All related (10)
Savita Kini
Director of Product Management, Speech and Video AI, CiscoMarch 2

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 that a few buzz words will help you to convince engineering, please refrain from doing that. If something looks too easy, remember it is not. 

Ask lot of questions in the early learning phase, no question is dumb. Make sure you have looked at the problem from all different angles, have understood the customer problem, sought out peer perspectives through industry analysts and experts, competitive plays -- so you can define the scope of the solution. 

With AI/ML and with any other product, perfect is the enemy of the good, but at the sametime, if you have biases in your dataset, the "good enough" might be a disaster as soon as it lands in the hands of your users and customers. 

Veronica Hudson
Director of Product Management, ActiveCampaignJune 7

Everyone is going to make new mistakes coming into a PM role depending on where they are starting from. For me, my biggest mistake was letting my own imposter syndrome take over. I had worked so hard to move from a CSM role into product and once it finally happened, I couldn't get rid of the voice in my head saying, "Wow you are so lucky they *let* you move into product, don't screw it up, because you definitely have no idea what you are doing!" While this was kind of true (I really didn't know what I was doing at the beginning), I should have trusted the fact that I would not have been giving this opportunity if my managers and peers didn't believe in me. I was unable to focus on the skills that got me here, like my deep understanding of our customers and their problems, and instead focused on the skills I needed to improve, like my technical understanding.

Give yourself the patience and grace to know you aren't going to be good at all of it from the get-go, but be hungry and eager to learn. Which leads me to my next piece of advice; ask for help! There is nothing wrong with saying, "I don't know" or, "I don't understand." No one is going to expect to you know everything right away and asking for help ends up saving a lot of time and energy vs you trying to figure it out on your own.

It can be hard to ask for help, for fear of looking "dumb" or at least feeling that way. It helped me to identify a few allies early on that were happy to walk me through things (slooooowly sometimes), because they understood the faster I learned, the better our team would be for it. The first engineering manager I worked with was a great example of this. He became the person I knew I could always ping to say, "Heyyyy can you explain this to me in more detail. I'm not quite getting it." It was such a relief to always know there was a friendly face to walk me through tough problems.

Lastly, decision fatigue is a very real thing in product management, especially for new PMs. Never in my career had I been the end-all be-all for making decisions and now all of the sudden, I had engineers and designers and asking me for answers left and right! It took me a bit to realize it was ok to be wrong, as long as we had a direction to move in. Not every decision has to be the right one, but the longer you take to make it, the more the team slows down. So just do your best, measure the results and be ok with pivoting if you are wrong.

Alexa Maturana-Lowe
Director of Product Management, Core Experience, FivetranJuly 7

I think the most common mistake that I see is jumping to solutions quickly. This is definitely my biggest mistake as I started as a product manager. For me, it was/is really easy to think I know the answer and to move quickly from the unknown/undefined to the known/defined stage and ultimately check something off the list by delivering it into the hands of customers. However, the role of the product manager is to stay in curiousity and research and the unknown for long enough in order to get enough information from customers and then from your crossfunctional partners to define to the best solution to solve the customer's pain while balancing that with the goals of the organization and your core product principles. And all this while driving urgency around achieving results. A tall order!

Mike Flouton
VP, Product, Barracuda NetworksMay 4

The biggest misconception about PM is that it's a tactical, inward facing role. It should be a strategic, outside in role. Writing stories, running scrum ceremonies and crashing standups are some of the least important things on your plate, yet it's where most new PMs spend their time. You need to get out of the office (virtually these days), spend time with customers and prospects, and develop a deep understing their pain and the problem. 

Engineers are really good at running scrum ceremonies, finding solutions to problems and micromanaging JIRA. The best value you can give them is helping them understand your customer and their pain. 

Savita Kini
Director of Product Management, Speech and Video AI, Cisco
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. 
Veronica Hudson
Director of Product Management, ActiveCampaign
I think of product management skill sets in three major buckets: technical, business, and customer understanding. When I talk to aspiring product managers, I like to gauge the maturity of their skills sets in each of these buckets. Ideally a candidate would be strong in one bucket, have some basic understanding in another, and be actively working to hone a third (although I wouldn't expect any sort of proficiency). A good example of this would be a CSM. They likely have a very strong understanding of customer wants/needs, a good grasp on the business overall, but may be lacking in their tec...
Alexa Maturana-Lowe
Director of Product Management, Core Experience, Fivetran
I have two tips for breaking into Product Management but I'm sure there are many more.  The first is to work with Product Management in your current role and/or talk to current Product Managers. Product Management can be seen as a very appealing job but many don't understand the day to day and trade offs of the role. By working with or informationally interviewing folks who are currently doing the job, you'll get more information on wether the job is actually for you and what appeals to you about it.  The second is to ask your manager if you can work with Product or use Product princi...
Suhas Manangi
Group Product Manager, Airbnb
Top 3 traits that makes a Good PM a Good AI PM: 1. Understand foundational ML tech concepts and having used them to make product decisions. For eg: Statistical Regression, Causation vs Corelation, AUC, P/R, Features vs Labels, Feature distribution, Model Training, Model drift and auto training, etc 2. Aware of potential bias and fiarness need in ML solutions they have launched in the past. Having used model observability and interpretability to explain the model output for their product corner cases. 3. Ability to scale up product decisions going from single global ...
Luca Beltrami
Head of Product, Retailers, Faire
I typically suggest to try to pivot into product management by changing a few variables but not too many - so you have a few strengths to rely on. When you start a new PM role, you are exposing yourself to potential changes on multiple dimensions (in order of likelihood): * Function/ craft: This one is non negotiable * Manager: You will be reporting to someone else with new expectations and unknown working styles * Team: Potentially joining a new set of teammates * Domain: You may need to learn a new domain (e.g. growth, conversion, platform) * Company: Exposing yourself to a...
Deepak Mukunthu
Senior Director of Product, AI/ML Platform, DocuSign
I have seen 4 different types of AI Product Managers: 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). This role typically works with data science teams that work on internal optimizations leveraging ML. 4. Research focused: Bringing AI research breakthroughs to market If you are interes...