Zane Burnett of The Agency is reframing the conversation around AI in real estate, insisting that operational efficiency—not return on investment—should be the primary goal. He told HousingWire that firms chasing quick returns from artificial intelligence are missing the point; the foundation must be clean, structured data.
Burnett's focus on data hygiene challenges the industry's prevailing hype cycle, where many brokerages rush to adopt AI tools without first auditing their internal databases. He argues that messy or incomplete datasets produce unreliable outputs, undermining any efficiency gains the technology might promise.
The executive's comments come as real estate companies increasingly experiment with generative AI for tasks like lead generation, marketing copy, and market analysis. Yet Burnett suggests that without disciplined data practices, these efforts amount to little more than costly experiments.
His perspective offers a cautionary note for brokers and agents eager to deploy AI: the path to meaningful automation runs through rigorous data management. Those who skip this step, he implies, risk wasting time and capital on tools that fail to deliver.
Some industry observers counter that waiting for perfect data may delay competitive advantages, as early adopters of AI—even with imperfect inputs—can iterate and improve over time. The debate highlights a broader tension between speed and precision in the adoption of emerging technology.