Insider Secrets of AI Product Management: A Journey with Nikhil from Meta

AI is top of mind for product managers. Everyone knows AI will change our lives forever, we just aren’t sure how. 

In this enlightening episode, Shobit from Intentional Product Manager engages in a deep discussion with Nikhil, an AI Product Manager at Meta, about advancing a career in product management with a focus on AI and ML. Nikhil shares his extensive journey from beginning as a management consultant at Deloitte, through his MBA at Yale, to his AI product roles at Adobe and Meta. He offers valuable insights on transitioning into AI product management, the evolving role of AI in various industries, and practical advice for product managers aiming to future-proof their careers. Nikhil also discusses the significance of using AI for personal and professional productivity and provides tips on navigating career transitions with a multi-step approach. Additionally, he highlights his podcast, 'The Art and Science of AI,' as a resource for those looking to delve deeper into the world of AI.

00:00 Meeting our AI expert

00:26 Nikhil's Journey into AI Product Management

03:28 First glimpse into how AI will change the world

08:59 Transitioning work experience to transition into AI

13:47 Advice for Aspiring AI Product Managers

14:00 Future-proofing your career with AI

22:42 More on the amazing podcast "The Art and Science of AI."

Learn more about Shobhit Chugh at intentionalproductmanager.com and connect on https://www.linkedin.com/in/shobhitchugh/

Learn more about Nikhil Maddirala at  https://www.linkedin.com/in/nikhilmaddirala/

Listen to his podcast on Apple or YouTube.

www.intentionalproductmanager.com

Chapters

• 0:26 Journey into AI Product Management
• 3:28 How AI Will Change the World
• 8:59 Transitioning into AI
• 13:47 Advice for Aspiring AI Product Managers
• 14:00 Future-proofing your career with AI
• 22:42 The Amazing Podcast "The Art and Science of AI."

Transcript

  Ready to move to the next level in your product career? I'm Shobit from Intentional Product Manager. Join me as we discuss ways to help you stand out in your job search and your career, so you can have more impact and make more money. Nikhil, thank you so much for being here as a guest speaker on this video.

Yeah. I'm happy to be here. Thank you for having me. 

Awesome. As we dig into, AIML product management, something that a lot of people are very keen to move into and learn about, take us through your journey in both your career and then getting into this field. 

Yeah, sure. I can start with a brief intro about myself.

Nikhil. I've been in AI product management for over five years now. I'm currently an AI product manager at Meta, where I work on AI for ads. Prior to that, I was an AI product manager at Adobe, both on consumer and enterprise products.  In my roles at Meta and Adobe, I've built and scaled multiple AI products, serving, billions of individuals and billions of businesses.

And In addition to that, I'm also a part time AI educator and mentor. I run a podcast called art and science of AI, where I share like my passion and learnings around AI with people who are curious prior to AI product management. I also used to advise like fortune 500 companies on big data strategy and implementation like the early days of AI as a consultant at Deloitte.

That's where I started my career and I live in the San Francisco Bay area. Outside of work and AI some of my hobbies include reading science fiction and fantasy and weightlifting,  

weightlifting, amazing. Really great to hear about your journey into this space. So it seems that you had.

AI and big data background by prior to your first role as a AI product manager. Is that yeah, that's 

right. So I've been involved with AI for over 10 years now. Like I wrote my first master's thesis back in 2014  on comparing two different approaches to language understanding. There was the rule based systems approach, which was called formal semantics.

And then there's the machine learning approach, which was called natural language processing. And. Then, like I said, I got into management consulting and worked on strategy and analytics projects. There, a lot of like big data strategy. I think where I really made the transition to AI and product management was when I went to business school.

I went to a business school at Yale from 2017 to 2019. And at that time, in addition to. The business coursework that we were required to take. I took a lot of elective coursework in computer science and machine learning, and I was learning about topics like language modeling and natural language processing.

And coincidentally, this was the time when open AI had just launched GPT 2 at the time, and I was like totally blown away by the possibilities and implications for business and society and the impact that AI was going to have. And that's was a transformative moment for me when I realized that I wanted to pivot my career to focus more on this direction, because I thought this is the thing that's going to be really impacting the world in the next decade or two. 



So at that point, you're like you just realize this curious, like what was the journey from that realization to actually getting your first AI product managed control? What did you do? How did you prepare? A lot of people are interested in that aspect of it.  

Yeah. So this,  so like I said, I was at business school, I was doing an MBA program and MBA programs are typically set up to help people make career transitions.

