Companies Must Ground AI Strategies in Reality, Avoid Blind Hype

Generative artificial intelligence (AI) is advancing rapidly, accelerating the transformation of industrial ecosystems. However, some enterprises face challenges such as inadequate scenario alignment, talent shortages, and imbalanced input-output ratios in their AI adoption efforts, hindering the large-scale deployment of AI tools. Certain companies treat adopting large models as a standard practice, investing heavily in computing infrastructure, yet the platforms they build often lack practical application scenarios. Others pursue short-term trends without aligning with their core business needs, overlooking long-term value. Empowering industries with AI is an irreversible trend, but companies must avoid disorderly expansion and investment bubbles driven by "AI for AI's sake." Truly effective AI applications should be rooted in core business operations, integrated into real-world scenarios, and leverage existing business data—rather than blindly accumulating software and hardware resources. Small and medium-sized enterprises (SMEs), in particular, commonly struggle with scarce algorithmic talent, high computing costs, and fragmented data, making it difficult to develop end-to-end AI capabilities internally. Off-the-shelf AI tools often underperform due to misalignment with their specific business data. To enhance the real-world impact of AI, it is essential to break down data silos, strengthen technical foundations, and foster collaboration guided by policy support among governments, leading enterprises, and industry platforms. Enterprises should also increase investment in fundamental research, core algorithms, and underlying architectures, integrating industry-specific data, business rules, and operational expertise to build intelligent decision-making systems tailored to their unique needs. AI adoption should not chase short-term hype but instead focus on steadily enhancing long-term market competitiveness through pragmatic, step-by-step implementation.