在实现通用人工智能这一宏伟目标牵引下，AI（人工智能）产业正迈向其发展的"下一个十年"。 不过，在模型能力持续提升的同时，产业落地的脚步却并未同步提速。11月16日，在"2025人工智能 +"大会上，多位业内人士表示，高昂的成本、缺乏高质量行业数据、工程化能力不足以及对大模型能 力边界认知的偏差，正成为制约AI规模化应用的四大结构性难题。 与此同时，从开源与商业化的平衡，到AI入口从云端向终端迁移，产业格局正在发生微妙变化。 AI落地面临"拦路虎" 当产业界尝试将大模型从实验室推向车间、办公室和街头巷尾时，规模化落地遇到的阻力远超预期。 "大模型（当前的）叙事逻辑对赋能千行百业不友好的地方在于，我们一直说规模法则，要把模型越做 越大、越来越强，对应的结果是成本越来越高。"清华大学计算机系副教授、面壁智能联合创始人兼首 席科学家刘知远在圆桌论坛上表示。 刘知远指出，叙事逻辑的不友好直接带来了大模型训练和使用成本的持续攀高。他认为，任何技术要对 人类社会产生深远影响，都必须解决标准化和成本问题。正如芯片行业的摩尔定律，他所在的团队提出 了大模型能力密度法则，即通过技术创新，让更少的参数承载更多的模型能力。"摩尔定 ...

Group 1 - The core idea emphasizes the importance of prioritizing demands to maximize the value of limited R&D resources through a multi-dimensional evaluation framework that includes "value," "cost," and "risk" [1][4] - The top priority in demand evaluation is the alignment with business value and strategic goals, which serves as the guiding principle for decision-making [3][7] - Demand prioritization is fundamentally an economic game of opportunity cost, where every demand represents a potential project vying for scarce R&D resources [4][5] Group 2 - The first criterion for prioritization is business value and strategic alignment, which assesses whether a demand can significantly contribute to achieving key strategic objectives [7][8] - The second criterion focuses on user value and pain points, determining the breadth and depth of the problem a demand addresses for users [9][10] - The third criterion evaluates cost and complexity, analyzing the effort required for implementation and the associated risks [11][12] Group 3 - The fourth criterion considers timing and dependencies, assessing whether the current moment is the right time to pursue a demand and identifying any necessary prerequisites [13][14] - In practice, these criteria should not be treated in isolation but rather integrated into a comprehensive prioritization process that balances multiple dimensions [15][16] - Quantitative models like RICE and WSJF serve as frameworks to mathematically combine these various criteria into a single comparable score [17][18] Group 4 - Regular team meetings, such as backlog refinement sessions, are essential for collaborative prioritization, allowing input from various stakeholders [19][20] - Tools can help make prioritization criteria explicit, enhancing transparency and data-driven decision-making [21][22] - The weighting of prioritization criteria may change depending on the product lifecycle stage, with different focuses at various phases [22][23]

Core Viewpoint - The essence of business remains the connection between people, and understanding user needs and insights is crucial for growth, especially in the AI era [2][5][15]. Group 1: AI and Growth - The arrival of AI has changed growth logic, but the fundamental principle of understanding user needs remains unchanged [6][7]. - AI can empower businesses by providing real incremental value and improving efficiency in user acquisition and retention [6][7][49]. - Companies that focus on unmet user needs can discover significant growth opportunities, as demonstrated by the AI PPT case targeting mothers [10][14]. Group 2: User Insights and Metrics - Establishing the right North Star metric is essential for guiding growth strategies, as seen in Meituan's shift from GMV to order volume [18][19]. - Metrics should be based on user insights and can evolve with the product lifecycle, ensuring alignment with user needs and market conditions [20][21][27]. - The importance of understanding why users leave is emphasized, as it can be more critical than knowing why they stay [55][51]. Group 3: Data Analysis and Strategy - A systematic approach to data analysis is necessary for effective decision-making, allowing for detailed breakdowns of performance metrics [31][32]. - Companies should focus on user behavior and preferences to refine their strategies, ensuring that insights are actionable and relevant [36][38]. - AI can assist in data processing and user research, enhancing productivity and decision-making capabilities [40][52]. Group 4: Retention and Recall Strategies - Retaining users requires a deep understanding of their needs and behaviors, with AI models helping to identify key factors influencing user retention [49][51]. - The ability to recall users hinges on understanding the reasons for their departure, which can be influenced by various factors, including geographic and economic indicators [51][52]. - Companies must balance short-term gains with long-term user value to ensure sustainable growth [22][30]. Group 5: Challenges in AI Growth - Despite the potential of AI, challenges remain in achieving high retention rates and effective monetization strategies [56][57]. - The industry is evolving, with domestic companies leading in growth strategies, indicating a shift in knowledge exchange between international markets [57].