AI Product Management

AS A FIRST PM, YOU WILL NEED TO BE VERY JUDICIOUS WITH HOW YOU ALLOCATE YOUR TIME AND RESOURCES. IN FACT, I THINK THAT’S TRUE FOR LARGER COMPANIES AS WELL. THERE ARE ALWAYS GOING TO BE MORE IDEAS THAN RESOURCES AVAILABLE. AS A PRODUCT MANAGER, YOU ARE RESPONSIBLE FOR TRANSLATING THE COMPANY’S VISION INTO A ROADMAP SO YOUR FIRST PRIORITY SHOULD BE INTERNALIZING THE COMPANY’S GOALS. IS IT TO DRIVE SIGN-UPS? INCREASE RETENTION? INCREASE MRR? OR SOMETHING ELSE ALTOGETHER? NARROWING IN ON THAT TOP GOAL HELPS TO WEED OUT WORK THAT MAY BE LESS RELEVANT. Once you’ve identified the top goal (there...

Honestly, the first product manager for a company is probably not ready to establish a prioritization framework. The first PM probably needs to focus on customer discovery, market discovery, MVP intuition, and experimentation. Until you have established product-market fit with enthusiastic customer demand, rigorous prioritization is probably bikeshedding. Once you have that fit, that's when you'll start to get inbound requests/ideas/complaints from current customers, potential customers, new market segments you hadn't considered, and sales teams eager to displace competition. Then you can ...

There are many great approaches to this question – and to some extent, it will depend on what the company values. If you're a first Product Manager, it is most important that customer needs / expectations are at the forefront of any framework.  With my teams, I sometimes like to use a graph, where one of the axes represents customer impact (how much does this product or feature postively improve their lives?) and the other maps effort or investment. That will give you an easy 2x2 where there is a clear set of high impact, low effort/investment things that represent quick wins. You'll als...

The great thing about being the first hire is something that is also great about Product Management: there is room for interpretation. My philosophy has always been more heavily focused on understanding how things operate current state, finding out pain points as well as the more successful parts of a product, and leaning on those insights to form your next steps. In certain scenarios, what a team may need is for someone to roll up their sleeves and do the work to keep the lights on for a product. It may be months before you can get the product to a comfortable enough place to think abou...

First PM in a company! I have not done it, nor have anyone in close network to have a good understanding. My guess is that they have to establish right roles/responsibilities on what to carve out from the CEO. Perhaps focused on scaling up product for next million users (or take on next set of enteprise clients), or execution focsed. Do take this with a grain of salt as I am guessing based on when should a CEO hire their first PM.

When you are the first PM, you are straddling several priorities: 1. Finding product market fit 2. Scaling the team 3. Scaling the product The biggest failure mode is trying to do 2 and 3 before you do 1: As the first PM, your foremost priority should be to establish product market fit. Distibuting that effort across more people dilutes the effectiveness because information gets lost along the way. Once you enter scaling, things become more complicated. Here the biggest failure mode is becoming your own bottleneck and entering what I call the doom loop of hiring: The less time you s...

In my experience a prioritization framework is foundational to establishing a great working relationship within your own team and stakeholders. I'd also argue that if executed well in the beginning, the framework may not change much regardless if you are the first or 10th PM at a company. In fact, it may be a bit more straightforward as a solo PM since the prioritized list of needs and deliverables is a direct negotiation between you, stakeholders and your delivery team(s). As the product organization grows you'll notice that blockers, dependencies and enablers exist within your own product...

There are a few different vectors to consider here. There is the effort/impact matrix, which is pretty good at helping identify low hanging fruit - essentially mapping out potential workstreams on a 2x2 matrix (High effort/high impact, low effort/low impact, etc). This can help cut through the noise as a good first step, but isn't the only thing you should be considering. Other lens I apply are things like: * Customer - how many customers does this serve? VIP customers? Is this a want or a need? This gets to the "impact" part of the 2x2. * Urgency/timeline - how flexible is the schedu...

