中国AI旅游应用分化加剧：谁在领跑？谁陷停滞？

Core Insights - The application of AI in China's tourism industry is evolving from conceptual discussions to practical implementations, significantly transforming operational methods for both travelers and tourism companies [2][3] - A report presented at the 2025 Global Travel Summit highlights the challenges and trends of AI adoption within tourism enterprises, emphasizing the role of grassroots employees over CEOs in driving AI integration [4][5] AI Adoption Trends - In the first half of 2024, 53% of surveyed companies reported using AI, with a slight increase to 54.1% in the second half, indicating a slow adoption rate in B2B contexts despite frequent media coverage of new models [5][6] - Large enterprises (1,000+ employees) saw a decline in AI usage from 80.6% to 74.4%, while medium-sized enterprises (200-500 employees) increased their usage from 38.5% to 53.3% [6][7] Sectoral Disparities - The AI application rates among tourism companies show a clear three-tier differentiation: - The first tier includes technology-intensive sectors like airlines, which have a high AI penetration rate - The second tier consists of business travel companies and travel tech firms, known for their quick adoption of new technologies - The third tier includes OTAs, tourism boards, and scenic spots, which are lagging behind [7][8][9] Organizational Challenges - Despite individual employees using AI, many companies have not established end-to-end AI workflows, indicating a gap in organizational integration [11] - Over 50% of companies believe that external policies and market conditions significantly impact AI technology applications, highlighting the uncertainty in the current environment [12] Application Focus - 76.3% of companies are prioritizing AI for internal operational efficiency, although some application rates, such as store management and personalized recommendations, have decreased due to perceived cost-benefit issues [12][13][14] - A significant portion of companies (46.8%) believes AI will mature within one to two years, reflecting an overly optimistic outlook on AI capabilities [16][18] Key Recommendations for AI Integration - Companies need to redefine their understanding of generative AI, moving beyond viewing it as a mere IT project aimed at replacing human roles [19] - Successful AI implementation requires overcoming three capability bridges: organizational questioning ability, data leadership, and human-machine collaboration [19][20] - Establishing dedicated AI project management offices and cultural performance metrics can facilitate better integration of AI into business processes [20][23]