Core Insights - The financial sector is undergoing a transformation driven by AI, moving from manual processes to automated and intelligent decision-making [2][4][5] - The adoption of AI in finance has been limited until recently due to high costs, but advancements like DeepSeek have significantly reduced these costs, making AI applications viable [4][5] - Despite the potential benefits, challenges such as AI hallucinations and the need for explainability remain significant barriers to widespread adoption in finance [2][12] Cost Reduction and Demand Surge - The financial industry has only recently begun to embrace AI, transitioning from process automation to intelligent decision-making, with a notable starting point being the launch of DeepSeek [4] - Prior to DeepSeek, the cost of using AI for tasks like expense report auditing was significantly higher than manual processes, deterring many companies from adopting AI solutions [4] - After the introduction of DeepSeek, the cost of AI auditing for receipts dropped from 9-10 RMB to 0.6-0.7 RMB, making it more cost-effective than manual auditing [4][5] AI Applications in Finance - AI has begun to empower various financial scenarios, including receipt auditing and expense management, which were previously reliant on manual verification [6][8] - The introduction of AI has enabled companies to handle complex tasks, such as recognizing receipts in multiple languages, which was a challenge for finance personnel [8] - The financial control capabilities of companies are currently at levels L3-L4, with the integration of AI being crucial for advancing to level L5 [8] Intent Recognition and Dynamic Decision-Making - AI has transformed the interaction in finance from manual data entry to natural language processing, allowing for more intuitive user experiences [9] - AI's ability to make dynamic decisions based on various data points represents a significant advancement over previous static rules [9][10] - The shift from task-oriented roles to decision-making roles is a key evolution in the finance sector, as AI takes over repetitive tasks [10] Challenges of AI Implementation - The phenomenon of AI hallucinations poses a major challenge, particularly in finance where accuracy is critical [12] - Hallucinations can arise from outdated data, unreliable online information, and imbalanced data distributions, necessitating robust solutions to mitigate these issues [12][13] - Organizations must overcome cognitive biases and structural inertia to fully leverage AI capabilities in finance [14][15] Organizational Evolution - The successful integration of AI in finance requires a rethinking of organizational structures and roles, moving away from traditional task-based divisions [15] - Financial shared service centers with empowered leadership can effectively implement AI strategies to optimize costs and improve decision-making [15][16]