创新背景

人工智能在数据和信息处理方面的强大能力吸引许多企业都陆陆续续引进先进技术，人工智能应用为公司量身定制才能进入企业管理和生产，许多人担忧人工智能会取代人力劳动。

创新过程

在企业管理过程中，人工智能的优势令它能够承担明确定义、重复且不太复杂的任务。人类可以在一秒钟内完成定义明晰且重复的所有事情，而人工智能在相同时间内完成任务的效率要比人类高。例如，人工智能可以从文档中提取发票总额或发票编号，自动指示银行转账。在银行拥有一百万客户时，人工智能完成任务的效率非常高。

慕尼黑大学人工智能管理研究所的所长Stefan Feuerriegel通过研究人工智能在商业、公共组织和医疗保健等方面的应用，肯定人工智能强大工作能力的同时认为，人工智能在企业管理中不能做出决策，它最大的作用在预测。相关论文《将人工智能带入企业管理》发表在2022年的研究期刊《自然机器智能》上。
在做公司合并计划的过程中，只要公司的相关属性可以以数字形式表现，人工智能就可以提供两家公司相似匹配的信息，帮助准确分析公司之间的契合度和合并价值。但无论这家公司是否在追求与另一家公司相同的战略，或者收购一家具有相同重点的公司是否是一个好主意，做出决定的都是经理而非人工智能。因为决策需要衡量的不仅仅是理性客观的数据，还有一些人情思维。人工智能无法处理需要创造力、情感和认知思维的任务，人工在这方面就非常具有价值。

在生成大量数据的地方，比如市场营销公司发送数千封促销电子邮件或跟踪网站访问，人工智能的作用和效率明显优于人工。在处理财务数据的部门或会计和控制中，人工智能可以帮助计算出公司有多少流动性。

人工智能的工作依赖其强大的数据处理功能，但它通常缺乏复杂决策的数据点。它能很快地计算出企业的盈亏，但盈亏资金用在哪？怎么用？决策永远由人类做出，因为当人类提供或看到数据时，潜意识里已经在做决策。在客户争取、产品设计等方面，人工智能可以提供极具意义的数据输入和参考性意见，但不能做出战略部署。

企业管理在运用人工智能的时候，不能将其当作万能工具，需要谨慎行事，对人工智能使用具有一定的风险意识，遵守相应的义务道德标准。比如在人力资源、公共部门和档案等隐私数据方面，使用人工智能必须非常谨慎，因为它的处理能力很可能被破译。管理者不需要完全明白人工智能的算法，但需要了解如何使用它们的某些基本规则。

创新关键点

考虑人工智能的缺陷和风险，在企业管理中正确使用人工智能，利用它的数据处理能力为决策提供可行的预测和建议。

The application of artificial intelligence to enterprises should be based on prediction as the maximum function

In the process of enterprise management, the advantages of AI allow it to undertake well-defined, repetitive and less complex tasks. Humans can do everything clearly defined and repetitive in a second, and AI is more efficient at completing tasks in the same amount of time than humans. For example, ai-powered people can extract the total invoice or invoice number from a document, automatically instructing a bank transfer. When banks have a million customers, AI is very efficient at completing tasks.

Stefan Feuerriegel, director of the Institute of Artificial Intelligence Management at the University of Munich, affirmed the powerful working ability of artificial intelligence by studying the application of artificial intelligence in business, public organization and health care, and believed that artificial intelligence cannot make decisions in business management, and its greatest role is in prediction. The paper "Bringing Artificial Intelligence to Business Management" was published in the 2022 research journal Natural Machine Intelligence.

In the process of doing the company merger plan, as long as the relevant attributes of the company can be represented in digital form, artificial intelligence can provide information about similar matches between the two companies, helping to accurately analyze the fit and merger value between the companies. But whether or not that company is pursuing the same strategy as another, or whether it's a good idea to buy a company with the same focus, it's the manager who makes the decision, not the AI. Because decision-making needs to measure not only rational and objective data, but also some human thinking. ARTIFICIAL intelligence cannot handle tasks that require creativity, emotion, and cognitive thinking, and humans are very valuable in this regard.

Where large amounts of data are generated, such as marketing companies sending thousands of promotional emails or tracking website visits, AI is significantly more effective and efficient than manual. In departments or accounting and controls that handle financial data, AI can help figure out how much liquidity a company has.

AI's work relies on its powerful data processing capabilities, but it often lacks data points for complex decisions. It can quickly calculate the profit and loss of the enterprise, but where is the profit and loss funds used? How to use it? Decisions are always made by humans, because when humans provide or see data, decisions are already being made subconsciously. In terms of customer acquisition and product design, artificial intelligence can provide meaningful data input and reference opinions, but it cannot make strategic deployment.

When enterprise management uses artificial intelligence, it cannot be used as a universal tool, and it is necessary to act cautiously, have a certain risk awareness of the use of artificial intelligence, and comply with corresponding obligations and ethical standards. For example, when it comes to private data such as human resources, the public sector, and archives, the use of AI must be very cautious because its processing power is likely to be deciphered. Managers don't need to fully understand AI's algorithms, but they do need to understand some of the basic rules for using them.