So most of my peer group, including myself, is there trying to transition from one thing to another. So there are people that are trying to transition. functions, industries, geographies, different kinds of things. So it's a time, it's an environment that is good for, it's conducive to helping people figure out how to navigate this journey.

So I, that was, I had a lot of resources to, to help me with this. That's one thing I think I want to call out. So the MBA program really helped. And I, like I said, in addition to that, I was also taking some coursework in machine learning and computer science. So that's something that, that certainly helped me a lot.

Like I was. Tinkering with projects on my own, just like learning how to build simple applications, like how to build machine learning models and things like that. And that really was something that helped me stand out. I was able to bring my passion and enthusiasm for machine learning and AI to the interviews when I started talking to two companies.

And that's something that helped me stand out. But yeah, the reality of it is that I Did not have any experience in product management at that time. I was my background was in management consulting. And so I had some domain expertise in AI. I was trying to leverage that and transition to product management at tech companies.

So  I think yeah, I would say the things that helped me. Or one is like having resources and peer group that like help me navigate this transition to the MBA program. Second is my domain expertise and like passion for  machine learning and AI, which helped me stand out. That's the second thing. And maybe the third thing is I was very fortunate in that companies like, so the first job I got out of business school was as a product manager at Adobe on a product called Adobe experience platform, which is a consumer data platform, a customer data platform that's used by like enterprise marketing teams.

And within that, we were building some machine learning and data science capabilities. Adobe  was helpful in that they have MBA recruiting programs. So where the goal of that is often they're not trying to find the best candidate for a given role, but they're trying to get talent that they think will grow with the company and possibly stay in, in like multiple roles. 

  📍 So that's another thing that helped me. Yeah, so I would say, there's the standard interview process stuff that you have to go through, like you have to develop a pipeline of applications, be ready to face tons of rejection apply to hundreds of roles, get hundreds of projections, just keep pushing through that.

And you have to be willing to proactively go out and network with people constantly having conversations with new people trying to learn more about different roles and companies and what's available. And then,  there's the whole like interview preparation process. You have to like practice and do mock interviews and make sure you're able to present your knowledge and skills in a way that comes across.

As having the right kind of expertise. So yeah.  The whole gamut,  

just thinking of when you were applying for those product roles, did you focus it on cool? Like only AI ML product management roles. At that point, they were probably like fewer than the number that exists right now, given the interest.

But was it very focused or were you open to any sort of product management roles? 

Oh, at that time, I just wanted any role related to AI or machine learning. I wasn't focused on product management per se. I applied to a bunch of different roles. I applied to many data scientists for holes. I applied to like analytics roles.

So yeah, my lens at that time was less on product management. It was more on AI because I was like, Hey, that's what I want to do. It just turns out that given my background and experience, like now I realize it like product management is a really good fit for me. I don't know, maybe I could have also been a good data scientist or machine learning engineer, but it was harder at that time to convince anyone that they should take a chance on me for that.

Product management had a much more clear story. And yeah, since then I've been doing this for five years. I think I found the right fit. I love product management. It enables me to have the, Level of technical exposure to AI that I want to have, but also it enables me to focus not very narrowly on the technology, but more on what are the problems that we're solving with this?

What's the longer term strategy for developing products with this technology and things like that. So yes I find a good fit in this function of product management, but it happened by accident at the time. I didn't really know what product management was. I've just wanted to work on cool things with AI.

Love it. Okay. So I understand all this well. So now I'm curious about, to whatever extent you can share, assume I know this confidential campaign for which you won't share, but what are the kinds of things you've worked on that have this intersection of product management and AI, both at Adobe and Meta?

Okay. 

Yeah, sure. I'd be happy to talk about what I've done so far at these companies. So at Adobe, I had two roles. The first role I had was as a product manager on Adobe experience platform. As I said, what the product is, It's a customer data platform that's used by enterprise marketing teams to unify all of their customer data in one place, and then use that to create personalized experiences using machine learning and data science.

For example, a company. Has, so say like a hypothetical example, say you're like a company like Best Buy. You have customer data from many different sources. There's customers who purchase things in store. There's customers that purchase things online. Those are two different data sources you have.

You have data about customers who interacted with your website bestbuy. com and that it gets captured through analytics data. Adobe actually has a analytics product called Adobe analytics. And it, people who use that this was a good fit for them. Then you also have data like there's call center data.

Customers might call you and ask some questions or something. So the idea is that we have a. Companies like Best Buy, for example, but you can generalize that example, have a lot of customer data from different sources, and now they want to leverage this, all the data together to create like personalized experiences.

