Core Viewpoint - Worldline is strategically divesting its Mobility & e-Transactional Services (MeTS) business line and selected Financial Services activities to Magellan Partners, aiming to refocus on its core payment services [1][2][3]. Company Overview - Worldline, a global leader in payment services, generated €4.6 billion in revenue in 2024 and is focused on enhancing its digital payment solutions [17]. - Magellan Partners, founded in 2008, specializes in consulting and technology, with an expected revenue of approximately €420 million in 2025 [9][18]. Transaction Details - The proposed enterprise value for the transaction is up to €410 million, with a binding offer of €400 million plus a contingent consideration of €10 million based on 2025 performance [4][5]. - The MeTS business line is projected to have a turnover of approximately €450 million in 2024 and includes around 3,800 employees across several countries [3][21]. Strategic Rationale - The divestment will simplify Worldline's operations, allowing for optimized resource allocation and increased focus on core payment activities [5]. - The separation is expected to enhance the growth potential of MeTS under Magellan Partners, leveraging their expertise in technology and consulting [6][10]. Market Positioning - The integration of MeTS into Magellan Partners aims to create a European leader in consulting and technology, with a combined workforce of over 6,700 employees and a turnover of €900 million in 2025 [11]. - The new entity will focus on delivering advanced digital services across various sectors, including health and public services [9][10]. Future Outlook - The transaction is expected to close by the end of the first half of 2026, subject to customary approvals and social processes [15][19]. - Both companies express confidence that the collaboration will drive innovation and growth, enhancing their service offerings in the digital transformation space [12][13].

新华财经北京7月29日电（记者闫鹏）28日，2025世界人工智能大会期间，毕马威中国举办"人工智能驱 动创新商业文明生态论坛"。论坛聚焦技术前沿、企业转型与产业重构三大维度，从破解企业"不敢转、 不会转"的实践困局出发，着力为全球AI（人工智能）深水区探索提供可复用的中国方案。毕马威中国 及人工智能领域的专家、企业领袖、学界和政界代表等齐聚一堂，探讨人工智能技术前沿发展及机遇挑 战。 论坛上，毕马威中国首次公开全球调研成果与前沿AI工具——毕马威智速转型平台（KPMG Velocity Platform），现场解读《新智启新质：生成式AI赋能产业变革的实践与路径》，并正式启动第十届中国 金融科技企业双50评选。 AI驱动商业文明新范式 解锁企业高效转型密码 作为论坛的重要亮点之一，毕马威中国在此次论坛上首次公开发布了"智速转型平台（KPMGVelocity- Platform）"。该平台覆盖企业转型全生命周期的智能引擎，结合毕马威中国的行业洞察、人工智能、数 据、ESG（环境、社会与治理）、信息安全的转型资产等资源，通过AI赋能的平台交付，整合了方法 论、资产、赋能工具和技术等。 这一平台核心价值在于支持多模 ...

Group 1 - The core viewpoint emphasizes the imbalance between value extraction and risk management in the context of generative AI, highlighting the need for companies to act quickly to harness AI's potential for sustainable business value [1] - Deloitte China suggests that effective generative AI governance requires clear responsibilities, enhanced skills, and a comprehensive risk management process throughout the AI lifecycle [1][2] - Companies are shifting from questioning whether to adopt generative AI to focusing on how to implement it effectively, with a critical need to prioritize investments in verifiable return cases [1][2] Group 2 - The AI transformation process in enterprises typically involves four stages: establishing an AI strategic vision, pilot exploration, deep integration into core business processes, and financial mapping [2] - During the pilot exploration phase, companies face challenges in quantifying value and ensuring collaboration between IT and business departments [2][3] - The final financial mapping stage connects AI investments directly to financial metrics, although companies still encounter difficulties in quantifying indirect benefits like customer satisfaction [2] Group 3 - A "light architecture" approach is recommended, which encapsulates AI capabilities as API services to reduce the burden of core system reconstruction [3] - Companies should maintain flexibility in technology selection and establish agreements with technology vendors to mitigate costs associated with potential technology shifts [3] Group 4 - The concept of "illusion" in AI outputs, which can mislead business decisions, is identified as a significant risk, necessitating the establishment of a "trustworthy AI framework" to implement multi-layered defenses [4][5] - Structural illusions, which manifest in seemingly accurate outputs that are actually based on flawed data, should be prioritized for resolution due to their high risk [5] Group 5 - The board of directors is advised to redefine the value boundaries of "human-machine" collaboration, positioning AI as an "enhancement tool" rather than a replacement [6] - In media applications, AI should initially be deployed in low-risk scenarios, with ongoing training and feedback mechanisms to improve its reliability [6] Group 6 - Effective AI governance should transition from a passive to an active approach, involving clear strategic goals, dedicated governance teams, and the use of explainable AI technologies [7] - Companies are encouraged to integrate AI into their core operations rather than treating it as an add-on, which can enhance revenue through improved customer satisfaction and compliance [8]

