With the rapid development of artificial intelligence technology, AI product managers have become a popular and high-paying career position. However, many traditional product managers face the confusion of how to make a smooth transition. This article will delve into the paths and strategies of transforming traditional product managers into AI product managers, help you understand the differences between the two, analyze the advantages and challenges of transformation, and provide practical transformation path suggestions, hoping to help you.
Recently, when chatting with some product manager friends, I found that everyone was anxious about a problem:The salary of AI product manager positions is generally much higher than that of traditional PMsMany people can reach 4-50,000 yuan per month, but they don’t know how to transform.
To be honest, I understand this anxiety very well. In my 20 years of product career, I have experienced the explosion of mobile Internet and witnessed the whole process of AI technology from concept to implementation. Today I would like to talk to you about how traditional product managers can smoothly transform into AI product managers.
01 What is the difference between traditional product managers and AI product managers?
Many people think that AI product managers are traditional PMs plus some AI knowledge, but this is actually a misunderstanding.
Xiao Wang, a product manager I took before, has a solid technical foundation and good user insights, but he repeatedly hit a wall when interviewing AI product managers. Later, it was discovered that the problem was his lack of “technical translation ability” – his inability to translate the possibilities of AI technology into specific product solutions.
The core work of a traditional product manager is:“Turning user needs into achievable product functions”, and AI product managers need“Turning the capability boundaries of AI technology into solutions to user problems”。
Let’s take a look at the specific differences between the two:
The most critical difference is the way of thinking. Traditional PM is often “function-oriented” – we do what users want; AI product managers are “capability-oriented” – we use AI to solve problems with what it can do.
02 What are the advantages and challenges of transformation?
Let’s talk about the advantages first
Experience is the greatest asset.I have seen too many AI product managers with technical backgrounds who understand the algorithm without any problems, but do not understand the user, and no one uses the product. Traditional PMs have a natural advantage in this area – you already have the ability to feel the product and gain user insights.
Other than thatAI technology is penetrating all walks of life, your deep experience in a certain field has become a scarce resource. For example, a friend I know who makes financial products has doubled his salary after transforming into an AI risk control product manager, because there are too few people who understand both AI and financial business.
Let’s talk about challenges
- The technical threshold does exist.It’s not that you need to be an algorithm expert, but at least you need to understand what supervised learning is, what is NLP, CV, and what does the accuracy and recall of the model mean.
- The learning speed is more demanding.AI technology iterations are too fast, GPT-3 was still being discussed last year, and this year it is a multimodal large model. You must maintain a mindset of continuous learning.
- Uncertainty is greater.Traditional products can be used after being made, but AI products may not meet the model effect or have problems with data quality, requiring stronger risk management and control capabilities.
03 My recommended transformation path
Based on my observations and practices over the years, I have summarized a more reliable transformation path.
Stage 1: Build AI cognition (about 3 months)
Don’t gnaw on algorithm textbooks as soon as you come up, it’s easy to dissuade yourself. My suggestion is:
Focus on understanding the use case, not the technical details.For example, learn how Transformer is used for intelligent customer service, rather than studying its mathematical formulas.
Recommended Learning Resources:
- Andrew Ng’s Machine Learning Course (with a focus on the application part)
- The technical boundaries chapter of the Advanced DeepSeek Application Tutorial
- Flying Paddle official introductory tutorial
The key is to establish an “AI capability map” – to know what problems current AI technology can and cannot solve.
Stage 2: Accumulate practical experience (3-6 months)
Just look at it and don’t practice the fake handle. I suggest you:
Start with the problems around you.For example, if the product you are responsible for has user feedback analysis needs, you can try using DeepSeek for sentiment analysis and classification.
Learn the “28 Principles”.Focusing on mastering 20% of the core concepts (BERT, CNN, recommendation algorithms, etc.) can solve 80% of practical problems.
Do more scene mapping exercises.Every time you learn a technical concept, immediately think about what problems you can solve in your product.
Stage 3: Preparing for a job search (1-2 months)
The resume should highlight the compound ability of “technology + business”,Instead of simply stacking technical keywords. For example, write “Design a recommendation algorithm optimization scheme based on user behavior data, increase click-through rate by 15%” instead of “familiarize yourself with machine learning algorithms”.
