Artificial intelligence works best within companies when it knows the context in which it is operating. Some of that context is easy to codify upfront, in explicit rules and guidelines.(部分环境信息可以预先用明确的规则和指南轻松系统化。) Some of it can be captured by analysing raw data: Celonis(① 背景：Celonis成立于2011年...), a German software firm, ingests(摄取，吸收（数据）) information from enterprise systems to see how processes like invoices or procurement actually unfold within organisations(在组织内展开/发生).
当人工智能了解其运行环境时，它在公司内表现最佳。部分环境信息可以预先用明确的规则和指南轻松系统化。另一部分则可以通过分析原始数据来获取：德国软件公司Celonis从企业系统中摄取信息，以了解发票或采购等流程在组织内实际如何展开。
But some information is harder to pass on, and none more so than tacit knowledge, the know-how born of experience and intuition.(但有些信息更难传递，隐性知识——源于经验和直觉的专有技术——尤为如此。) Michael Polanyi, a philosopher, famously distilled tacit knowledge(隐性知识) thus: “We can know more than we can tell.” How can AI learn about work when even humans cannot articulate(清晰表达) why they do things a certain way?
但有些信息更难传递，隐性知识——源于经验和直觉的专有技术——尤为如此。哲学家迈克尔·波兰尼曾这样精辟地提炼隐性知识：“我们知道的比我们能说出来的多。”当连人类都无法清晰解释为何以特定方式做某事时，AI又如何学习工作呢？
One answer is that they don’t need to. Tacit knowledge is already embodied(体现，具体化) in the data that machines are trained on: a corpus(语料库) of marketing copy for a specific brand, say, encapsulates(封装，包含) organisational judgment and expertise that models can soak up(吸收). And one of AI’s defining characteristics(定义性特征), says Enrique Ide of IESE(① 背景：Enrique Ide是IES...), a Spanish business school, is its ability to identify patterns in data that humans cannot describe. From facial recognition to chess, AI has proved very handy at mastering activities that it was never explicitly taught.(从面部识别到国际象棋，AI已被证明非常擅长掌握从未被明确教授的活动。)
一种答案是它们不需要。隐性知识已经体现在机器训练的数据中：例如，特定品牌的营销文案语料库就包含了模型可以吸收的组织判断力和专业知识。西班牙商学院IESE的恩里克·伊德表示，AI的定义性特征之一就是它能识别人类无法描述的数据模式。从面部识别到国际象棋，AI已被证明非常擅长掌握从未被明确教授的活动。