The state of AI How organizations are rewiring to capture value Alex Singla Alexander Sukharevsky Lareina Yee Michael Chui Bryce Hall March 2025 Organizations are beginning to create the structures and processes that lead to meaningful value from gen AI. While still in early days, companies are redesigning workfows, elevating governance, and mitigating more risks. O rganizations are starting to make organizational changes designed to generate future value from gen AI, and large companies are leading the way ...

麦肯锡中国消费与零售咨询业务 新常态下的 中国消费 泽沛达（Daniel Zipser） 许达仁（Daniel Hui） 石俊娜（Junna Shi） 陈曦（Cherry Chen） 2025年4月 引言 2025年第二季度以来，中国消费市场步入以个位数增长为特征的"新常态"。分析师指出中国 消费者"信心不足"和"消费降级"，并持续抛出一系列尖锐问题：增长是否已收窄？低迷情绪 是否抑制了消费意愿？消费者是否已普遍开始降级？ 我们的最新研究显示，尽管挑战依旧存在，但实际情况远非某些观察人士所描述的那般黯 淡。2024年底，麦肯锡中国消费与零售咨询业务在中国进行了一项全国性调查，调查对象涵 盖17,000多名中国消费者。我们根据城市等级、年龄、家庭收入等因素，将消费者细分为108 个不同群体。 基于对调查结果的分析，以及麦肯锡全球研究院（McKinsey Global Institute）对消费和城 市化趋势的宏观经济分析，我们重点关注塑造中国消费市场"新常态"的三大主要趋势： 2024年及2025年第一季度，中国国内生产总值（GDP）持续保持约5%的增长。尽管国内消 费总体适中，但仍展现出韧性。旅游、餐饮、食品 ...

Jan Mischke Chris Bradley Olivia White Guillaume Dagorret Sven Smit Dymfke Kuijpers Charles Atkins Ishaa Sandhu Editor Janet Bush Data visualization Juan M. Velasco May 2025 Executive summary The power of one: How standout firms grow national productivity Authors The McKinsey Global Institute was established in 1990. Our mission is to provide a fact base to aid decision making on the economic and business issues most critical to the world's companies and policy leaders. We benefit from the full range of McK ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The gender pay gap is estimated to be 27 percent for both the sample and the broader US workforce, indicating significant disparities in earnings between men and women [19] - The analysis focuses on how career choices and time impact the gender pay gap, emphasizing the importance of occupational trajectories and advancements [19][52] - The study utilizes a comprehensive dataset of over 100 million individuals and job postings to analyze career pathways and wage mobility [2][4] Sample Selection - The study uses a random sample of one million men and one million women from over 60 million gender-identified profiles, focusing on US-based profiles [3][4] - The final sample consists of 35,235 women and 50,529 men, totaling 85,764 individuals, with approximately 36,000 unique job titles [4][5] - The sample skews towards higher-educated workers in higher-paying occupations, reflecting women's lower representation in these roles [9][12] Skill Distance - Skill distance is estimated by analyzing job posting data from 20.9 million aggregated job postings, focusing on the skills required for each role [10][14] - The calculation of skill distance per role move considers the weighted number of new skills compared to the total skills required for the new role [13] Wage Mobility - The report examines wage mobility over a ten-year period, categorizing occupations into quintiles based on average wages [15][16] - It tracks the movements of men and women between occupational wage quintiles, disregarding the gender pay gap within occupations for this analysis [16] Decomposition of the Gender Pay Gap - The gender pay gap is decomposed into differences in starting points, occupational trajectories, within-occupation advancements, and hours worked [19][24] - Women's non-gendered overall average wage at year ten was approximately $82,000, with an 8 percent gap due to differences in occupational trajectories [25] Historical Trajectory Patterns - The report projects labor demand by occupation through 2030, estimating the number of workers transitioning into growing and shrinking occupations [32][34] Types of Companies - The analysis identifies 12,476 unique workers from various company types, comparing human capital outcomes for men and women across these categories [35][37] - The study categorizes companies into four types based on their performance in human capital development and financial results [47]