Thank you for the question and I'm sure this is exactly not the answer you're looking for which is, "it depends" You're balancing building trust and relationships, understanding your users and the business, and likely an evolving company strategy. So the question you need to ask yourself is what are you optimizing for? The runway of your company is critical to consider, but I always lean towards how might we prioritize learnings and building trust to build out a strong product roadmap

The fundamentals of prioritization are not too different when you're the first at a company. But in the early stages of a company or product, it's even more important to focus.  At an early stage company, or a new product at an existing company, chances are you're finding product-market fit. When thinking about prioritizing work in that context, you need to be crystal clear on the metrics that matter, and laser focused on moving them.  In my experience, developing and testing hypotheses is a great framework for this stage. You simply can't afford waste, so understanding what you're tr...

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...

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 help...

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 giv...

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 ...

Product School, Try Exponent, and Product Allinace are good resources for PM interviews prep.  Later is a good question. Interesting idea. I don't know of any, but it so interesting that someone should be offering it. Perhaps they might have rolled into certification or cohort courses with live projects!

Assuming you are specifically interested in AI product management, I would suggest these approaches to get started with ML. While I was new to ML/AI, these approaches helped me. 1. Online courses 2. Part-time certifications 3. Conferences 4. Kaggle 5. Publish your work 6. Internship/Volunteering I covered this in my session on AI/ML Product Management: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/ If you are looking for guidance on general product management, let me know. There is a lot of material online and I can point you to.

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. 

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 ...

AI PMs are expected to have a broad understanding of ML lifecycle and knowledge of key ML trends in the industry. Ensure your product management basics are strong: Deep focus on customers, business priority, foster an experimentation culture. In this session, I also covered key traits of successful AI product managers: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/. Hope this helps!

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...

In addition to core business metrics that are improtant for a product success, below are the additional ones AI PMs obsess over to ensure the success upon launch doesn't regress over time. 1. Precision and Recall 2. False Positive 3. Model quality monitoring metrics based on where is the risk to business (feature quality, score shift, re-training frequency, etc)

While working on ML product/feature, there are 2 sets of metrics: 1. Product success metrics that product managers define. Purpose of the is to measure the business/product outcome you are trying to optimize for. Your standard metrics like customer adoption, usage, retention, satisfaction etc. fall in this bucket. So, product managers choose what's best for the situation at hand. 2. ML metrics that data scienstics define. These metrics measure how good the ML approach/solution/model is. For e.g., good metrics to measure a Regression task are: Root Mean Squared Error (RMSE) or Mean Absolut...

While infusing ML into a product can be extremely powerful to light-up new scenarios for customers and to optimize current scenarios, it can take a lot of effort to get it right. It all starts with defining and prioritizing the right scenario/problem to focus on, working with data science and ML engineering teams to map the business problem to an ML problem, trying out to different approaches to identify the right one to go ahead with, experimenting and optimizing and then operationalizing the approach and shipping it as part of product stack working with ML engineering and development team...

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...

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...

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. 

PMs are generally categorised into B2C (Consumer), B2B (Enterprise), Platform, and Product. PMs role is generally 2 of these 4 things. Within this one can also be generalist PM vs domain PM vs growth PM vs scale PM etc. AI PM is the same. One additional categorey can be about building ML Ops platform but I am not convinced one needs a PM for it, or can't be fit into one of the 4 categories described above. PMs working on Alexa can be AI PMs, but not necessarily TPMs.

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...

Trust & Risk is a specific domain, so generalist PM would need to pivot to become a domain PM. This will require launching solutions across multiple years. Fraud evolves every year, but over 90% still remains the same decade long type. Payment Risk is a good stepping stone. Balacing security with usability (frictions to stop bad actors) which translates to doing a trade off between fraud loss and revenue loss is a new skill PMs have to develop. 

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 ...

5 years from now, likely there is going to be no difference between a Traditional PM and AI PM. AI is going to be used/present in all products. I see "Traditional PM role" as a foundational one to have, upon which one can grow to become a good AI PM. Good Traditional PM with aptitude for tech and data science is likely to do well as a good AI PM. Taking a Udemy course on basics of AI/ML, and applying to every day PM job will be a great start.