PwC report: Over half of surveyed institutions use AI to improve work efficiency, preferring to assist employees rather than reduce staff

PricewaterhouseCoopers (PwC) published a report on the application of AI practices and their impact on businesses, interviewing approximately 200 financial services professionals in mainland China and Hong Kong. The results show that the surveyed institutions have achieved an initial return of 10% to 15% through AI investments. While they focus on short-term gains, they place greater importance on the long-term value of AI in enhancing market position, expanding strategic development space, and creating new growth opportunities. However, 61% of financial institutions allocate less than 10% of their technology budget to AI, indicating a gap of 30% to 40% between AI investment and actual demand in the industry.

Respondents indicated that the investment returns from AI projects are reflected in reduced risks, improved compliance efficiency, increased revenue, and lower costs. 57% of the surveyed institutions are using AI to enhance employee productivity. The application of AI tends to assist employees rather than being used for workforce reduction.

Additionally, the report points out that the widespread deployment of AI faces many limitations. Respondents believe that talent shortages and rigid corporate structures hinder AI deployment more than budget or technical issues. Only 29% of financial institutions reported successfully establishing an "AI-first" cultural atmosphere. The report mentions that the implementation of AI applications cannot rely solely on technical capabilities; cultural transformation is also a necessary prerequisite. Furthermore, traditional processes and functional silos continue to restrict the promotion of AI.

In addition to talent and corporate culture, data is another major limitation. Respondents identified three main obstacles to AI investment: data availability, regulatory pressure, and the need to prioritize the maintenance of existing core systems. Data security and privacy protection issues are considered the main challenges in data management, leading 90% of financial institutions to rely on internal proprietary data to meet their AI application needs