Savita Kini

AI/ML by definition requires a decent foundational understanding for AI/ML/Deep Learning Concepts, trends in industry, tools and methodologies to be able to work with engineering in defining solutions to customer /user problems. For the forseeable future, I would say that most AI/ML product managers would likely function as both technical as well as regular product managers to blend user journeys into concrete AL/ML solutions. 

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-case, model deployment, and corner cases where the model might fail.  There is this notion tha...

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/ML PM is a new approch to solving the problem with AI/ML tools and frameworks. So anyone with a ...

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 Spee...

Key traits for AI PM is no different from other PM roles -- empathy for customer issues, ability craft / create / articulate problems and how we might approach the solution, industry and domain experience, and collaborative leadership to work with engineering. Willingness to learn or prior experience or understanding of AI/ML modeling challenges, and how they can be use in the context the industry / domain where it is applied is ofcourse a big plus. 

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 no "generic metric" -- we are still building product and features.  When it comes to the model it...

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, might as well get your hands dirty. If you are currently a software engineer, transitioning to...

This is an interesting topic since I have worn both hats - product manager and product marketing, in case of formal multi-product solutions with SKUs -- I wore all-in-one hat. Most recently at BabbleLabs as well - it was all-in-one. My learnings from these experiences if I had a product marketer -- here's what I would have appreciated  1) Thorough research on the market /industry segment including data that helps us assess the business opportunity, pricing and packaging vis-a-vis competition or lack there off  2) In some companies - when they have PMs with more technical background, Produ...

Sales enablement requires buy-in from the top sales and marketing leadership. It must have a strategic view of the business -  1. segments  2. regions  3. business and technical sellers  4. partners  5. programs by product  6. Communications  7. Calendar of things that come aligned to the business - programmatically aligned to region or segment or both.  Like every other program -- this needs Budget $$, program managers who execute well, understand the learning abilities and time constraints of who need access to the content, when.  To do it well and do it scalable -- needs goo...