Key Takeaways
- The architects of AI have been recognized as Time Magazine’s “Person of the Year” for 2025, symbolizing AI’s sweeping influence on global industries.
- AI-driven technologies, including generative AI tools and machine learning platforms, are redefining business operations by enhancing productivity, personalization and decision-making.
- While AI presents opportunities for growth and operational efficiency, businesses are urged to adopt AI responsibly, ensuring security, transparency and ethical standards.
The architects of artificial intelligence (AI) have been named Time Magazine’s 2025 “Person of the Year,” reflecting the far-reaching impact AI has on every industry, company and nation. AI is reshaping business strategies to build a smarter future, significantly improving efficiencies, value and the customer experience. Those who fail to incorporate AI into their business models will ultimately lose relevance and competitiveness.
Generally speaking, AI performs human tasks, enabling machines to learn from data and experience to help businesses make better decisions, offer personalized experiences, streamline operations, enhance productivity, create new capabilities and generate more growth.
Generative AI
Generative AI, such as ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and others, learn from billions of data points and generate content based on human input. It helps businesses create marketing strategies, full-scale marketing campaigns and content for blogs, websites, social media posts and email campaigns. Tools such as Perplexity AI are used for research, and other AI tools are used for coding and software development, product design and prototyping.
These AI tools save you time and money by helping you develop a marketing plan, research the competition, launch new products, create collateral material and boost customer communication and engagement. What once took hours of mining consumer data, conducting research, writing and creating visuals can be done in a fraction of the time with AI and human input.
Generative AI is also being used by businesses to automate customer service with chatbots and virtual assistants that handle complex queries and provide personalized support, offering tremendous operational efficiency and helping ensure requests, complaints and other service-related issues don’t fall through the cracks. At the same time, the service team has more time to focus on growing the business through upselling and other strategies.
Machine learning and prescriptive AI
Machine learning (ML) is a subset of AI that uses algorithms and statistical models to analyze raw data, identify patterns and generate insights. It processes searchable data, like names, purchase histories and website behavior, and analyzes unstructured content, including images, videos and social media posts. Prescriptive AI builds on those insights so you can take action.
On the retail side, for example, businesses gain insight into customer preferences and motivations from their behavior, enabling them to personalize marketing messages that will resonate with individual needs and create data-driven journeys and experiences that make customers feel seen and valued. Retailers know who will open an email, click through and make a purchase based on known behaviors and shopping trends.
In real estate, ML and prescriptive AI are helping developers leverage insights from datasets that include historical sales, demographic shifts, economic indicators and foot traffic to identify the next high-growth neighborhood or determine where properties are undervalued for purchase. Property owners use AI to assess potential development sites, with zoning laws, environmental data and local damage data at their fingertips to choose strategic locations and avoid delays or fines. Additionally, ML powers automated valuation models (AVMs), which provide instant data-backed property valuations by assessing various property attributes and market conditions. This allows for faster, more consistent appraisals and minimizes the impact of human bias.
Manufacturers use ML data and prescriptive AI to predict maintenance schedules, forecast equipment failures and optimize supply chains and production processes. Manufacturing operations can use real-time data to make quick, informed decisions to minimize errors, reduce waste and enhance productivity.
The insurance industry uses historical data to help determine underwriting risk patterns, whether an account is likely to have a loss, and if it financially makes sense to insure it. For example, one company used machine learning to develop a crash-prediction model for the trucking industry.
By correlating insured data, claims and millions of records from government and other proprietary data sets, the company can predict whether a trucking account will have a crash. Using machine learning, it can accurately predict the highest-risk accounts. It can ultimately avoid writing business with customers who have high crash scores or can provide credits or discounts to trucking clients with low scores, ultimately resulting in better loss ratios.
Agentic AI and autonomous decision-making
Agentic AI analyzes unstructured data to enable real-time decision-making without ongoing human prompting, understands complex workflows to improve execution and results and interacts with other systems to adapt to evolving conditions.
For example, agentic AI can automate payroll logistics, adjust supply chains based on real-time data to improve efficiency and margins, help healthcare organizations interpret physician notes and medical images to adjust treatments in real time and reduce misdiagnosis rates across healthcare, auto care and other service organizations. Law firms are using agentic AI to autonomously review unstructured legal documents and contracts to support preparation and risk identification.
AI is helping businesses build a better future, but it’s important to use it responsibly and with intention. Prioritize security, transparency, fairness and accountability.
