Core Insights - Most companies are directing their AI budgets towards sales and marketing departments where results are easier to quantify, while less investment is seen in back-end departments like finance and procurement, despite the potential for cost reduction through AI [1][2][3] - There is a significant internal resistance within companies regarding the adoption of AI in finance, primarily due to uncertainties about which data can be accessed by AI and the complexities involved in changing job roles [3][5] - The implementation of AI in financial processes can lead to substantial time savings and increased accuracy in approvals, as demonstrated by the case of Yunhai Yao, where AI reduced approval times and error rates significantly [4][5] Industry Trends - The trend of AI investment is heavily skewed towards areas with immediate and quantifiable benefits, such as marketing, while finance remains cautious due to its stringent compliance and accuracy requirements [2][5] - Companies are increasingly recognizing the need for AI to streamline operations and reduce costs, but many struggle to quantify the financial benefits of their AI investments [5][6] - A report from MIT indicates that 95% of global AI investments have not yielded economic benefits, highlighting a gap between high expectations and actual returns [6] Company Case Studies - Yunhai Yao's CFO expressed a strong need for AI to alleviate the burdensome manual processes in expense approvals, where each employee handles a large volume of transactions [3][4] - The AI approval system implemented by Yunhai Yao has shown to save an average of 4345 minutes per document, significantly improving efficiency and reducing the need for manual checks [4][5] - Companies like Keda Xunfei are focusing on AI applications in financial control, aiming to free up human resources for more analytical tasks rather than manual approvals [5][6]

Core Insights - The majority of companies are directing their AI budgets towards sales and marketing departments, where results are easier to quantify, while less investment is seen in back-end departments like finance and procurement, despite the potential for cost reduction through AI [1][4][5] - There is a consensus across industries that generative AI serves as a decision-making tool that requires computational power and technological iteration, ultimately leading to cost reduction and efficiency improvement [2] Group 1: AI Investment Trends - Companies are hesitant to invest in AI for finance and procurement due to the difficulty in quantifying the results, even though these areas can benefit from cost reductions [1][4] - The founder of a SaaS company noted that many enterprises are caught in a cost dilemma: they fear falling behind if they do not invest, yet worry about not seeing tangible results from their investments [3][4] Group 2: AI Implementation Challenges - The finance department is often the most cautious in adopting AI due to high compliance, accuracy, and data security requirements, leading to a slower pace of AI integration compared to marketing and sales [5][6] - Many companies are still uncertain about which data can be accessed by AI, contributing to a lag in AI transformation within finance [5][6] Group 3: Case Study - Cloudy Yao - Cloudy Yao, a restaurant chain, has a significant portion of its finance team dedicated to expense approvals, with each employee reviewing over 500 invoices monthly, highlighting the cumbersome nature of the process [6] - After implementing AI for expense approvals, Cloudy Yao reported an average time savings of 4345 minutes per invoice, equating to three days, and an 80% reduction in approval error rates [6][7] Group 4: Future of AI in Business - The market expert emphasized that while CFOs are eager to embrace new technologies, quantifying the financial impact of AI investments remains challenging due to the lack of standardized pricing and the shift towards results-based payment models [7][8] - A report from MIT indicated that 95% of global AI investments have not generated economic benefits, trapping companies in a cycle of high investment with zero returns [8]