Core Insights - The integration of AI with real estate is advancing towards a higher level, presenting a dual reality of a "clear future" and a "fuzzy present" for the industry, where practical implementation is the only pathway forward [1] - The report titled "Leading Real Estate Technology 50 New Intelligent Practice Cases" outlines the phased characteristics and future directions of AI applications in the real estate sector [1] Group 1 - The industry has reached a consensus on the future of intelligence, but the speed of technological breakthroughs far exceeds the evolution of organizational capabilities, data foundations, and strategic coordination, resulting in many AI applications in real estate and construction being in a "well-received but underutilized" state [2] - To successfully navigate current challenges, companies need systemic transformations to address structural issues, as traditional real estate faces data silos and high system integration complexity, making the transition from "experience-driven" to "intelligent decision-making" difficult [2] - The real estate and construction sectors in China are transitioning from a focus on scale growth to emphasizing quality, efficiency, and sustainable development, with the national "14th Five-Year Plan" promoting the construction of a "Digital China" and presenting significant opportunities for transformation and upgrading in the industry [2] Group 2 - The real estate and construction industries require AI as an external support system, and the competitive pressure from industry transformation has been rising, leading to inevitable short-term return versus long-term value trade-offs in innovation investments [3] - The "clear" expectations in the narrative of AI empowering real estate depict a future worth striving for, while the "fuzzy" reality represents a necessary phase of trial and error and learning during industrial upgrades [3] - There is a need for stakeholders to break free from conventional thinking, leveraging AI models to empower internal and external users, reshape business processes, and drive significant changes in construction industrialization, lean operations in real estate, and asset management improvements [3]

Core Insights - The effective implementation of new technology in the real estate sector hinges on addressing core industry challenges, enhancing governance and operational efficiency, and undergoing continuous market validation [1] - The integration of AI into real estate is advancing towards a higher level, presenting a dual reality of a "clear future" and a "fuzzy present," with practical application being the only pathway [1] Group 1: Industry Challenges - The traditional real estate industry faces issues such as data silos and high complexity in system integration, making the transition from "experience-driven" to "intelligent decision-making" difficult [1] - The lack of unified standards in the construction industry, complex system integration, long investment cycles, and a shortage of multidisciplinary talent necessitate a systemic transformation from "passive" to "active" [1] - Establishing competitive advantages in real estate asset management requires balancing short-term returns with the long-term development of management capabilities [1] Group 2: Innovation Practices - Five key innovative practice directions have been identified: 1. Evolution of AI foundational capabilities from "tool empowerment" to "ecosystem reconstruction" 2. Data-driven core with first-party data becoming the engine of value creation 3. Restructuring customer relationships where CRM shifts from a "management tool" to a "value co-creation partner" 4. Future space construction driven by both "technology empowerment" and "human-centered care" 5. Advancement in real estate asset management through AI-enabled refined operations of existing assets [2] Group 3: Future Outlook - The clear expectations in AI-enabled real estate depict a future worth striving for, while the fuzzy reality represents a necessary phase of trial and learning during industry upgrades [3] - The industry must break free from conventional thinking, leveraging AI models to empower both internal and external users, leading to a transformative change in business processes and enhancing core competencies in data assets, algorithm capabilities, and organizational agility [3]