人工智能是研究、开发用于模拟、延伸和扩展人类智能的理论、方法与技术体系，核心是用从数据中归纳出的概率性模型替代人工编写的确定性规则，能力包括机器学习、深度学习、自然语言处理与计算机视觉。它解决的是规则难以穷举、经验难以规模化复制的问题，并不解决数据本身缺失或场景本身不适配技术特性的问题。
人工智能的核心是从数据中归纳规律，它可以帮助企业实现以下目标：
让经验变成可复用的模型。历史上的判断被沉淀为训练数据，规律由系统归纳而非每次由人重新总结。任务边界与输入形式在设计阶段就被明确，能力与限制同时写进需求文档，避免上线后才发现与场景不匹配。
让处理能力随数据增长而提升。样本积累带来准确率提升，边际投入的回报不再线性递减。
让人从重复判断中解放出来。识别、分类、抽取与生成这类高频任务交给模型，人转向需要判断意图与承担责任的环节。模型以服务接口的形式对外提供，调用方不需要理解内部实现，能力升级可在不改动调用方的前提下完成。
让新的业务形态具备经济性。模型能力被封装成可调用的接口，原先无法经济提供的个性化服务变得可以交付。
总之，人工智能可以帮助企业实现价值创造方式从人工逐条处理到模型批量支撑的转变，把不可规模化的经验转成可复制的能力，提升服务响应速度与一致性，降低单次服务的边际成本，为后续的智能决策奠定基础。
翻译
Artificial intelligence is the body of theory, methods and technology for studying, developing and applying machines that simulate, extend and expand human intelligence. Its core is replacing hand-written deterministic rules with probabilistic models inducted from data, with capabilities covering machine learning, deep learning, natural language processing and computer vision. It solves the problems of rules that are impractical to enumerate and experience that is hard to replicate at scale, but it does not solve the problems of missing data or of scenarios that fundamentally do not suit the technique.
At the heart of artificial intelligence is inducting rules from data, which can help companies achieve the following:
Turn experience into a reusable model. Historical judgments are accumulated as training data, with patterns induced by the system rather than re-summarized by people each time.
Let capability grow with data. More samples raise accuracy, and marginal returns no longer diminish linearly.
Free people from repetitive judgment. Recognition, classification, extraction and generation move to models, while people turn to work that requires intent and accountability.
Make new business forms economic. Model capability is packaged as callable interfaces, so personalized services that were previously uneconomic become deliverable.
In short, artificial intelligence can help enterprises shift value creation from handling every case by hand to supporting them in batches through models, turn experience that cannot scale into capability that can, improve service responsiveness and consistency, lower the marginal cost per service, and lay the foundation for later-stage intelligent decision-making.
