Core Insights - Workday's research reveals that 76% of employees lack clarity on contract ownership, leading to lost revenue and missed opportunities [2][4] - The report emphasizes the importance of contract intelligence in transforming contracts from static documents into strategic assets that drive business growth [3][4] Key Findings - A significant portion of employees (50% of legal and 49% of enterprise employees) report financial losses due to unintended auto-renewals, with sales and marketing departments being the most affected (60%) [4] - There is a disconnect between legal teams and other employees regarding contract approvals, with an 18% discrepancy indicating potential risks from bypassing legal processes [4] - Contracts are often fragmented across various systems, with 70% of legal and 50% of non-legal contracts stored in shared drives, which hinders holistic visibility and opportunity identification [4] - Over 41% of respondents believe that slow contract processes stifle innovation and productivity, particularly affecting departments like R&D and engineering (79%) [4]

Core Insights - Zoom Communications, Inc. has introduced new agentic AI capabilities through the Custom AI Companion add-on, allowing users to connect to 16 third-party applications to enhance productivity and streamline workflows [1][6][8] Group 1: Product Features - The Custom AI Companion add-on is available for online purchase at $12 per user per month with paid Zoom Workplace plans, enabling small business owners to leverage AI across various video conferencing platforms [9] - Users can connect AI Companion to applications such as ServiceNow, Jira, Asana, and Box, facilitating task completion and intelligent app orchestration without leaving the Zoom platform [6][7] - The add-on includes features like AI-generated meeting summaries, customizable meeting templates, and the ability to create custom avatars for personalized communication [8][11] Group 2: Productivity Enhancements - The integration of AI Companion with third-party apps is designed to improve sales and service outcomes by enabling real-time updates and reducing manual efforts [7] - AI Companion can assist in project management by allowing users to update project statuses and assign tasks directly from Zoom, thus minimizing the need for switching between applications [7] - The tool also supports document collaboration by providing secure access to existing documents and generating new ones based on meeting discussions [7] Group 3: User Experience - The onboarding experience for AI Companion has been improved, making it easier for users to configure settings related to meeting summaries and sharing [12] - Users can query AI Companion for insights before, during, and after calls, enhancing the overall communication experience [12] - AI Companion can summarize documents shared in chat threads, helping users digest information efficiently [12]

以下文章来源于纵所周知101 ，作者纵所周知 101 纵所周知101 . 每一个与 AI 协作方式，都值得纵所周知。 作者 | 纵所周知101 来源 | 纵所周知101 导语： "谁愿意为此付费，为什么？"先验证需求，再优化技术。 深夜12点，朋友圈又炸了。 从爆火到裁员，只用了4个月。这是必然。 我为什么没"蹭"那波流量？ 3月7日，当整个科技媒体都在为Manus写赞歌的时候，我选择了另一条路。 不是因为我清高， 说实话，在全网都在为Manus叫好的时候，我选择唱反调是需要勇气的。 毕竟，谁也不想当那个"扫兴的人"。 这次不是因为新的AI突破，而是因为一条裁员新闻——那个4个月前让整个科技圈彻夜不眠的Manus，正在"优化"员工。还记 得3月6日那个不眠之夜吗？整个科技圈都为Manus疯狂，邀请码炒到5万元，200万人排队等候。所有人都在狂欢，说这是"AI Agent的GPT时刻"。 而我，写了一篇文章，泼了一盆冷水。 当时有人说我酸葡萄心理，有人说我不懂技术趋势。现在回头看，那篇文章里的疑问，都在今天得到了印证。 但我看到了一些让我不安的东西： 「没有用到有效内测码，对于没有亲自用过的产品，没有判断依据。 ...

