企业AI应用率提升，投入产出不明显成AI落地首要挑战

Core Insights - The primary challenge for large-scale AI application is the unclear return on investment, which is becoming a significant barrier to its widespread adoption [1][2][3] - Companies are increasingly focusing on the practical value of AI projects, with a core emphasis on improving investment returns [1][2] Group 1: AI Application Challenges - A recent survey by the Australian Accounting Association revealed that over one-third of large enterprises are concerned about the uncertainty of technology application effectiveness, data quality issues, and complex legacy systems [2] - The upfront investment in AI infrastructure is substantial, with some systems costing hundreds of thousands to millions, leading to difficulties in quantifying AI's actual business value [2][3] - Both large and small enterprises face common pain points, including a lack of transparency and explainability in AI outputs, as well as privacy and security concerns [3] Group 2: Strategic Investment Approaches - Companies are advised to focus resources on core business and development strategies, addressing specific business pain points, and matching technology precisely to avoid unnecessary investments [3] - A phased investment approach is recommended to balance costs and risks, along with establishing governance mechanisms covering the entire AI lifecycle [3] Group 3: Talent and Workforce Implications - The survey indicated that 32% of respondents reported a reduction in hiring entry-level accounting staff, while 18% expanded their finance departments to include professionals with AI expertise, reflecting a shift in talent acquisition strategies [4] - Companies should enhance existing employee training to improve AI application capabilities and create a clear framework for AI usage to ensure compliance and efficiency [5] - The long-term success of finance professionals will depend on their ability to combine traditional expertise with technological skills and a mindset geared towards lifelong learning [5]