Generative artificial intelligence management thinking : Two characteristics of generative artificial intelligence

 Generative artificial intelligence management thinking

Today, as the business environment becomes increasingly complex and the pace of enterprise development continues to accelerate, an enterprise may experience four stages of entrepreneurship, growth, maturity, and transformation in just a few years, and enter the development cycle of the second entrepreneurship, growth, and maturity. middle. These enterprises will face four combinations of uncertain development direction and discontinuous development paths at the same time, and the various businesses of enterprises will also be divided into four types: innovation, growth, maturity, and transformation. In such a complex situation, how to coordinate various businesses, how to grasp the emerging development directions, and how to change development paths in a complex environment are major challenges for enterprise management decision-making.

 

In the era of the Internet where information is diverse and readily available, planned management tends to cause companies to ignore abundant external signals, resulting in organizational rigidity and missed opportunities for development. And too many market signals will also lead to random wandering and disorderly spread of enterprises adopting emergent management in the market, seeing multi-directional development paths but unable to stick to them. Therefore, enterprises that lack the ability to process massive amounts of information at high speed will find it difficult to create optimal performance no matter which one of adaptive, visionary, planned, and emergent management is adopted. Facing the complex external environment and multiple business combinations, enterprises can learn from the logic of generative artificial intelligence technology and try the logic of generative artificial intelligence management.

 Two characteristics of generative artificial intelligence

 

The basic logic of generative artificial intelligence management is from divergence to convergence, that is, from "birth" to "formation". In the divergent stage, the generative artificial intelligence management system obtains massive information from various businesses, pre-trains the enterprise decision-making model, enables it to learn the laws and patterns in the data, and then predicts the subsequent business direction and future status of the enterprise. In the convergence stage, the trained model enters the decision-assisting simulation stage, and the generated data is used for managers’ reference. At the same time, the manager evaluates the decision-making scheme output by the model, so as to facilitate the subsequent improvement of the model and the quality of the generated results.

In practice, the combination of bottom-up and top-down decision-making methods adopted by enterprises has the shadow of generative artificial intelligence management. For example, when discussing the transformation of Huawei's decision-making system, Ren Zhengfei said, "I don't know how many resources are suitable for the front line. I can only let people who can hear the gunfire call for artillery fire, because he is the closest to the customer. Everyone listens to him first and chooses to believe first." He. We found out that ammunition was wasted during the review after the event, and then "settled accounts after autumn" and summed up experience." Every time Huawei's front line called for artillery fire was a bottom-up strategic output, and the subsequent review of the management and settlement after autumn belonged to an automatic Top-down corrective returns. The goal of artificial intelligence is to label a large number of revised returns, and to establish a system foundation for the organization to realize the self-training of "output→feedback→adjustment→output".

When an enterprise follows its own life cycle, it develops from the entrepreneurial stage to the growth stage, and then enters the maturity stage and transformation stage. The uncertainty of the future development direction and the discontinuous combination of the future development path faced by enterprises at each stage are different, but the combination similarity in each stage is relatively high. There are also rules to follow in the changes from one stage to another. In this case, enterprises can make management decisions according to the thinking modes of adaptive management, visionary management, planned management and emergent management.

However, with the continuous development of enterprises and rapid changes in the external environment, many enterprises have a combination of innovative business, growth business, mature business and transformation business at the same time. These businesses face uncertainties in their development directions and discontinuities in their development paths. In order to cope with the opportunities and challenges faced by various businesses, enterprises will serve different types of users, adopt corresponding organizational structures, develop products that meet user needs, and try to compete and cooperate in different market environments. So the complexity of enterprise management began to rise exponentially.

From the perspective of the corporate headquarters, each business faces different specific situations, and it is impossible to adopt a unified management mechanism and method for management, and it is difficult to achieve synergy among businesses. From different business perspectives, due to the different specific situations faced by each business, it is often difficult to meet the requirements of the company headquarters, and it is also difficult for each business to form synergy with other businesses.

The generative formula includes two keywords, one is "sheng" and the other is "cheng". The basis of the overall management objectives of the enterprise is the management objectives of each business, and the management objectives of each business include user management, organization management, product management and market management. Due to the differences in the development stages and specific development conditions of different businesses, these businesses will generate massive amounts of information in actual operation. Enterprises can use the pre-trained management model to label the management problems and data information faced by each type of business, and continuously optimize the model through the evaluation of managers to form a bottom-up management information collection system.

The second keyword of generative artificial intelligence management is "success", which includes the achievement of various business goals and the achievement of the company's overall goals. Since the management objectives of each business include specific objectives such as user management, organization management, product management and market management. Therefore, the aggregation of these objectives should achieve the overall management objectives of each business, and the aggregation of the overall objectives of various businesses at different development stages should be able to achieve the overall objectives at the company level. The top management of the enterprise can evaluate and rank the achievement of each business goal from top to bottom, and adjust the company-level return model, so as to promote each business to achieve its own goals and help the company achieve its overall management goals.

 

In the generative artificial intelligence management model, in addition to the two key words "sheng" and "cheng", which respectively represent bottom-up and top-down management processes, "artificial" and "intelligence" are also important Key words. "Intelligence" emphasizes that enterprise management is a science that can deal with uncertainties in management through the formulation of rules. Use the certainty of the rules to deal with the uncertainty of changes, and use the continuity of the process to deal with the discontinuity of behavior. The key word "artificial" emphasizes that management is an art, even a craft. Managers are required to intervene moderately in the management system, including but not limited to evaluation of management data, feedback on management behavior and optimization of management models.