Policy analysis, methodology and industry observation around real enterprise problems.
Failing tourism projects usually have broken loops between space, operations, content and capital—not a lack of demand. This article explains how to diagnose the real problem, pick a small entry point, and use AI to rebuild the asset into an operable, investable one.
Distressed tourism assets are not worthless; they are simply under-defined. 维策有解 starts with an AI diagnosis and applies small incisions, deep restructuring, and continuous upgrading to turn idle scenic areas into operable, investable industrial assets.
Cities don't lack AI pilots; they lack a structured map connecting real scenarios, data assets, and industrial opportunities. This article explains how to build a city-level AI scenario repository in three steps—inventory, prioritize, and connect—so AI moves from scattered pilots to a genuine engine for industrial upgrade and investment attraction.
AI doesn't predict markets; it rebuilds the chain from information to decision to capital. This article explains how AI helps state-owned platforms and investors rediscover undervalued projects—from diagnosis to post-deal enablement.
For governments and SOEs, human review is not a barrier but a trust line for AI adoption. This article explains why independent checkpoints matter and how to design controlled review processes.
Reports about farmers losing crops due to 'AI' often blame AI as a whole, ignoring which specific model or tool was used. This article argues that vague attribution is unscientific and harms trust in AI, urging media and practitioners to be precise and apply human oversight.
Individuals can use AI models, but enterprises need engineering, integration and continuous iteration. This article explains why enterprise AI reconstruction demands a professional service provider, not just prompting skills.
AI pilot projects in state-owned enterprises often stall at scale due to fragmented data. This article explains why cleaning and structuring data assets must precede model deployment, and how human-in-the-loop review enables a steady path.
Why does AI still seem unreliable to many? Because demos are not deployments. This article explains the gap between AI chat and AI operations, and how small-scope, deep-refactoring, continuous-upgrade makes it work in real projects.
