AI Popularization Application Method for Inclusive Management Innovation: A Framework Grounded in Cybernetics and Efficiency Theory

Objectives: This paper proposes a systematic, inclusive, and operationally simple AI popularization framework grounded in cybernetics and efficiency theory. The framework enables ordinary users-including those without technical or domain-specific expertise-to leverage large language model (LLM) tools to independently produce professional-grade documents, thereby narrowing the global cognitive and digital divide and advancing goals of educational and knowledge inclusion.Background: Despite the rapid proliferation of generative AI tools worldwide, no unified and user-accessible methodology exists to guide their effective application [1,2]. The resulting reliance on unguided trial-and-error imposes significant cognitive costs, particularly for non-expert users [3,4]. Simultaneously, the global professional document creation market—spanning commercial templates, copywriting, and outsourcing services-continues to erect economic and knowledge barriers that exclude vulnerable and low-resource populations from the dividends of AI technology [5,6].Methodology: Drawing on Wiener's cybernetic principles of feedback-regulated goal attainment [7], Shannon's information theory [8], and knowledge-work efficiency models [9,10], this paper constructs a progressive, stepwise AI interaction protocol applicable from beginner to expert proficiency levels. The framework is validated through four illustrative document-creation cases of increasing analytical complexity.Results: The proposed framework enables structured, zero-threshold AI-assisted document production. Case demonstrations across weekly work reports, project progress reports, annual summaries, and industry analysis reports show marked improvements in output coherence, precision, and user self-efficacy relative to unstructured AI interaction.Conclusion: The AI popularization framework presented here reconstitutes the locus of professional document creation from institutions and intermediaries toward individuals. It has meaningful implications for global AI literacy education, digital equity policy, and the restructuring of knowledge-work markets. Controlled empirical evaluation constitutes the immediate priority for future research.