So for example, next time you call Best Buy, instead of just asking you that, Hey, look, What are you calling about? Can they leverage all of this data to predict like what you may be calling about and give you a more personalized experience. Or when you go onto the storefront on the website, like instead of just showing you something generic, maybe they can show you things that are relevant to you.

So that was the first product that I worked on. Then after that, so Adobe has multiple business units, this their biggest business unit is called the creative cloud, where they create software for creative professionals, like video editing, photos audio and so on. The other business unit they have is called document cloud, where they create, productivity software to work with PDFs and documents. And the third business unit is called the digital experience or the marketing cloud. So that role that I described in that product that was in the marketing cloud. And then I moved to a new role at Adobe in Adobe Acrobat AI. So at the time what we were trying to, the problem we were trying to solve is that reading PDFs on your mobile device sucks.

Because PDFs are optimized for large screens or like A4 sized paper or letter sized paper. When you read a PDF on your mobile, you constantly have to pinch and zoom to  read it in the right way. So we were using AI to understand the content and structure of documents and then create a mobile responsive PDF reading experience.

It was called liquid mode and it still is. And I think it's awesome. It's one of the best ways to read. A PDF on your mobile device. And now of course, like Adobe Acrobat AI has a lot more than that. There's a with generative AI. They're also trying to get into helping people understand documents, ask questions and answers and then things like that.

So that's one thing I worked on. Now in my current role at Meta, I work in AI for ads. And specifically focusing on improving the ads personalization system and the algorithms that determine which ads to show to which users. So our goal is to make meaningful connections between advertisers and users.

So we want to help advertisers find  the right customers who are interested in their products. We want to find, help users find. The right products that, that they're interested in. And this is a problem space that comes with like tons of data. Because there's at any given time, like there are billions of users, there's like millions of ads in the system.

And how do you figure out like this matching problem? What kind of data can you leverage for that? What are the kind of algorithms that you want to use for that? And yeah, that, that's the area I focus on. And this also interesting because there's  tons of it intersects with not only like machine learning, but there's also a lot of legal regulatory privacy concerns that you have to pay attention to.

So it's a, by nature, like a very cross functional role. And there are like many considerations that you have to balance.  

Awesome. Great to hear about your work. Now I'm going to put you, it's like last set of questions, but given that there's a lot of product managers who are, let's say somebody with, some experience in product management, but they're really trying to figure out how do they get into AI product management to some extent, how do they future proof.

They create like that's the term often people use and thinking about the impact AI is going to have on product management, product development in general. What sort of advice would you have for them? Would you suggest any trainings? I would love to hear that from 

you. Yeah. So I think there are multiple ways in which you can think about engaging with AI. 

So one is.  At a personal level. And one is at the level of the product that would say, I would separate out these two things. So  we're talking about a target audience of product managers, right? So regardless of what your product is, there are certain activities that you do on a day to day basis as a product manager, you're trying to.

Do some market research, industry analysis. You're trying to develop a strategy for your product and a roadmap and trying to come up with requirements. There's a lot of like communication and presentation. So all of these things, I think it's definitely helpful to start thinking about how you can. Use AI to help you be better at those things more productive, more efficient, and so that you can focus more on high value work.

And this I think is general to almost like any profession. Product management is just an example of this. So I definitely think whatever profession you're in please start using AI and see where it can add value. What are the things that it can make you better at? What are the things that can help you scale up?

What are the things that can help you automate? So that's one thing. And that's advice I would give to anyone regardless of the profession. So yeah, within product management, I would look for things that you think could are repetitive things that you're doing like frequently.  Things that you could potentially automate or augment with AI.

And for me, often it's things like doing research, like I need to find some information across like a whole bunch of documents and how do I find the right sources of information? And so I'm very lucky. In that I work at meta and we have a bunch of pretty like sophisticated internal AI tools to help people be more productive and solve some of these challenges.

Of course, I realized that most companies probably don't have like specific internal tools for that. So in that case, you would have to leverage  Existing systems like chat, GPT or Gemini or whatever. Of course, pay attention to privacy policies and what data you're allowed to share or not.

So that's a huge, make sure that. Whatever you're doing with your company's data, that it's appropriate that you're if you're going to go put that on chat, GPT, make sure you're actually allowed to do that. If you're not, then try to work with some example or dummy like data and use the insights from that.

I think this problem will be solved pretty soon. Most companies are probably going to. Either have their own in house solutions or make deal like chat, GPT open AI. And these companies have enterprise solutions that have all the privacy and data security built in. So pretty soon I'm sure most like companies are going to have that built in.