OpenAI发布具备自主思考能力的ChatGPT智能体；零一万物发布企业级Agent智能体"万仔"……一时之 间，AI Agent（AI智能体）正从概念加速落地，国内外科技巨头纷纷布局。 7月28日，在2025世界人工智能大会暨人工智能全球治理高级别会议（WAIC 2025）上，安永大中华区 发布AI Agent产品——安永智能问答3.0。安永大中华区人工智能与数据咨询服务联席主管合伙人陈剑光 在接受《每日经济新闻》记者（以下简称NBD）采访时表示，衡量AI Agent"好用"的关键指标，需兼顾 技术效能与业务价值。 陈剑光强调，AI Agent是否"好用"，技术上需关注准确性、响应速度等指标，业务上则需关注效率提 升、成本优化、风控增强等实际价值，需体现对业务目标的支撑度。 除此之外，不同行业的Agent 需求差异显著。 不过，各行业在人事、行政等职能部门存在共性需求，核心是提升运营效率。 谈Agent应用：存在两大核心痛点 NBD：目前企业在落地Agent应用时，最常遇到的痛点是什么？从您的实践经验来看，这些痛点背后的 核心原因是什么？ 这些痛点的背后，核心原因在于双重壁垒。一是技术整合壁垒。打通异构系统 ...

Core Viewpoint - The integration of ESG (Environmental, Social, and Governance) practices into corporate strategies is becoming increasingly important for high-quality development in Chinese listed companies, especially in the context of the "dual carbon" goals and sustainable development initiatives [1][2]. Group 1: Current ESG Practices - The ESG report disclosure rate among A-share listed companies is continuously increasing, with a notable trend of "rapid expansion and structural differentiation" [2]. - Leading companies are aligning their ESG report quality with international standards, and there is a growing integration of ESG reports with annual reports and social responsibility reports [2]. - Chinese enterprises, particularly state-owned ones, are developing unique ESG governance models that align with national policy directions [2]. Group 2: Future Changes in ESG Ecosystem - The ESG ecosystem in China is expected to undergo significant structural changes, with enhanced regulatory frameworks and mandatory disclosure requirements [3]. - Investors are increasingly prioritizing ESG information in their decision-making processes, which will compel companies to improve their ESG management capabilities [3]. - Collaboration among various stakeholders, including government, enterprises, investors, and professional service institutions, will be crucial for promoting ESG practices [3]. Group 3: Recommendations for Companies - Companies are advised to strengthen the top-level design of their ESG strategies and integrate ESG principles into their overall development strategies [4]. - There is a need for improved internal management systems for ESG data collection, analysis, and management to enhance data quality and transparency [4]. - Engaging with stakeholders and fostering a culture of ESG awareness among all employees is essential for effective ESG implementation [4]. Group 4: Role of AI in Enhancing ESG Reporting - AI technology is becoming a critical driver for improving the quality of ESG reports, evolving from a data processing tool to a comprehensive support system across governance, strategy, risk management, and target setting [5][6]. - AI enhances ESG governance by creating dynamic compliance frameworks and generating oversight checklists for boards [5]. - The technology provides data-driven decision support for ESG strategy formulation and helps in risk management through scenario simulations and real-time monitoring [5][6].

