AI Product Manager Thinking: Building a New Paradigm for Intelligent Products
With the rapid development of artificial intelligence (AI) technology, the application scenarios of AI products are becoming increasingly extensive, covering multiple fields such as smart homes, autonomous driving, and healthcare. AI product managers need to insight user needs, accurately position products, create competitive advantages, and continuously optimize and iterate in a complex technical environment.
**I. Insight into User Needs: Deep Mining from “Pain Points” to “Desire Points”**
1. Pain points
User pain points are usually problems encountered in existing solutions. For example, in smart homes, devices cannot be interconnected. AI product managers need to find pain points through data analysis and user research and design intelligent solutions.
2. Desire points
Users have potential needs or desires. For example, they hope that smart devices can automatically adjust environmental settings according to living habits. AI product managers can analyze user behavior patterns through machine learning algorithms to meet desire points and improve satisfaction.
3. Pleasure points
When user needs are met promptly and exceed expectations, a sense of pleasure and satisfaction will be generated. AI product managers must ensure that products can not only solve problems but also bring surprises. For example, intelligent voice assistants can have humanized conversations with users and provide interesting functions.
**II. Accurate Product Positioning: From “Scenario-based Design” to “Traffic Entrance”**
1. Scenario-based design
AI products need to consider users’ behavior and emotional trigger points in different times and spaces and conduct scenario-based design. For example, smart home products provide personalized services according to different life scenarios. AI product managers can understand user needs by building user personas and design functions that meet psychological expectations.
2. Traffic entrance
Scenarios that can trigger user emotions are the real traffic entrances. AI product managers need to design key moments that touch users’ emotions. For example, intelligent health monitoring devices encourage users when they reach fitness goals, enhancing a sense of achievement, increasing user stickiness, and promoting word-of-mouth dissemination.
**III. Creating Competitive Advantages: From “System Capability” to “Efficiency Revolution”**
1. System capability
Behind AI products is a complex system, including links such as data collection, model training, inference engines, and user interfaces. AI product managers need to ensure that all links work efficiently and collaboratively to form a closed-loop ecosystem. For example, an intelligent customer service system not only needs to have strong natural language processing capabilities but also needs to be seamlessly connected with other enterprise systems.
2. Efficiency revolution
AI technology brings opportunities for efficiency improvement to enterprises. AI product managers can optimize business processes and reduce operating costs through intelligent means. For example, an intelligent supply chain management system predicts market demand to arrange production and logistics, and an intelligent customer service system automatically handles common problems.
**IV. Continuous Optimization and Iteration: From “Quick Steps” to “User-driven”**
1. Quick steps
The development cycle of AI products is long. The “quick steps” strategy can be adopted to launch core functions first to obtain user feedback and then quickly iterate. For example, the initial version of an intelligent voice assistant supports simple instruction recognition, and subsequent functions and scenarios are added.
2. User-driven
The optimization of AI products cannot be separated from user participation. AI product managers need to establish an effective feedback mechanism, collect user opinions and suggestions, and turn them into improvement directions. For example, an intelligent recommendation system optimizes the recommendation algorithm through user data and can also select the optimal solution through A/B testing.
**V. System Lifeline and Leadership: From “Key Tasks” to “Risk Awareness”**
1. Key tasks
AI projects involve multi-department collaboration. AI product managers need to find key tasks to ensure effective resource allocation. For example, when developing an intelligent driving system, safety and stability are the most important tasks.
2. Risk awareness
There are risks in the development and application of AI products. AI product managers need to have risk awareness, foresee problems, and formulate countermeasures. For example, ensure that data privacy protection complies with laws and regulations and pay attention to technological trends to adjust product directions.
**VI. Five Levels of User Experience: From “Perceptual Layer” to “Sense of Existence”**
1. Perceptual layer
Users’ first impression of a product comes from intuitive feelings. AI product managers need to ensure that the interface is simple and clear and the operation is smooth. For example, the appearance design of smart speakers conforms to modern home styles, and the sound quality is clear and pleasant.
2. Role framework layer
Users’ usage habits and behavior patterns affect their perception of products. AI product managers need to design functions according to different user roles. For example, smart devices for housewives and young office workers provide different functions.
3. Resource structure layer
Users’ personal resources and internal structures affect their use of products. AI product managers