In 2021, real estate giant Zillow shocked markets by shutting down Zillow Offers, its ambitious AI-driven iBuying business — laying off 25% of its workforce and absorbing more than $500 million in losses, alongside a $40 billion market-cap wipeout.What was meant to transform Zillow into the “Amazon of homes” became one of the most important enterprise AI failure case studies of the decade.In this episode of AI Governance & Strategy: Navigating the Future, we break down why Zillow’s AI bet failed — despite massive data advantages and a world-class brand.🔍 In this video, we explore:The Power (and Limits) of the Zestimate — how Zillow tried to turn valuation models into instant cash offersProject Ketchup — why removing human pricing guardrails to chase $20B in annual revenue backfiredConcept Drift & Macro Blindness — how the algorithm failed to recognize a cooling post-pandemic marketThe “Last-Mile” Problem — why AI couldn’t account for labor shortages, renovation delays, and hidden home defectsThe Aftermath — $500M+ losses, inventory backlogs, and a 25% workforce reductionKey Lessons for Enterprise AI — why human judgment and governance still matterZillow’s story is a premier cautionary tale for the AI age: even the most data-rich companies can fail when algorithms are decoupled from macroeconomic reality, operational complexity, and human oversight.Produced by Neural Flow Consulting.