以下文章来源于晚点科技 ，作者晚点团队 晚点科技 . 晚一点，好一点 今年 4 月，《晚点 LatePost》曾访谈 AI Agent 创业公司 Pokee.ai 创始人朱哲清， 他分享了强 化学习如何用于构造 Agent 。 我们了解到，Pokee.ai 近期完成种子轮融资，募资额约 1200 万美元。投资人包括对冲基金 Point72 旗下风险投资机构 Point72 Ventures、芯片公司高通的风投部门 Qualcomm Ventures， 以及英特尔 CEO 陈立武、前 Adobe CTO Abhay Parasnis 和前 Tinder CTO Maria Zhang。 Pokee.ai 去年 10 月成立，最近也只有 7 名员工。创始人朱哲清告诉我们，完成融资后 Pokee.ai 不计划大规模扩招，短期内研发员工数量不会超过 10 人。资金将用于扩张其 Agent 产品 Pokee 的可选工具集、加快对大公司客户销售。 创立 Pokee.ai 之前，朱哲清曾任 Meta "应用强化学习" 部门负责人，用强化学习算法改善内容推荐系 统，把上任前只剩 3 人，一度要关停的部门扩张至 10 余人，为 ...

Core Insights - The focus of AI discussions has shifted from large models to practical applications that help reduce costs and improve efficiency in real-world scenarios [1][6] - Feishu has launched multiple upgraded AI products, including Knowledge Q&A and AI Meeting, to address the challenges of implementing large models [6][27] - The competition in the AI space is intensifying, with Feishu capturing significant market share in various industries, including electric vehicles and tea beverage sectors [6][11] Product Updates - Feishu's multi-dimensional table product has seen significant upgrades, with monthly active users exceeding 10 million and a tenfold increase in single table capacity to 10 million rows compared to 2024 [11][18] - The loading speed of multi-dimensional tables has drastically improved, with a 2,000-row table loading in 0.94 seconds, compared to 7.4 seconds previously [11][18] - New features in the multi-dimensional table allow it to replace many small business systems, streamlining workflows for enterprises [16][18] AI Application Development - Feishu introduced an "AI Application Maturity Model" to help businesses assess their AI applications, categorizing them into four levels from concept validation to fully mature applications [24][29] - The newly launched "Feishu Aily" allows businesses to create custom agents by integrating enterprise knowledge and business systems, enhancing customer service capabilities significantly [27][28] - The development suite of Feishu has been updated to support the entire business system development process with AI assistance, optimizing efficiency and stability [28]

Group 1 - The core growth engines of the Chinese software industry are identified as AI Agents, multimodal AI models, and model deployment [1] - AI agents are evolving from concepts discussed in enterprise settings to commercially viable products, serving as a new user interface for businesses and knowledge workers [1] - Software vendors are accelerating the integration of AI agents into their professional platforms [1] Group 2 - Analysis of enterprise application project bids from late April to present indicates a robust momentum for projects in Q2 2025 [3] - The release of foundational models like DeepSeek R1 has significantly stimulated demand for privatized AI model deployment among state-owned enterprises, schools, and government clients [3] - AI model deployment projects generally have larger overall scales compared to other ERP or system upgrade projects, often including integrated solutions like computing hardware, which increases contract values [3] Group 3 - Despite solid demand for AI applications on the enterprise side, challenges remain on the consumer side, including low payment conversion rates for consumer-facing AI tools [3] - The revenue contribution from AI functionalities remains small relative to total revenue [3] - AI vendors are continuously pushing multimodal models towards lower costs, higher quality, and longer context, while the acceleration of EDA software localization is seen as a certain growth avenue for AI vendors [3]

消息面上，7月9日，飞书发布并升级了知识问答、AI会议、飞书Aily、飞书妙搭等多款AI产品。王牌产品飞书多维表格持续性能升级，月活已接近千万，这 也标志着多维表格迈入成熟工具产品品类。飞书CEO表示，"我们承诺为企业提供真能用、真落地的企业AI产品，真正帮助企业在AI时代实现智能化。" 业内机构指出，AI应用正进入高速发展期。AI原生应用和Agentic AI的崛起正在重塑行业生态。AI不再仅是辅助工具，而逐步成为业务流程和应用的核心驱 动力。在成本控制方面，无服务器和按量付费模式正成为主流趋势，平台将提供更精细的成本管理和优化工具，以确保长期运营的可持续性。此外， Data+AI能力的普及化也在加速，通过自然语言交互、低代码/无代码工具等方式，非技术用户也能快速获得数据洞察和AI赋能。可观测性能力则成为现代系 统稳定运行的关键组成部分，与实时分析紧密结合，保障AI应用和数据平台的高效运作。 规模方面，科创AIETF最新规模达44.29亿元，创成立以来新高，位居可比基金1/7。 份额方面，科创AIETF近1周份额增长1.26亿份，实现显著增长，新增份额位居可比基金1/7。 资金流入方面，科创AIETF最新资 ...

