AI不是颠覆者的镰刀，而是进化者的阶梯 | 马蹄友局笔记

This article compiles key takeaways from a closed-door discussion on AI commercialization and implementation led by Ebrun Horse Hoof Community, where more than a dozen entrepreneurs shared real-world conflicts and actionable insights on AI adoption. The core findings are as follows:

1. AI today is not a "disruptor's scythe," but a ladder for business evolution. There exist three gaps between AI adoption and reality: the gap between the intelligent revolution marketed by tech providers and the deterministic returns businesses demand can be broken down into gaps in data, engineering capabilities, and organizational cognition. Companies need neither fear AI nor blindly resist it.

2. Proven practical AI application methods have emerged: in customer service, a three-layer funnel model of "rule-based screening + knowledge base invocation + human fallback" can lift automatic response rates to 85%. AI can also handle routine inquiries, freeing up top-performing agents to resolve high-value customer complaints and optimize cost structures. For cross-border marketing, a "hitch a ride" strategy works well: call on local models to generate draft content, then have human teams adjust for cultural alignment.

3. In the future, AI will shift corporate resource allocation from labor-intensive to cognition-intensive operations. Opportunities will go to players that proactively break cognitive boundaries and dare to get in the game.

This discussion revealed common pitfalls brands face when implementing AI, summarized reusable experiences and projected industry and consumer trends. Key takeaways are as follows:

1. Common challenges for brands adopting AI: When AI generates brand positioning-related content, outputs from different tools are inconsistent, and tuning costs far exceed expectations. For global marketing, training data for Chinese large models only accounts for 10% of global internet data, leaving them lacking understanding of local overseas cultures. As a result, AI-generated content often produces "accurate mistakes" that fail to resonate with consumers. General-purpose AI also lacks industry-specific insights and cannot replace professional judgment directly.

2. Actionable implementation frameworks: Brands do not need to pursue technological perfection in customer service. Instead, they can restructure cost structures with AI: let AI handle 90% of routine inquiries, freeing up top agents to resolve high-value customer complaints for optimal profitability. For global marketing, brands can use AI as a scaffold for cultural understanding via the "hitch a ride" strategy: call on local models from Europe and North America to generate creative drafts, then have human teams complete cultural localization and calibration.

3. In the future, brand competition will shift from competing on organizational scale to competing on human-AI collaboration efficiency. Brands need to proactively restructure their commercial DNA to seize the evolutionary opportunities brought by AI.

This discussion sorted out AI implementation risks, replicable experiences and clear opportunity directions for all types of sellers. Key takeaways are as follows:

1. AI implementation risk warnings: Fully replacing human customer service with large models carries a risk of probabilistic errors. AI-generated brand content has low consistency, and tuning costs far exceed expectations. For cross-border expansion, Chinese large models lack accumulated data on overseas cultures, which often leads to ineffective marketing content. General-purpose AI also lacks industry-specific insights and cannot replace professional judgment directly.

2. Replicable implementation experiences: For customer service, the three-layer funnel model (rule-based screening + knowledge base invocation + human fallback) can lift automatic response rates to 85%. Sellers can also let AI handle routine inquiries, reallocating human labor to high-value complaint resolution to restructure cost structures and cut costs while boosting efficiency. For cross-border marketing, the "hitch a ride" strategy resolves cultural gaps: call on local models in the target market to generate drafts, then have human teams complete cultural calibration.

3. Opportunity outlook: AI will give rise to "super individuals" in the future, and sellers that improve human-AI collaboration efficiency will gain a competitive edge. Sellers need to proactively adjust their resource allocation structures to capture AI transformation dividends.

This discussion on AI commercialization and implementation offers valuable insights for factories advancing digital transformation and accessing e-commerce. Key takeaways are as follows:

1. Core mindset for AI transformation: AI cannot fully replace human labor today, and factories do not need to blindly pursue technological perfection. Instead, they should target deterministic returns and select appropriate use cases to launch AI transformation. The core value of AI lies in helping enterprises restructure resource allocation, shifting operations from labor-intensive to cognition-intensive models and boosting overall operational efficiency.

2. Reference transformation framework: Mature AI implementations all adopt a human-AI collaboration model today, where AI handles standardized, low-value repetitive work and humans take on high-value tasks that require professional judgment. Factories can replicate this logic in production and design: for example, AI can generate initial design drafts and organize user demands, then in-house designers and technicians make professional adjustments, cutting the labor spent on repetitive work.

