Marketers have long aimed to pinpoint consumer needs in real time and respond effectively, a challenge that has persisted since the discipline began. Traditional methods relied on segmentation and planning cycles, which often led to missed opportunities and slow learning. Campaigns were launched based on historical data rather than immediate consumer behavior, creating a disconnect in understanding what people really wanted. This resulted in a focus on metrics like reach and clicks rather than genuine customer engagement. With the rise of AI, many of these constraints are lifting. The article emphasizes a shift to an "intent economy," where technology allows for real-time sensing of consumer needs. Instead of relying on broad segments or planned campaigns, marketers can now respond immediately when someone shows intent, like searching for specific products. Systems can operate cohesively, making decisions based on current user behavior rather than outdated data. This new model allows for continuous learning from every interaction, refining marketing strategies on the fly. The piece contrasts the attention economy, built on interruption and indirect growth, with this emerging intent economy. Previously, brands created demand through campaigns and messages, often requiring substantial investment and patience. Now, the focus is on capturing demand as it emerges, allowing for immediate action. Profitability becomes clearer and more traceable, as companies can see direct correlations between consumer actions and marketing efforts, leading to smarter decision-making and improved efficiency across all channels.