Core Insights - The forum organized by KPMG China during the 2025 World Artificial Intelligence Conference focused on leveraging AI to drive business innovation and transformation, addressing the challenges enterprises face in adopting AI technologies [1][2] - KPMG China introduced the KPMG Velocity Platform, a new AI-driven tool designed to support enterprises throughout their transformation journey, integrating various resources and methodologies [7][10] Group 1: AI as a Strategic Driver - AI is recognized as a critical driver for industrial upgrading and high-quality economic development, with countries globally accelerating their AI strategies [2] - The Chinese government has elevated AI to a national strategic level, implementing comprehensive policies to promote orderly development in the AI sector [2] Group 2: AI Transformation Framework - AI-driven transformation is characterized by deep value creation, innovative thinking, and emotional synergy, requiring a structured approach through the stages of empowerment, integration, and evolution [3] - KPMG has accumulated numerous case studies and solutions to enhance the efficiency and sustainability of enterprise transformations [3] Group 3: Industry Applications and Challenges - AI is delivering significant benefits to enterprises, including cost reduction, enhanced data-driven decision-making, and improved analytical capabilities [4] - Many enterprises still lack clear AI strategies and implementation plans, which are essential for realizing continuous value creation [4] Group 4: Financial Services and AI - The financial services sector is undergoing rapid evolution due to AI, transitioning from a supportive tool to an advisory expert role [9] - Financial institutions need to strengthen foundational models and support industry-specific applications to find strategic advantages in the AI landscape [9] Group 5: Capital Market Dynamics - The investment landscape for AI is expected to remain consistent with 2024, with increased interest in robotics and AI software applications [8] - AI and high-tech industries are contributing new momentum to the global IPO market, with many AI companies planning IPOs for further growth [8]

Core Insights - The report by KPMG highlights that China's innovation is paving a new paradigm for generative artificial intelligence (AI) [1][2] Group 1: AI Development Pillars - The development of AI is supported by three main pillars: computing power, algorithms, and data [1] - China's policies, such as "East Data West Computing," aim to centralize computing infrastructure and public cloud capabilities to serve various industries, making AI infrastructure a public service akin to tap water [1] - The open-source foundational model, represented by DeepSeek, stands out globally in terms of capability, cost-effectiveness, and computing density, lowering the barriers for enterprises to utilize large model software [1] Group 2: Data Utilization and Dynamic Capabilities - In the era of large models, it is essential not only to accumulate data but also to deeply understand and extract business value from it [2] - The promotion of data exchange, compliance, and sharing by the government will facilitate reasonable data circulation among institutions and industries, leading to significant changes in digital China [2] - China's advantages in AI extend beyond static resources like data and market size to dynamic capabilities, characterized by efficient social collaboration and execution speed, which enhances the scalability of AI technology deployment [2]

"3.0的产品功能，聚焦企业ESG管理场景，基于ESG披露指引、战略规划方法论、双碳管理经验以及安 永丰富的ESG实践等核心知识体系，提供ESG叙事线一键生成、ESG议题库智能构建、ESG愿景图智能 输出、企业碳核算量化呈现、虚拟通话互动指导等功能，助力企业实现ESG全流程管理闭环，系统性赋 能绿色转型与高质量可持续发展。"安永大中华区ESG可持续发展主管合伙人李菁表示。 安永大中华区人工智能与数据咨询服务合伙人陈剑光称，安永智能问答3.0构建于自研EY METIS平台， 整合 Agent 架构并优化模型组合，提升专业场景下的推理与理解能力。通过智能解析引擎支持非结构化 数据转化与多格式输出，语音交互模块整合上下文与跨域知识，推动智能顾问在专业服务中的深度落 地。 自2025年4月推出1.0版本以来，安永智能问答整合审计、税务、咨询、战略与交易及金融服务五大核心 领域的海量专业知识，构建了高效精准的智能问答能力，显著提升了客户获取精准专业解答的效率。 2025年6月发布的2.0版本则在交互体验和内容覆盖上实现升级，拓展多模态协同能力，增强用户与系统 的互动深度，进一步丰富了智能问答的应用场景。 3.0版本功能 ...