3. Transformation direction outlook: AI is a ladder for business evolution. Earlier布局 of human-AI collaboration builds stronger competitive advantages. Factories need to break through cognitive boundaries around AI, proactively pursue practical implementation, and capture the business opportunities brought by AI transformation.

This discussion sorted out real pain points enterprises face when implementing AI and summarized viable implementation directions, offering high reference value for various service providers developing products and serving clients. Key takeaways are as follows:

1. Core pain points of enterprise clients today: Between the "intelligent revolution" marketed by tech providers and the "deterministic returns" enterprises demand lie three gaps in data, engineering, and cognition. Most enterprises' internal data assets are like unrefined ore, with extremely high cleaning and refining costs—for example, cleaning conversation data from top customer service agents consumes 40% of total project work. Enterprises have demands for cross-border marketing and industry-specific content generation, but existing large models have clear flaws including insufficient cross-cultural awareness and a lack of professional insights.

2. Scalable practical solutions: There is no need to build conceptual products that promise "full replacement of human labor." Human-AI collaboration solutions better align with enterprise demand. Examples include the three-layer funnel model for customer service and the "hitch a ride" model for cross-border marketing, where AI handles basic work and humans complete final calibration and fallback. These approaches help enterprises optimize cost structures, ensure stable performance, and deliver deterministic returns.

3. Industry development outlook: Corporate competition will shift to competition over human-AI collaboration efficiency, and demand for practical, implementable AI supporting services will continue growing. Service providers should avoid the pitfall of hyping empty technological concepts, and instead develop products and services centered on enterprises' actual business returns.

This discussion sorted out enterprises' real demand for AI services, identified potential risks to avoid in AI development, and offers reference for platforms布局 AI business and optimizing operations and merchant recruitment. Key takeaways are as follows:

1. Core demand of enterprises for AI platforms: Enterprises need platforms that can connect to large model resources from different regions and different fields to address pain points: Chinese large models' lack of overseas data and general-purpose large models' lack of professional insights. They also need platforms to provide supporting services for organizing and processing data assets, helping them cut data refining costs and bridge the three gaps of data, engineering, and cognition for AI implementation.

2. Misconceptions to avoid: Platforms should not blindly hype the concept that AI will fully replace humans. Instead, they should align with enterprises' core demand for deterministic returns, prioritize practical human-AI collaboration implementation solutions, help enterprises cut AI tuning costs, deliver tangible benefits of cost reduction and efficiency improvement, and avoid amplifying anxiety that AI will disrupt the industry.

3. Development opportunities for platforms: Platforms can build a supporting ecosystem centered on practical AI implementation, aggregate implementation experience across different use cases, and help enterprises complete resource allocation and commercial structure restructuring. Just as Horse Hoof Community, the organizer of this discussion, did, platforms can also host industry events around AI transformation to bring together action-oriented enterprises, build platform influence, and advance AI adoption across the industry.

This closed-door discussion documents the real-world landscape of current AI commercialization and implementation, sorts out new trends and new problems in industrial development, and offers high-value first-hand reference for industrial research. Key findings are as follows:

1. New problems exposed in AI commercialization: There is a mismatch between technology-side supply and enterprise-side demand. Between the intelligent revolution marketed by technology providers and the deterministic returns demanded by enterprises lie three gaps in data, engineering, and cognition. Existing large models have three core flaws: probabilistic errors when directly replacing human labor, insufficient cross-cultural awareness (training data for Chinese large models only accounts for 10% of global internet data), and a lack of professional insights for general-purpose AI. AI lowers content generation costs but pushes up testing costs, fundamentally changing how enterprises allocate resources.

2. New trends in industrial development: AI is pushing enterprises to shift from "labor-intensive" to "cognition-intensive" operations, and "super individuals" will emerge in the future: three AI agents can achieve the output that once required 300 human workers. Corporate competition has evolved from competing on organizational scale to competing on human-AI collaboration efficiency. AI is upgrading from process optimization to commercial DNA restructuring, driving species-level evolution across the industry.

3. New practical business models have already emerged: Human-AI collaboration models that do not pursue technological perfection and target deterministic commercial returns have become mainstream, including the three-layer funnel customer service model and the "hitch a ride" cross-border marketing model. These provide real-world researchable samples for AI implementation.