从虚拟到可行：首席财务官如何重新规划人工智能的应用

Core Insights - Many AI projects fail to deliver expected results, prompting CFOs to refocus on three core elements: business value, data foundation, and employee engagement [2][3][4] Group 1: Business Value - Companies must clearly define the business value they expect to achieve through AI, focusing on solving quantifiable business challenges rather than pursuing technology for its own sake [5] - Successful companies prioritize practical, actionable business problems, leading to measurable outcomes, such as increasing annual revenue from $1 million to $1.3 million through targeted AI-driven marketing strategies [5] - A focused and pragmatic approach allows companies to accumulate incremental successes, fostering internal momentum for larger initiatives while minimizing high-cost trial-and-error risks [5] Group 2: Data Foundation - The second key dimension for successful AI implementation is data quality and accessibility, as the effectiveness of AI models is highly dependent on the quality of input data [8] - Companies often face challenges in data volume, diversity, and structure, which can hinder AI training [8] - Data collaboration platforms enable organizations to train AI models while ensuring privacy, allowing for the analysis of data without transferring it, thus addressing the critical issue of high-quality training data scarcity [9] Group 3: Employee Engagement - The third dimension, personnel, is crucial for the success of AI projects, as public concerns about job displacement by AI can lead to resistance [12] - Companies must communicate the core message that AI is meant to enhance human capabilities, not replace them, to alleviate fears and build trust among employees [12] - Successful AI initiatives emphasize communication and change management, requiring CFOs and executives to engage stakeholders early and maintain ongoing dialogue to ensure smooth transitions [12][13]