Traditional business education, grounded in disciplines such as economics, management, and psychology, has long emphasized experiential analysis and qualitative decision-making. However, the advent of artificial intelligence is reshaping commercial practice, driving a shift toward data-driven and intelligent decision-making, and continually redefining the scope and boundaries of business education.

In business practice, traditional decision-making has primarily relied on market research and expert experience. However, AI technologies enable enterprises to build high-precision models using vast amounts of data, allowing for more accurate trend forecasting and optimized decision management. In the future, business decision-making will align more closely with the principles of “computational social science, transitioning from experience-based to data-driven approaches, from qualitative assessment to quantitative validation, and from static analysis to dynamic optimization, ultimately serving as a critical bridge connecting technology, business, and society.

As business practices undergo this transformation, the talent cultivation model in business education requires a systematic transformation. With AI systems increasingly capable of handling routine management decisions autonomously, business education must reassess the irreplaceable value of human managers. Going forward, business education should focus on developing interdisciplinary talent—individuals who are well-versed in business principles and adept at navigating AI systems. Broadly, this competency framework can be envisioned as a three-tiered pyramid:

• The foundational layer is “digital survival skills”, including data literacy and an understanding of algorithms;

• The middle layer represents “business augmentation capabilities”, such as AI-powered strategic analysis and intelligent decision-making;

• The top layer is “uniquely human capabilities”—encompassing  ethical judgment and innovative leadership.

The dual transformation of talent development philosophy and training models will further drive a generational shift in teaching approaches. In the age of digital intelligence, business education must evolve from unidirectional instruction to multidimensional interaction, and from experience-based simulations to real-world decision-making. In the future, generative AI-powered “real-time case generation systems” may be able to dynamically create supplementary materials based on classroom discussions, compute case data, and even simulate competitive market scenarios, which will dramatically enhance the depth and adaptability of case-based teaching.

Correspondingly, the evaluation system in business education must undergo transformation in three key aspects:

• In terms of evaluation content, the  focus should shift from assessing “knowledge  retention” to evaluating “the  ability to apply analytical tools to solve real-world problems”;

• In terms of evaluation methods, the  approach should move from "end-of-course assessments" to "ongoing formative evaluations throughout the course”;

• In terms of evaluators, the model should evolve from "teacher-only  assessments" to "evaluations supported by multi-intelligent systems."