack of customer data. Enterprises are drowning in it. The problem is that conversational AI requires real-time, portable memory of customer interactions, and few organizations have infrastructure capable of delivering it. Traditional CRMs and CDPs excel at capturing static attributes but weren’t architected to handle the dynamic exchange of a conversation unfolding second by second.Solving this requires building conversational memory inside communications infrastructure itself, rather than attempting to bolt it onto legacy data systems through …
