Group 1 - The core idea emphasizes the importance of "precise matching" between existing data resources and real business problems or opportunities to unlock the value of artificial intelligence (AI) [2][3] - Companies are currently seeking practical AI use cases that can provide insights, enhance efficiency, and potentially transform business landscapes, but this process is complex and requires continuous experimentation and investment in technology and talent [2][3] - There is no clear definition of what constitutes a qualified AI use case, as perspectives vary between business executives and technology providers [2][3] Group 2 - A high-quality AI use case originates from a "precise matching" action, exploring the intersection of data resources and specific business problems or opportunities [3][4] - Companies face challenges such as poor data quality, insufficient data preparation, and communication barriers between executives and data science teams, which complicate the design of valuable AI use cases [3][4][5] - Four key principles should be followed during the design phase of AI use cases to avoid common pitfalls and enhance project efficiency [3][4] Group 3 - The first key principle is to precisely match the type of AI project to the business problem or opportunity, ensuring clear definitions of project characteristics at the outset [4][5] - AI experiments are typically small-scale and time-limited, aimed at validating specific hypotheses, while concept proofs (POCs) and pilot projects focus on testing AI applications under controlled conditions [4][5] - Successful AI use cases serve as the starting point for projects, providing business context and evaluation criteria for subsequent initiatives [7][10] Group 4 - Successful AI use cases typically exhibit characteristics such as iterative matching between business problems and data sets, clear milestones, and defined key performance indicators (KPIs) [10][11] - High-level executives often play a crucial role in driving projects and ensuring alignment with overall business strategy [11] - The development of AI use cases should be driven by business needs, particularly when new technologies emerge or when compelling business cases are required for high-cost transformation projects [7][11] Group 5 - The second key principle involves determining the matching key points, where the relationship between business problems and data needs to be clearly defined [14][15] - Existing or accessible data sets can serve as good entry points for developing AI use cases, allowing valuable patterns to be uncovered [16] - The matching process between data sets and business problems is complex and requires ongoing evaluation and adjustment [17][18] Group 6 - The third key principle focuses on an iterative matching process, emphasizing the importance of cross-functional teams that combine data science with business domain knowledge [19][21] - The execution of AI use cases should have clear endpoints to avoid project scope creep and ensure organizational learning [21] - The fourth key principle stresses the importance of planning for the expansion of AI use cases early in the process to realize their full potential [22][25] Group 7 - Successful use cases should address repeatable problems suitable for long-term AI solutions, supported by adequate resources and a stable technical infrastructure [23][30] - Companies can effectively manage multiple use case projects simultaneously by adhering to established rules and governance structures [24] - Focusing on scalability from the outset is crucial for transitioning AI use cases from exploration to production, ultimately driving long-term business value [25][30]