Core Insights - The imbalance between value extraction and risk management in generative AI has become a critical gap for enterprises to bridge [1] - Deloitte emphasizes that generative AI governance is not an option to delay, and companies must act quickly to clarify responsibilities, enhance skills, and integrate risk management throughout the AI lifecycle [1] Group 1: AI Investment and ROI - The AI transformation process typically involves four stages: establishing an AI strategic vision, pilot exploration, deep integration into core business processes, and financial mapping [4] - In the initial stage, there is often a significant gap between management's ROI expectations and reality, with departments pursuing projects independently [4] - The final stage involves linking AI investments directly to financial metrics, although companies still face challenges in quantifying indirect benefits like customer satisfaction [4] Group 2: AI Architecture and Cost Management - Traditional enterprises face challenges such as complex legacy systems and limited budgets, which can be addressed through "light architecture, soft integration, and distributed evolution" [5] - Light architecture involves encapsulating AI capabilities as API services to reduce the need for core system overhauls [5] - Companies should maintain flexibility in technology selection and establish flexible contracts with technology vendors to mitigate cost risks associated with technology shifts [5] Group 3: Addressing AI Illusions and Black Box Issues - "Illusions" in AI outputs can mislead business decisions and compliance, necessitating a multi-layered defense strategy [6] - Structural illusions, which often appear in AI-generated tables and data analyses, should be prioritized for resolution due to their high risk of misleading decision-makers [6] - To quantify hidden costs from these illusions, companies can assess model output accuracy and operational data impacts [6] Group 4: Risk Mitigation in High-Stakes Scenarios - In high-stakes environments like healthcare and finance, a systematic approach to building illusion mitigation mechanisms is recommended [7] - A mixed architecture of small models and expert rules is suggested for better reliability in regulated fields [7] - Detailed logging capabilities are essential for traceability and accountability in AI outputs [7] Group 5: Strategic AI Governance - Effective AI governance should transition from passive to proactive, with clear strategic goals and dedicated governance teams [11] - Companies should adopt explainable AI technologies and data governance tools to ensure transparency and control [11] - Cultivating employee AI literacy is crucial for fostering a responsible AI usage culture [11] Group 6: AI Security and Revenue Impact - Companies should integrate AI into a unified architecture rather than treating it as an add-on to legacy systems [12] - A secure AI system can enhance customer satisfaction and loyalty, indirectly boosting revenue [12] - Real-world examples show that integrating AI into cybersecurity can significantly reduce response times and downtime, leading to revenue growth [12]

Financial Performance - For the quarter ended June 2025, Booz Allen Hamilton (BAH) reported revenue of $2.92 billion, down 0.6% year-over-year [1] - EPS for the quarter was $1.48, an increase from $1.38 in the same quarter last year [1] - The reported revenue was a surprise of -0.54% compared to the Zacks Consensus Estimate of $2.94 billion, while the EPS surprise was +1.37% against a consensus estimate of $1.46 [1] Key Metrics - Booz Allen's total backlog stood at $38.27 billion, below the two-analyst average estimate of $40.23 billion [4] - Revenue from U.S. Government Defense Clients was $1.52 billion, slightly below the estimated $1.53 billion, but represented a year-over-year increase of +7.1% [4] - Revenue from U.S. Government Civil Clients was $923 million, which was lower than the average estimate of $973.22 million, reflecting a year-over-year decline of -13.2% [4] - Revenue from U.S. Government Intelligence Clients was $484 million, slightly above the average estimate of $483.07 million, with a year-over-year increase of +5% [4] Stock Performance - Shares of Booz Allen have returned +11.5% over the past month, outperforming the Zacks S&P 500 composite's +4.6% change [3] - The stock currently holds a Zacks Rank 5 (Strong Sell), indicating potential underperformance in the near term [3]