Keywords: digital transformation; chat gpt；ai
Introduction: GPT technology is not perfect, but process and management are more worrying
Continuing from the previous part, with the popularity of GPT technology, many well-known software development companies in China have also joined this field and launched their own AI models, such as Wenxin Yiyan. With years of digital experience, Wuque Technology would like to talk about the concerns and expectations of GPT technology in enterprise software from the perspective of practical application
First,When enterprises choose to embed GPT technology in their own application software, they should not simply change the packaging of the built-in search engine in the system, but should have substantial improvement and innovation.
Second,Although it is relatively simple to use GPT technology within an independent system, it will face complex problems such as data security compliance and interface connection when applied across systems, which requires practical solutions.
Third,Although some enterprise software has embedded models in the system, in view of the security of data assets, looking at Samsung's bloody lessons, the cost of computing power required for privatization deployment cannot be ignored.
Fourth,On the contrary, some enterprises often do not connect to the Internet due to the special requirements for data security in the application environment, which limits the opportunity for GPT technology models to obtain more training scenarios. But can domestic software companies continue to optimize and improve large-scale model technology according to the specific needs of customers? This is a problem. At the same time, this puts forward a higher standard for the technical service support ability of enterprises.
Fifth,Whether the machine learning capabilities of large models are advanced enough and whether they can reduce manual intervention is another challenge to the technical strength of enterprises themselves.
Sixth,For enterprise application software, Wuque Technology expects GPT technology to bring convenient services to enterprises in terms of front-end operation and back-end operation and maintenance. For example, on the front end, as long as the leader says to the built-in GPT intelligent assistant: "I need a monthly business report", then the system can automatically sort out the report, analyze and evaluate its content, point out risk points and suggestions for improvement; In the background operation and maintenance, if the administrator orders to "close a user's permissions and transfer them to a certain user", the GPT intelligent assistant can also automatically complete the complex permission adjustment work, greatly improving the efficiency and accuracy of background maintenance. In addition, for professional technical requirements, such as writing SQL statements, GPT bots can also automatically generate and execute them, thereby reducing the technical difficulty of back-end operation and maintenance and saving the operation and maintenance costs of enterprises.
Before introducing AI, companies have some work to do
Although GPT technology is theoretically expected to improve work efficiency and change the way of working, the key behind this is whether enterprises can effectively integrate this cutting-edge technology with their own management and operations. It's like going back to the core of the digital revolution: how to bring technology and business together. A: Improve management and optimize processes! Enterprises should understand that no matter how advanced technology is, there is no magic power to turn stones into gold. Business leaders should not be overly superstitious about the advantages of technology, because high technology may not necessarily bring about productivity improvement, and if you do not manage and sort out the process before that, it may limit the existing production model, and AI will change from a sharp weapon to a murder weapon.
When enterprises consider the use of GPT technology, which scenarios are suitable and how to use it, this requires all employees to participate in learning and improving. New technologies are not only to make everyone's work more convenient, but more importantly, to improve the overall management and operation capabilities of enterprises. And the premise of all this is that there must be sufficient and high-quality data within the enterprise Quality Author List as a support, if there is a lack of data or messy data in actual work, even if GPT technology is smart, there may be nothing to be done in the face of such a situation, and finally become a waste or a shit-stirring stick. 
Editor in charge: Flying Rabbit.
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