Group 1 - The core viewpoint of the news highlights the positive performance of the financial technology sector, particularly the financial technology ETF, which has shown significant growth and investor interest [3][4][5] - As of July 8, 2025, the financial technology ETF has increased by 130.77% over the past year, ranking 2nd out of 2901 index stock funds, placing it in the top 0.07% [5] - The financial technology ETF's latest scale reached 1.101 billion yuan, marking a new high in nearly a year [4] Group 2 - The recent revision of the People's Bank of China's rules on the Renminbi cross-border payment system is expected to accelerate the application of stablecoins in cross-border payments, positively impacting related concept stocks [3] - The top ten weighted stocks in the CSI Financial Technology Theme Index account for 51.2% of the index, indicating a concentrated investment in key players within the sector [6] - The financial technology ETF has experienced a net inflow of 1.61 billion yuan over the past five trading days, with an average daily net inflow of 32.28 million yuan [4]

今天，飞书终于又开了一年一次的飞书大会。 作为飞书的深度用户和老基友，我自然是也来到了现场。 我们整个公司，可以说就是长在飞书上。 我这两年，跟之前做金融公司的时候还不太一样，我们这次完全没有别的系统，没有ERP，没有CRM，没有OA，也没有什么花里胡哨的自研中台。 几乎我的所有的业务、流程和流程，都在飞书上，所有的数据，也都存在多维表格里，这玩意就是我们现在最核心的数据库。 | 用 发布 国 的创新驱 | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ● 酒加记录 ◎ 字段配置 视频配置 分期 | | 三行高 ◇ 4 壤色 | | | | | | | | | | | | 0 | ල 图 生成表单 | | さ ち さ 五 日 | | | | | | | | | | | | | 日 次日数据 | | | | | | | | 日 15分 | | □ 色 A ...

Core Viewpoint - The article discusses the emergence of AI teams that collaborate like human teams in software development and scientific research, highlighting the need for effective evaluation metrics for these multi-agent systems [2][3]. Group 1: Introduction of MultiAgentBench - MultiAgentBench is introduced as a comprehensive benchmark for evaluating the collaboration and competition capabilities of LLM-based multi-agent systems [4][6]. - It aims to fill the gap in existing evaluation metrics that focus primarily on individual agent capabilities rather than the essential aspects of collaboration efficiency and communication quality [3][6]. Group 2: Key Findings and Contributions - The research reveals that the gpt-4o-mini model exhibits the strongest overall task performance among various models [8]. - The decentralized collaboration model using a graph structure is found to be the most efficient, while cognitive self-evolution planning significantly enhances task completion rates [8][12]. - MultiAgentBench identifies critical moments where agents begin to exhibit emergent social behaviors, providing insights into achieving AGI-level collaboration [9][12]. Group 3: Evaluation Framework - The framework includes a collaboration engine, an agent graph to structure relationships, and a cognitive module for personalized information and adaptive strategies [12][15]. - It incorporates diverse interaction strategies and six varied evaluation scenarios, simulating real-world team dynamics [19][20]. Group 4: Performance Metrics - The evaluation system uses milestone-based KPIs to assess task completion and collaboration quality, including task scores, communication scores, and planning scores [27][28]. - The findings indicate that high collaboration does not always correlate with superior task outcomes, emphasizing the importance of individual agent capabilities [30][32]. Group 5: Organizational Structure and Team Dynamics - The study highlights that decentralized organizational structures outperform hierarchical ones, which can lead to communication costs and inefficiencies [38]. - The "Ringelmann Effect" is observed, where increasing the number of agents can lead to diminishing returns in performance, underscoring the need for efficient collaboration mechanisms [40]. Group 6: Emergence of Social Intelligence - Notable emergent behaviors, such as strategic silence and trust differentiation, are observed in competitive scenarios, indicating a shift from pure logical reasoning to initial social behavior capabilities in AI agents [43][44]. - The findings suggest that under the right conditions, AI can learn and exhibit advanced social behaviors, marking a significant step towards more sophisticated artificial intelligence [48].