Summary of Key Points
This article addresses the value of “AI + industry” vertical products by analyzing 20 different products. It proposes a “Triple Illusion Model” to identify common pitfalls and a “Three-Question Framework” to assess true value. The article argues that truly valuable vertical AI solutions should possess three core characteristics: a unique data barrier, a end-to-end workflow, and a guarantee of certainty. The main takeaway is that the value of AI vertical products does not lie in simply “clothing” general-purpose models with industry-specific interfaces; rather, it lies in doing things that general-purpose models cannot or do poorly.
Detailed Analysis
The Three Illusions: Common Pitfalls in AI Vertical Products
Many “AI + industry” products seem promising but actually fall into three common traps:
1. Prompt Illusion: The misconception that adding a prompt such as “Act as a lawyer” makes it a vertical AI solution. For example, an AI legal service may charge $199 per month, but its core functionality is merely providing a general-purpose model with a prompt like “You are a professional lawyer.” However, users can achieve similar results using free general-purpose models, leading to rapid user churn due to the lack of a competitive advantage.
2. Pseudo-Need Illusion: The product offers a good user experience, but users do not use it frequently. For instance, an AI meeting summary tool may have advanced features and high accuracy, but most users attend few meetings per week and can complete the task manually in just two minutes, making the tool unnecessary. Tracking data shows that only 13% of highly active users remain after 12 months, indicating a smaller market scale than expected.
3. Substitution Superiority Illusion: These products claim to be a better alternative to human professionals, but in reality, they are often inferior to free general-purpose models. While in traditional industries, specialized tools (e.g., professional surgical knives) outperform general-purpose ones (e.g., Swiss Army knives), the capabilities of general-purpose AI models continue to expand, squeezing the market for vertical products.
The Three-Question Framework: Quickly Assessing Product Value
Three simple questions can help reveal the true value of an AI vertical product:
1. Equivalence Test: Can the same task be accomplished using a general-purpose model with similar results? For example, if 10 users evaluate your AI teaching tool and a general-purpose model equally well (on a 10-point scale), then the product lacks a competitive advantage.
2. Sustainability Test: How long will users continue to use the product? A retention rate of less than 30% after 6–12 months suggests that the product’s appeal is based on novelty rather than genuine need. A retention rate of over 50% indicates a real market opportunity.
3. Superiority Test: Does the product offer features that general-purpose models do not? Identify three core functions of the product and determine whether general-purpose models can perform them. If more than two of these functions can be replaced by general-purpose models, the product’s value needs to be re-evaluated.
Truly Valuable Vertical AI: Three Essential Characteristics
Products that pass the three-question framework typically possess at least one of the following characteristics:
1. Unique Data Barrier: Access to real-time or exclusive data that general-purpose models cannot obtain. For example, legal products may access continuously updated case databases, medical products patient data, and financial products real-time market information. General-purpose models’ training data has expiration dates and does not include such up-to-date, specialized information.
2. End-to-End Workflow: The product can complete the entire task, not just generate output. For instance, an AI tax solution should not only calculate taxes but also directly submit them to the tax system; an AI legal service should not only draft documents but also handle court filings. Such deep integration requires connectivity with external systems, which general-purpose models cannot achieve.
3. Guarantee of Certainty: The product meets the compliance and accountability requirements of enterprise-level customers. For example, financial products need to ensure compliance audits, and medical products require data privacy protections. General-purpose models, as public tools, cannot provide these guarantees.
Practical Advice for Product Managers and Entrepreneurs
- Product Managers:
- During the definition phase, use the three-question framework to self-assess the product’s value; if two questions fail, reconsider the direction.
- In the MVP (Minimum Viable Product) stage, conduct an equivalence test; if the results are inconclusive, redesign the product’s value.
- During the growth phase, monitor user retention for 6–12 months and adjust the product’s focus as needed.
- Entrepreneurs:
- Choose a direction with a clear data barrier, end-to-end workflow, or guarantee of certainty.
- Focus on building competitive barriers rather than on improving prompts (which general-purpose models will eventually match).
- Target areas where general-purpose models have limitations to create a unique value proposition.
Conclusion
“AI + industry” is not about simply applying a generic framework to generate revenue. The true value of a vertical AI product lies in its ability to solve problems that general-purpose models cannot. If users can replicate the product’s functionality with a simple prompt, its business model is likely unsustainable. Real barriers lie in data, workflow efficiency, and certainty—these are the key factors that determine the success of vertical AI solutions.