The interview focuses on problem-solving ideas.The interviewer asks you how to optimize the search algorithm, you have to analyze the user’s pain points first, then propose technical solutions, and finally say how to verify the effect.
Here’s a tip: even if the JD requirements don’t exactly match your experience, be bold with your resume. Many companies value learning ability and business understanding, and technology can be cultivated slowly.
I suggest learning collocation
【Specially Recommended】DeepSeek Product Manager Series Courses:
DeepSeek empowers product managers to improve efficiency – starting point classroom – cultivating digital product, operation, and marketing talents| Everyone is an education brand under the product manager
“Understanding Users with DeepSeek: A Practical Lesson in User Insight for Product Managers” – Learn User Analysis in 3 Days
“Designing Products with DeepSeek: Prototype Ideas and Iterative Planning for Product Managers” – Say goodbye to idea dryout
“Analyzing Competitors with DeepSeek: A Winning Lesson in Market Strategy and Decision-Making for Product Managers” – 3 hours to complete competitor analysis
“Writing Documents with DeepSeek: PRD Writing and Team Collaboration Lesson for Product Managers” – 30 minutes to complete PRD
These four courses add up to 35 lessons, basically covering the whole process of product management, and they are all practical-oriented.
Suggested learning sequence:
- Read books first to lay the foundation
- Choose 1-2 core courses for in-depth practice
- Complement traditional product management knowledge
- Find a practical project to practice
05 Key elements of successful transformation
In my observations, product managers who successfully transform have three traits:
1. Data thinking
This is the most important thing. You have to learn to speak with data, and think clearly about what data indicators correspond to each function of the product.
A friend used to make C-end products, only focusing on DAU and retention. After transforming into an AI product manager, he began to pay attention to the accuracy and recall rate of the model, and even learned to look at the confusion matrix. This shift in thinking is key.
2. Cross-disciplinary collaboration capabilities
AI product teams usually include algorithm engineers, data scientists, product managers, front-end and back-end, and everyone’s knowledge backgrounds vary greatly. You have to learn to be a “translator”, transform business requirements into technical language, and transform technical capabilities into product value.
3. Continuous learning mentality
The AI field is changing too fast, and this year’s hot spots may become obsolete next year. Staying curious and actively learning new technologies is essential for survival in this field.
06 Career development prospects
The development prospects of AI product managers are indeed good, but we also need to see trend changes.
- Positions will become more and more subdivided.Medical AI, financial AI, and education AI have completely different requirements for product managers, and it is better to choose one direction than to do everything.
- The technical threshold will get higher and higher.In the past, it was enough to know the concept, but now you need to understand model deployment, effect optimization, and even participate in model tuning.
- But there are also more and more opportunities.The AI transformation of traditional industries has just begun, and a large number of compound talents who understand both business and AI are needed.
A friend I know who has transformed from an e-commerce product manager to an intelligent customer service product now has an annual salary of 800,000 yuan and is often poached by headhunters. The key is that he has a deep understanding of e-commerce customer service scenarios and knows what problems AI should solve.
07 Some suggestions for transformers
1. Define Your Position:Think about whether you are suitable for being an AI product manager at the application layer, or at the platform layer or at the algorithm layer. Most traditional PMs are more suitable for application layers.
2. Take it step by step, don’t rush:Transformation is a process, don’t expect to be fully mastered in three months. Try AI tools in your existing job before considering changing jobs.
3. Practice more, memorize less concepts:AI product manager is a very practical position, and the theory of light back is useless. Find opportunities to do projects, even small projects in your spare time are valuable.
4. Establish Your Own Methodology:Everyone’s background is different, and the transformation path is also different. Summarize the AI product management methods that suit you in practice.
5. Keep an Open Mind:Join the AI product manager community and communicate with your peers. This field changes too quickly, and closed learning is easy to fall behind.
Written at the end
The transformation of AI product managers is indeed not easy, requiring time and effort. But if you really want to seize opportunities in this era, the transformation is worth it.
The key is to be patient. I have seen too many people give up because of momentary setbacks, and I have also seen some people who have persevered and achieved good development.
Technology changes, tools change, but the core competencies of product managers—understanding users, solving problems, and creating value—never go out of style. With this foundation, coupled with an understanding of AI technology, you can find your place in the new era.
I hope this article will be helpful to friends who are considering transformation. If you have any specific questions, please feel free to exchange and discuss.
