The first stage, the Copilot stage, uses AI as a plug-in, and the plug-ins are embedded in modules such as forms, processes, reports, etc., and the application is still based on low-code configuration; the second stage, the Pilot stage, uses AI as an assistant, capable of 50% of development tasks, and reconstruction Application development paradigm; in the third stage, the Agent stage, AI will become an agent, authorized to lead the completion of tasks. Low-code has become the engine capability for building enterprise AI applications and reconstructing application forms.
In the process of our rapid embrace of AI, the practice of co-creation with many enterprises has proved that low-code + AI can also drive the preservation and appreciation of enterprise data assets and drive business innovation.
We are working with a securities company to study how to implement AI intelligent data analysis. Based on historical data, we use low-code data analysis capabilities and industry and enterprise knowledge models to achieve automatic correlation of multiple tables, generate reports, multi-dimensional interactive analysis, data insights, etc., so that business can Personnel directly perform intelligent analysis to maintain and increase the value of data assets.
There is a retail company that hopes to realize AI intelligent data integration. Business personnel can complete the integration requirements of historical systems and low-code platforms through natural language, expand the scene boundaries of the historical system, and thereby maintain and increase the value of the historical system.
There is also a live broadcast e-commerce company. They hope that AI can make development easier. Business personnel can directly generate applications through natural language, so that each business can be settled through digitalization and become their core operating capabilities. To some extent, it can also realize human assets. value preservation and appreciation.
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