Core Insights - The report highlights the significant impact of AI and automation on labor markets in Europe and the United States, predicting a shift in demand towards high-skill professions while reducing demand for lower-skill jobs [12][13][24] - By 2030, it is estimated that up to 30% of current work hours could be automated, necessitating around 12 million occupational transitions in both Europe and the United States [12][66][77] - The need for a major skills upgrade is emphasized, with a rising demand for technological, social, and emotional skills, while traditional manual and cognitive skills stabilize [14][15][66] Labor Market Context - Labor shortages and a slowdown in productivity growth are pressing issues in both regions, exacerbated by an aging workforce and the impacts of the COVID-19 pandemic [12][24][26] - The pandemic has accelerated shifts in labor demand, with a notable increase in hybrid work and e-commerce, which are expected to persist [25][41] Occupational Transitions - Europe may require up to 12 million occupational transitions by 2030, which is double the pre-pandemic pace, while the U.S. transitions could align with historical norms [13][66][68] - The pace of occupational change is expected to be uneven, with Europe facing a potential doubling of transition rates compared to pre-pandemic levels [68][69] Skills Demand - There is a projected increase in demand for STEM and healthcare professionals, with expected growth rates of 17-30% in these sectors by 2030 [58][63] - Conversely, occupations in food services, production, and customer service are anticipated to decline, with potential job losses ranging from 300,000 to 5 million in Europe [59][63] Automation and AI Impact - The report indicates that by 2030, 27% of hours worked in Europe and 30% in the U.S. could be automated, driven by advancements in generative AI [77] - The potential for automation is expected to rise significantly by 2035, with projections of 45% in Europe and 48% in the U.S. [77] Productivity Growth - Embracing technology and proactive worker redeployment could lead to productivity growth rates of up to 3% annually in Europe through 2030, while slow adoption could limit growth to 0.3% [16][66] - The report underscores the importance of making strategic choices today to enhance productivity and societal outcomes in the future [16][37]

Investment Rating - The report does not explicitly provide an investment rating for the luxury industry Core Insights - The luxury industry is experiencing a significant slowdown after a period of exceptional growth, with a projected annual growth rate of 1 to 3 percent from 2024 to 2027, compared to a 5 percent compound annual growth rate from 2019 to 2023 [31][35] - Price increases accounted for over 80 percent of the luxury industry's growth during the previous five years, while volume gains were more moderate [30][72] - The luxury client base is becoming more diverse, with younger clients seeking unique experiences rather than just luxury goods, creating new challenges for brands [33][34] Market Backdrop - The personal luxury goods industry grew at a rate of 5 percent per year from 2019 to 2023, with price increases driving much of this growth [53][54] - The luxury sector's economic profit nearly tripled from 2019 to 2024, indicating strong financial performance despite the current slowdown [60][62] - The Chinese market, which grew at 18 percent annually from 2019 to 2023, has been a significant driver of luxury growth, but is now facing macroeconomic challenges [32][53] Strategic Imperatives - Luxury executives are advised to conduct a strategic reset, restore product excellence, rethink customer engagement strategies, bridge talent capability gaps, and futureproof their portfolios to navigate the current market challenges [37][43] - Investment in technology, AI, and data capabilities is essential for uncovering customer insights and personalizing customer journeys [40][45] - Brands must clarify their core values and align on priority clients to sharpen long-term strategies and maintain brand relevance [44][45]

Industry Investment Rating - The report does not explicitly provide an industry investment rating [1][2][3] Core Viewpoints - The report focuses on economic empowerment and how companies can benefit more people by improving income and affordability [1][9][16] - It analyzes 120 economies, covering 90% of the global population, categorized into lower-income (GDP per capita < $5,000), middle-income ($5,000-$20,000), and higher-income (>$20,000) groups [2] - The empowerment line is defined as the private cash expenditure required for basic needs, including a 10% allocation for recreation and a 5% savings buffer [3][5] - The report estimates the share of the population below the empowerment line using consumption and distribution data [6] - It highlights the importance of stable jobs with sufficient wages and affordable essential goods (housing, food, transportation, healthcare, education) for economic empowerment [12][16] Income Analysis - The report uses four labor market metrics: working-age population, labor force participation, unemployment rates, and stable jobs with sufficient wages [12] - For countries with GDP per capita > $10,000, it considers time-related underemployment and low-pay rates, while for those < $10,000, it focuses on formal employment share [13] - The analysis identifies best-performing countries based on labor market metrics and estimates the population that could be lifted to empowerment by improving one element [14][15] Cost Analysis - The report calculates a "lowest-cost line" for essential goods and services, excluding statistical outliers and identifying top-quartile economies [18][19] - It estimates the population that could achieve empowerment through improved affordability by comparing costs to the lowest-cost line [20] Cost-to-Impact Ratios - The report evaluates cost-to-impact ratios for initiatives aimed at economic empowerment, using external data and academic assumptions [21] - It notes that ratios may improve with better targeting or more substantial benefits, such as supporting housing for low-wage employees [22]

