In recent years, artificial intelligence technologies have advanced rapidly across the business landscape. However, for a significant number of Small and Medium-sized Enterprises, the core challenge lies not in whether to adopt AI, but in how to realize its full potential. While many enterprises have begun utilizing automation tools and intelligent systems, longstanding issues such as fragmented internal data, ambiguous business processes and data ownership, and persistent reliance on manual coordination have meant that numerous AI initiatives remain at the exploratory stage.
Yang Zinan, who has long specialized in enterprise data analysis and intelligent operations, has in recent years remained focused on this issue. He asserts that the full value of AI can only be realized when enterprises first establish a robust data infrastructure and clearly defined operational frameworks. If organizations continue to depend on manual spreadsheets, redundant statistics, and fragmented systems, the adoption of AI is unlikely to yield substantive efficiency gains.
Yang Zinan holds a Master’s degree in Business Analytics from Drexel University (USA), and completed undergraduate studies in both Accounting and Business Analytics, graduating with Cum Laude honors. His research has consistently centered on enterprise data analysis, business intelligence systems, and operational decision-making.
In his prior data analysis work, he was extensively engaged in the development and operational analysis of enterprise-level business intelligence platforms across finance, operations, human resources, and marketing, maintaining a sustained focus on the integration of data governance, automated workflows, and AI-assisted operations.
With the progressive integration of AI technology into enterprise business scenarios, Yang Zinan deepened his research on how AI can be effectively embedded within enterprise operational processes. In 2026, he participated in the founding of Stratum Data Solution, with an emphasis on addressing the practical challenges Small and Medium-sized Enterprises face in data integration, intelligent data dashboards, automated workflows, and AI implementation.
Unlike traditional IT consulting models, this team places a stronger emphasis on the concept of 'Operational Clarity,' which entails assisting enterprises in developing more robust data structures, a more unified system of metrics, and more efficient operational processes. In Yang Zinan’s view, what many enterprises truly lack is not data itself, but a system capable of transforming data into sustained decision-making capabilities.
Among these approaches, the multi-agent operations system has become one of the primary areas of focus. This model leverages the collaborative division of different AI roles to structurally manage tasks such as research, code development, project coordination, and operational support, thereby alleviating operational burdens caused by repetitive work and establishing more transparent workflows. The essence of this approach lies not in simply relying on AI tools, but in ensuring that AI is effectively integrated with actual business workflows.
At present, the development priorities of the AI industry are also gradually shifting from model capabilities themselves to the practical application of AI within enterprise contexts. This is particularly significant for Small and Medium-sized Enterprises that lack large-scale technical teams, as achieving data governance and AI integration at lower cost has become a crucial direction for future digital transformation.
Industry experts observe that professionals who both understand enterprise operational logic and possess capabilities in data analysis and AI implementation are becoming pivotal drivers of industry advancement. Yang Zinan’s work—ranging from business analysis and data governance to research on AI workflows and multi-agent operational systems—exemplifies a practical trend in enterprise intelligent transformation: in the future, the key to AI truly reshaping enterprises will not be the technology alone, but whether organizations can establish new operational systems capable of collaborating effectively with AI.
（Reporter: Lin Shuyan）
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