AMA: Cisco Director of Product Management, Speech and Video AI, Savita Kini on AI Product Management

This is not a simple question. There is no such thing as a "traditional PM" because PM roles and responsibilities differ by industry segment. There are substantial differences in PM roles from consumer app to enterprise app to hardware and enterprise infrastructure.  Fundamentally the role of the PM is still the same - to consider customer problems, potential solution approaches, and lead them from concept to launch and facilitate a continous innovation lifecycle. The only difference here in AI/ ...Read More

Seek to understand and clarify first before assuming you have learnt everything that is to know and propose solutions.  I see there are lot of online courses, documentation, articles -- you can do a lot of reading online to educate yourself about the complexities of model development, data gathering, data labeling, training and testing.  One of the big challenges for AI/ML PMs is understanding whether we have enough data for training the model, is it diverse enough to cover for the specific use- ...Read More

There is no one "metric" that ML Product Teams will use to define success. It entirely depends on what is the "ML" used in the context of a feature or product. Typical metrics might include Quality improvements achieved via ML implementation vs traditional algorithmic implementations Usage adoption of AI/ML based feature Business or user perception metrics of improvements - CSAT, NPS scores Time savings, $$ savings, etc typical Business metrics. Benchmarking vs peer products Accuracy and respons ...Read More

Metrics are an interesting question. This really depends on the type of product we are building that leverages ML. Since ML can be use for example in electronic records, sales workflows, computer vision type use cases or speech / audio use cases some of which I am familiar with -- we can break it down to product use itself and then algorithm/model used, how often it is used, what kind of business or customer experience metrics it provided or influenced. So the long and short answer is there is n ...Read More

A strong software engineering and product development background is a good foundation to transition to a PM role. AI is a very broad field, and one can apply AI in many different applications and use-cases. Just as you would with any job search and transition -- take some foundational courses in AI/ML so you understand the basics of model development, data issues, ethical/responsible AI. There are many courses online, and you don't have to do actual coding, but since you are a software engineer, ...Read More

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 comple ...Read More