How to Become an AI Product Manager: 5 Essential Steps

How AI Product Managers Differ from Traditional PMs

AI product managers require deeper technical background and understanding of AI technologies. Beyond market positioning and user needs, they participate directly in algorithm selection, data collection and processing, and model training. They also need cross-disciplinary communication skills to collaborate with data scientists, engineers, and designers.

Shared Responsibilities

Core Responsibilities: Both roles center on product design, development, promotion, and lifecycle management, including market research, user definition, feature design, and cross-department coordination.

User Orientation: Both emphasize user-centric design and experience.

Market Adaptability: Both must track market trends and industry dynamics.

Team Collaboration: Both work with engineers, designers, and marketing to move products from concept to market.

Five Steps to Becoming an AI Product Manager

1. Understand AI Industry Structure

The AI industry comprises three company types:

Industry + AI: Core industry knowledge with AI-enabled products/services (e.g., smart home). Requires deep industry understanding and scenario analysis.

AI + Industry: Core AI technology providing services/solutions (e.g., intelligent customer service). B2B-focused, demanding strong communication and project control.

Basic Platforms: Provide AI technology platforms and data support for rapid enterprise AI adoption. Suits PMs with bottom-layer technical understanding; R&D background preferred.

2. Grasp Large Model Capabilities and Boundaries

Large models — neural networks with massive parameters and layers — are pivotal in AI advancement. They process and understand vast data, boosting system performance and accuracy. PMs must know what these models can and cannot do to set realistic product expectations.

3. Know AI Product Manager Classifications

Narrow-sense AI PMs directly apply semantic, speech, computer vision, and machine learning technologies (commercially viable in recent years, powering smart speakers, service robots). Subcategories:

Semantic: dialogue, knowledge graph, machine translation, search PMs.

Speech: ASR, TTS PMs.

Vision: face recognition, vehicle recognition, intelligent video analysis, image retrieval PMs.

Machine Learning: ride-hailing, recommendation system PMs.

Terminal Application: physical robots, virtual robots, smart automotive, smart home PMs.

Broad-sense AI PMs indirectly involve the four core AI domains or apply emerging tech (brain-computer interface, quantum computing). They may not directly master AI tech; roles existed pre-2015, often handled by engineers or founders. Subcategories:

Terminal Application: robots without core AI tech.

Strategy: ride-hailing, recommendation systems using ML.

Non-mature AI Tech: brain-computer interface, quantum computing.

Broad-sense roles may evolve and merge into narrow-sense AI PMs over time.

4. Master the AI PM Competency Model

The AI industry has entered a product-first phase (second of three: tech-first, product-first, operations-first), raising competency bars:

Business Monetization & Closed Loop: In capital winter, finding viable commercial models is critical. PMs must deeply understand industries, identify valuable scenarios from pain points, devise effective strategies and pricing. Successful commercialization seen in security, finance, internet services, enterprise services.

Control Product Requirements: Based on clear company strategy, dig into requirements, redefine scenarios/needs with AI, rapidly validate and launch pain-point-solving products.

Mutual Drive with Technology: Understand technical implementation, combine user needs with AI tech, optimize design, accelerate goals. Continuously track AI trends, communicate with engineers, know technical boundaries.

Earn User Trust: As AI advances, ethical, moral, legal risks grow. PMs must ensure products meet ethical standards, protect user rights, increase transparency.

5. Execute the Full AI Product Workflow

The AI product workflow typically includes these stages (illustrated in the article's diagram): requirement analysis, data preparation, model selection/training, evaluation, deployment, monitoring, and iteration. Each stage involves specific PM responsibilities bridging business, data, and engineering.

Closing Thoughts

Becoming an AI product manager is challenging yet rewarding. It demands passion for AI, continuous learning, technical knowledge, innovative thinking, and user-oriented product design capability.