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]