So yeah, start making use of those, do it for your own personal life as well. In that case, then you're not restricted by your company's data policy. So a simple example that I use, that's you're like a content creator. You create YouTube videos and things like that. And you know that it's a lot of work.

Every time you create a video, you have to edit it. And then you have to extract like a title, chapters, descriptions, show notes, and things like that. For myself, like I automated that I created like a custom GPT where it has knowledge about my podcast. And then I just upload a new podcast transcript to it every time.

And it automatically gives me all the info I want in the format that I wanted. So it'll suggest here, the episode titles you can use. Here's some descriptions you can use here, the chapters and so on. So cool.  Anything like that, like anything you're doing repetitively think about how to automate that.

And I would say, yeah, both in your personal and professional life.  And then there's the other dimension of, okay. Like with, as a product manager, sure. I'm using AI for my personal productivity, but you're also like, now I want to think about. Maybe transitioning to working on like an AI product or like incorporating AI into my own product.

And yeah, that, that is different. That's more of a career transition type thing, especially if you want to change your role to like work on a different product, that's more AI focused. I don't know if that is, I do see like a lot of people these days want to make that transition, but. I'm not sure if it's super necessary, because I think like  pretty soon, like all products are going to have AI incorporated into it in some way.

So you could also just think about how am I incorporating AI into my current product? So that's something you could do, but yeah, maybe there are other arguments you could have. Like you could say okay, for my personal growth and learning. I want to go work at a company that has more established understanding of how to use AI.

And so I want to work there and then bring the learnings back. Yeah, I think that's, it's similar reasons why I think PMs often want to work at big tech companies. To help accelerate their career. And then you can go to a company in any other domain, and then you can transfer your knowledge of best practices and things like that to the other domain you're working in.

So yeah, I think if that is your goal it does make sense to try and get some experience working on AI products that are being developed by some of the more like sophisticated, like AI companies. And I guess if your question is how do you make that happen? I think a lot of it is just general  PM career guidance will apply here.

One thing I always advise people is to think of Your career transition as like a multi step journey. And it's often not a single step. Like often people who are like not product managers right now, they're doing something else they might want to know how can I become an AI product manager at Meta?

And I think one of the things to recognize is that may not be something that you can do directly in one step. Think about if I were to break this down into a multi step journey what would that look like? So maybe let's say, for example, you are a product designer at a FinTech company.

So think about moves you can make that minimize Degrees of transition. So there are many dimensions to your role, right? There's the function you're in, which is like product management, product design, consulting, whatever there's the industry you're in, there's a type of product you're working on.

There's the technical domain that you work in.  The easiest transitions to make are ones where you are minimizing the number of things that you're trying to change. So if you're like, Hey, I'm keeping everything fixed and I'm just changing this one thing I work in B2B SaaS, I want to go work in B2C SaaS. 

Doing everything else the same industry, I'm product manager. So that's the easiest, the hardest transition is where you're like, okay, I want to change a bunch of things. I want to change the type of product I'm working on. I want to change my function from product design to product management. I want to change the type of company I'm working at from a startup to a big tech company.

So I think, yeah, you should balance that. If you notice that transitions you're trying to make are like many try to break that down into smaller chunks and see what are more reasonable transitions. And specifically, I think for the transition of I had no experience in AI and I want to, Work on a more AI focused role for that part of the transition, try to find ways to show your understanding of this domain.

And it could be as simple as the kind of things I mentioned earlier, which is find ways to incorporate this into your personal and your, workflows.  And maybe create some automations, go publish them on like your GitHub profile, or there are many no code platforms now where you can do that.

And that's a really simple way of being able to demonstrate your passion and expertise in this area and show that you're actually able to use this stuff to, to solve problems. Real problems, 

Become a power user first before you go and try to understand it. Yeah. Yeah. I think so. Awesome.  so much for sharing your journey, sharing your own transition, and then also advice for others.

I totally resonate that example of automating YouTube stuff. I follow the exact same thing and it's so helpful and it's taken so much repetitive work off my plate. 

Awesome. I hope whoever's listening to this, finds this helpful. Oh, and also if I may, I'd love to make a last plug for my podcast, the art and science of AI, where we talk about Various topics in understanding the science of how AI works and the art of using AI to reimagine your life or your business.

And we have a lot of examples. We talk about how to build like automations and workflows for yourself. So yeah, if you found this helpful, please check that out. 

And just learning from that would be a very logical first step for folks who are looking to transition into AI product management.

Thanks again, Nikhil. Appreciate it. Yeah. Great talking to you. Hey, be sure to check out our website at intentionalproductmanager.  com to see how you can level up in your career.