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What level of hard/technical skills should someone aim to develop to thrive as an AI product manager?

2 Answers
Savita Kini
Savita Kini
Cisco Director of Product Management, Speech and Video AIMarch 3

Excellent question. Hard/technical skills are absolutely critical to be a successful AI product manager. As I mention in my other answers -- PMs bring domain knowledge, and customer perspectives to seek solutions. In AI/ML product development, we apply all the same foundational technical knowledge, with the added AI/ML component. AI/ML models do come at a cost for the compute, so one has to consider optimizations. For example, consider aspects like latency .."how much time will it take to complete this task" , "what is the impact on the user experience or the business workflow". In our Speech Enhancement technology in Webex -- we have to consider improvements in speech quality after extracting noise, background speech, music etc and then package these models into small form factor software libraries to run on the end devices like laptops, mobile phones, desk cameras. In the case of a Cloud/SaaS based product, you are not bound by the constraints of the edge device, since you could run your solution as a microservice in the cloud. The depth and breath of technical skills and ability to keep learning and building your knowledge base is critical for your sucess. We are still in very early stages of this technology shift as it continues to proliferate in every area of our life. 

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Deepak Mukunthu
Deepak Mukunthu
Salesforce Senior Director of Product, Generative AI Platform (Einstein GPT)June 28

Broad understanding of ML lifecycle and knowledge of key ML trends in the industry are required to be successful in AI product management role. Beyond this, the level of hard/technical skills required depends on the type of AI product management role - there are 4 different types:

1. Product focused: Infuse intelligence into products (e.g., search, personalization)

2. Platform focused: Infrastructure & tooling for data scientists and ML engineers to manager ML lifecycle

3. Business/Operations focused: ML to improve product adoption/operations (e.g., customer churn prediction) or business outcomes (e.g., revenue forecasting). 

4. Research focused: Bringing AI research breakthroughs to market

If you are interested in learning more, watch my session on AI/ML Product Management: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/

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