The ai product manager is becoming one of the most promising high-paying jobs in the future because of the combination of "product design" "technology understanding" models. The recruitment of ai product managers has increased by 144 per cent, and core pay is generally concentrated in between $350 and $500,000 per year, with a million-dollar pay incentive in large factories。
The successful transition from a traditional position to a programmer/product manager for the ai product manager could result in an average salary increase of 40 per cent. This increase is due mainly to two factors:
In addition, ai-based r & d applications are at a high stage of development, with high development opportunities such as low competitive entry pressure。
A common dilemma for transformational ai product managers
The entry of the ia product manager is not a simple product plus technology, and many people in the world of work want to enter the profession, but are caught up in these transitions:
1. Lack of knowledge systems: a product manager needs to have a combination of “business-down” “technology understanding”, a fragmented web-based self-learning knowledge and a lack of system-level planning. I don't know where to start。
2. Lack of landing experience: enterprises are more inclined to recruit “immediate forces” with successful ai project experience. Despite the enormous needs, many newcomers have no solid landing experience and are prone to a “no experience, no job; no experience, no experience”。
3. Cognitive anxiety and fear: considering ai product manager to be a technical position, blindly pursuing rapid iterative technologies, making it difficult to build a solid core of product competitiveness and unable to convert technology into product value。
So how do traditional technology, traditional product managers, and zero-experienced newcomers plan to enter the ai industry
The right path for transformation of the ai product manager
Prior to transition, systematic assessment of individual strengths
2. In transition: developing individualized learning plans
Based on an individual assessment, the individual learning plan is customized and then firmly implemented:
If you're trying to transform the a. I. O. Manager, are you in trouble? How? This is an example of the correct path dismantling of the fourth phase of the transformation of the ai product manager:
Phase i: developing product awareness + business understanding (approximately 1-2 months)
1 product capacity-building: the basis for product managers, the parallel operations of many companies at the bc end, and the basic product landing capability of ai product managers to independently integrate an ai product project。
2 industry understanding & business insight: clear business logic to see valuable ai applications, clear how to use ai-enabled operations and which processes/business difficulties to optimize. Converting technology to significant operational and commercial value。
Phase ii: building on the ai project landing experience (around 3 months)
3 technical comprehensibility: without being conversant with code achievement, knowledge of professional terms and technical logic/boundary is required to communicate smoothly and with algorithms/technical teams, integrate user needs with ai technology, optimize product experience and accelerate product achievement。
There is an ai landing experience: the ai product manager serves a different client group, finance, education, government, health, etc., the project may have several hundred models, as well as a multi-sector client scenario fast-out product programme, where the quick-output solution for the industry can be produced quickly。
The ultimate goal of these four-step transition paths is to build a product base, expand ai's technical awareness, build up one or three real ai project experiences, build high-scoring cvs and get a product manager offer。
You want to make your own transition plan
Scanning and adding a consultant teacher to help you comb
3. Success stories in transition
Case i:
Li si, 27 years old, a company technological development, two years of development experience. There is a greater interest in creating innovative products than in dry technology and an attempt to transform the ai product manager。
Transition path:
Empirical summary: “technology gives me a natural advantage in communicating with the algorithm team, while product thinking gives me a more holistic view of technology values.”
Case ii:
Rin, manager of g-end products of a construction digitized company, two years of experience, passive and demand drawing prototypes, with the sharp decline of the project feeling the winter of the industry, anxious for career development and determined to transform the ai product manager。
Transition path:
Lessons learned: "property experience gives me a good skill base, teacher knowledge sharing gives me the confidence to change business, hands on my resume, interviews help me so much."
Case iii:
Yang yang, master's degree in architecture, zero product experience, zero ai experience, leaving school without internships, anxiety without competitive job search, wanting to join ai and get an ai product internship offer。
Transition path:
In addition to the above, so far many good news have been received, and those who have studied carefully in accordance with the above-mentioned intensive planning have received almost all of them a hearty offer with a landing rate of over 95 per cent
Mr. Frank took the 30k product offer with the teacher
G-team b, 25% increase
One year of b-end product experience
Four years of experience product jump, 60% salary increase
Second grade students, get the chinese product offer
Two years of ui windowless, double payoffer
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In conclusion, i would like to say that with the advent of the ai era, industry as a whole has further upgraded its capacity requirements for product managers。
The competency requirements for product managers in different industries and areas have evolved from a single professional capacity with a product to a composite of both product-specific professional skills plus industry/business knowledge。
Whether it's b & c & ai products, or jump/up, your path has remained the same: learning! Don't worry about basic differences, education, action is over 80%。
If you want to get too hard, have a lot of experience in the project and double your pay, you have to start with classroom studies
1 systematic learning > 1 year blind searching
Rotation products/noru subproduct upgrades