Investment Rating - The report emphasizes the need for banks to transition from experimentation to becoming AI-first institutions to unlock material financial value from AI technologies [1][3]. Core Insights - AI is transforming the banking industry, but many banks remain in the experimental phase. To thrive, they must adopt AI technologies enterprise-wide [3][18]. - Successful AI transformation requires investment across multiple interdependent layers of the organization, as underinvestment in any one area can hinder overall progress [4][22]. - Leading banks view AI as a transformational tool, focusing on core strategic priorities such as revenue enhancement and customer satisfaction [22][68]. Summary by Sections AI Transformation Essentials - Banks need to modernize core technology, reimagine customer experiences, and enhance decision-making processes through AI [4][18]. - A comprehensive AI capability stack is essential, including automated cloud provisioning, streamlined architecture, and a central AI control tower [4][54]. Business Areas for AI Transformation - Banks can identify high-impact subdomains for AI transformation, such as customer underwriting, risk management, and sales and marketing [7][12]. - The report identifies that a typical bank has around 25 subdomains that could benefit from AI rewiring, with a focus on those with high business impact and technical feasibility [24][27]. Multiagent Systems - Multiagent systems are highlighted as a key component in automating complex banking workflows, improving productivity by 20 to 60 percent in tasks like credit memo preparation [21][45]. - These systems can act as virtual coworkers, capable of planning and executing tasks, thus enhancing both customer and employee experiences [61][67]. Investment in AI Capabilities - To extract value from AI, banks must invest across the entire AI capability stack, including enterprise data, machine learning operations, and core technology [68][77]. - The report stresses the importance of a full-stack approach that combines generative AI with traditional analytics and digital tools [72][78].

Industry Investment Rating - The report highlights the significant financial opportunity in combating government fraud, estimated at hundreds of billions of dollars annually [1][3] Core Viewpoints - Government fraud losses are estimated at $233 billion to $521 billion annually, representing 3 to 7 percent of federal obligations [3] - Fraud risk is increasing due to sophisticated fraudsters, particularly in programs like tax refunds and pandemic-era unemployment insurance [3] - Effective anti-fraud efforts require collaboration between agencies, legislature, and the public [3] Challenges in Fighting Fraud - Agencies often handle fraud cases individually, leading to inefficiencies and limited impact [5] - Government agencies face capability gaps in recruiting AI talent and managing data pipelines for real-time fraud detection [6] - Many agencies fear embarrassment and budget constraints, which hinder proactive anti-fraud efforts [8][9] Opportunities for Improvement - The banking industry's approach to fraud prevention, including data fusion and probabilistic risk assessment, can serve as a model for government agencies [10] - Agencies like the IRS and CMS have successfully implemented anti-fraud measures, saving billions of dollars [11][17][21] - Coordinated action across stakeholders, including better data sharing and funding mechanisms, is essential for progress [12][26] Key Success Factors - Executive commitment and dedicated anti-fraud teams are critical for overcoming funding and capability challenges [26] - Improved data capture and sharing within and across agencies can significantly enhance fraud detection [26] - Public acceptance of additional verification steps is necessary for the success of government fraud defenses [28] Potential Impact - Reducing fraud losses could eliminate the annual Social Security deficit and fund major government departments [3] - The IRS's anti-fraud efforts reduced fraudsters' success rate from 19 percent to 12 percent, saving $2.7 billion annually [21][23]

Industry Overview - The report focuses on the Applied AI industry, highlighting its growth and potential impact across various sectors [1] Core Investment Thesis - The report does not explicitly state an investment rating but emphasizes the transformative potential of Applied AI in driving efficiency and innovation [1] Key Findings - Applied AI is expected to significantly enhance productivity and operational efficiency across industries, with potential economic impacts in the trillions of dollars [1] - The technology is being adopted at an accelerated pace, with major investments from both private and public sectors [1] Sector-Specific Insights - The report suggests that industries such as healthcare, finance, and manufacturing are likely to see the most immediate benefits from Applied AI integration [1] - Case studies within the report highlight successful implementations of AI in optimizing supply chains and improving customer experiences [1] Future Outlook - The report projects continued growth in the Applied AI sector, driven by advancements in machine learning and data analytics [1] - Emerging markets are identified as key areas for future expansion, with increasing adoption rates expected in the coming years [1]