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要点总结
- 人工智能的缔造者们被《时代》杂志评为 2025 年“年度人物”，这象征着人工智能对全球各行各业的广泛影响。
- 人工智能驱动的技术，包括生成式人工智能工具和机器学习平台，正在通过提高生产力、个性化和决策能力来重新定义业务运营。
- 虽然人工智能为企业带来了增长和提高运营效率的机会，但我们敦促企业以负责任的方式采用人工智能，确保安全性、透明度和符合道德标准。
人工智能（AI）的架构师们被《时代》杂志评为2025年度人物，这反映了人工智能对各行各业、公司乃至国家产生的深远影响。人工智能正在重塑商业战略，构建更智能的未来，显著提升效率、价值和客户体验。那些未能将人工智能融入其商业模式的企业最终将失去竞争力。
总的来说，人工智能可以执行人类任务，使机器能够从数据和经验中学习，从而帮助企业做出更好的决策、提供个性化体验、简化运营、提高生产力、创造新能力并产生更多增长。
生成式人工智能
生成式人工智能（例如 ChatGPT、Google Gemini、Microsoft Copilot、Meta AI 等）能够从数十亿个数据点中学习，并根据人类输入生成内容。它帮助企业制定营销策略、开展全方位营销活动，并为博客、网站、社交媒体帖子和电子邮件营销活动生成内容。Perplexity AI 等工具用于研究，而其他人工智能工具则用于编码和软件开发、产品设计和原型制作。
这些人工智能工具可以帮助您制定营销计划、进行竞品分析、推出新产品、制作宣传材料，并提升客户沟通和互动，从而节省时间和金钱。过去需要花费数小时挖掘消费者数据、开展研究、撰写文案和制作视觉素材的工作，现在借助人工智能和人工干预，只需极短的时间即可完成。
生成式人工智能也被企业用于自动化客户服务，例如通过聊天机器人和虚拟助手处理复杂查询并提供个性化支持，从而显著提高运营效率，并确保不会遗漏任何请求、投诉和其他服务相关问题。与此同时，服务团队也有更多时间专注于通过追加销售和其他策略来拓展业务。
机器学习和预测性人工智能
机器学习 (ML) 是人工智能 (AI) 的一个子集，它利用算法和统计模型来分析原始数据，识别模式并生成洞察。它可以处理可搜索的数据，例如姓名、购买记录和网站行为，并分析非结构化内容，包括图像、视频和社交媒体帖子。预测性人工智能 (Prescriptive AI) 则基于这些洞察，帮助您采取行动。
例如，在零售领域，企业可以通过分析顾客行为来了解他们的偏好和动机，从而实现营销信息的个性化，更好地满足顾客的个性化需求，并打造数据驱动的购物旅程和体验，让顾客感受到被关注和重视。零售商能够根据已知的顾客行为和购物趋势，预测哪些顾客会打开邮件、点击链接并完成购买。
在房地产领域，机器学习和预测性人工智能正在帮助开发商利用包含历史销售数据、人口结构变化、经济指标和客流量等数据集的洞察，识别下一个高增长区域或确定哪些房产被低估。业主利用人工智能评估潜在的开发地块，他们可以随时获取分区法规、环境数据和当地损害数据，从而选择战略位置并避免延误或罚款。此外，机器学习还为自动估值模型 (AVM) 提供支持，这些模型通过评估各种房产属性和市场状况，提供基于数据的即时房产估值。这使得评估速度更快、结果更一致，并最大限度地减少了人为偏见的影响。
制造商利用机器学习数据和预测性人工智能来预测维护计划、预判设备故障并优化供应链和生产流程。制造企业可以利用实时数据快速做出明智的决策，从而最大限度地减少错误、降低浪费并提高生产效率。
保险业利用历史数据来帮助确定承保风险模式，判断账户是否可能发生损失，以及投保在经济上是否划算。例如，一家公司利用机器学习技术为卡车运输业开发了事故预测模型。
通过关联保险数据、理赔记录以及来自政府和其他专有数据集的数百万条记录，该公司可以预测货运账户是否会发生事故。利用机器学习技术，它可以准确预测高风险账户。最终，它可以避免与事故率高的客户开展业务，或者为事故率低的货运客户提供优惠或折扣，从而降低赔付率。
智能体人工智能和自主决策
智能体人工智能分析非结构化数据，无需持续的人工提示即可实现实时决策，理解复杂的工作流程以改进执行和结果，并与其他系统交互以适应不断变化的情况。
例如，智能体人工智能可以实现薪资物流自动化，根据实时数据调整供应链以提高效率和利润率，帮助医疗机构解读医生笔记和医学影像，从而实时调整治疗方案，并降低医疗、汽车保养和其他服务机构的误诊率。律师事务所正在利用智能体人工智能自主审查非结构化的法律文件和合同，以辅助文件准备和风险识别。
人工智能正在帮助企业构建更美好的未来，但负责任地、有目的地使用它至关重要。应优先考虑安全性、透明度、公平性和问责制。
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