 So I want to start this topic with a small story on the Chinese experiment. So for those of you who haven't heard about this experiment, let me give you a short story on this. So it seems that there's a philosopher called Searle who did a thought experiment like there's a person inside a room and that person doesn't know to read or write Chinese. And then somebody from the outside of the room, they're given a piece of text in Chinese language. And this person's job is just to see the shape and the symbols in the Chinese language, get the information from what's available within the room and all the information will be available in the room. And this person's job is to just find the text that's matching according to the input that they received and then finally give the corresponding output for that input text to the output window. So the person on the other end understands or assumes that the person inside the room understands Chinese, whereas in reality, the person is just identifying and matching shapes and it's processing the output, whereas that person doesn't have to understand Chinese at all. So this is the Chinese room experiment. And the whole AI, in my opinion, is kind of very similar. With that being said, like I said before, I'm Siddhartha Longogan and I'm currently a senior technical product manager at Amazon. And before going into the presentation, I want to give you a disclaimer that all of the opinions here are my own and doesn't reflect any of the current or previous organizations that I am and I was part of a little bit of a background about me. I come from India and the southern part of India, Chennai. I did my undergrad in College of Engineering in India and did my post-grad in MBA in Arizona State University. I love photography and I love motorcycle riding and I also love drawing and computer design. As you can see, that's the Lamborghini I drew using AutoCAD. So a very creative person, which is why I really love doing product management. I'm passionate about the space. The next thing, my journey to product management so far. So I'm basically a very creative person and I'm also very empathetic. Like I identify myself as an INFJ, the introverted intuitive feeling and judging personality. So these are my core traits and because of this, I really loved working on designs of software products and I started off with mobile applications, which made me start on designing delightful customer experience. So I was doing this back in 2014 for a mobile application startup back home in India. And eventually I slowly graduated to translating user stories to product features. I really enjoyed solving a customer's problem in that way and translating all the customer requirements, the features into clear documentation and then handing them over to the engineers. Really loved working collaborative with the designers and engineers. Now, as I moved to my next row, I took on bigger scope within the product management where I'm now not only writing requirements or the specifications for the product or feature, but I'm also now solving problems. What could be the best solution to solve a particular problem? So I was doing this for quite a while before my MBA, before I came to the US. And I really loved working with multiple product managers, where we basically brainstorm and discuss how to best design a particular feature. Given there are multiple teams, multiple product teams, multiple designer, engineer teams, et cetera. So I was doing that for quite a while and then now here at Amazon, my scope is much larger in terms of as a senior product manager here where I not only solve problems, but also discover problems, define what the problem is and then more importantly, prioritize what problems to solve. Because there are a whole bunch of problems that you can go out there and solve, but where you have values like prioritize the problem that you wanna solve now and what you wanna solve later on. So that's a pretty much a snapshot of how I started my product manager journey, almost like 10 years back and where I am right now. And it also speaks about the why I'm doing this, which will form a large part of a presentation today, which you will know in some time. So today's agenda. Today we'll be covering three key topics. The first one is the product manager's value proposition and product management, and then the AI value proposition and product management. And then finally, the product manager powered by artificial intelligence value proposition and product management. So let's get started. What is the product manager's value proposition today? And if you see, I've tried to summarize conceptually how the PM ladder looks like. Someone might start as an associate for a product manager. They then grow to be a product manager and then some companies have the senior product manager and then you have, and then you grow to become a product leader. A product leader can be a director, a general manager, a vice president or a chief product officer. Anything that requires you to lead a group of teams or organizations, I defined it under this bucket. So if you start from the left to right, you have the associate product manager who translate user stories to customer experience and product features, which is, if you recollect my product journey map, I was doing as the initial stage. And then as one grows in their PM skill and role, they start taking on more responsibility in terms of writing user stories and coming up with new features of how they can fit into an existing product or how they can launch a new product to meet a specific demand. And once you do product management for a while, now you become a much more senior product manager in the space and then you take on more experience, more responsibilities in your workplace and where you just don't solve existing customer problem but also identify new problems and products and then you also work on product strategy. So now having done all these things for a while, people grow to become the leader in the product space and in organizations where they not only take care of, they are dollarly managed products but they also manage people. So a typical product leader identifies problem where the problem is not even identified yet. They are required to have a three to five year strategy or a conviction on what's the next biggest thing that's gonna happen where the company can monetize on that. And then they also have to manage a portfolio of products and people and cross organizational teams. Now if you see one pattern in this whole map, you see that there's a lot of clarity in the left side of the ladder where most of the things that you get to work on are already preset and predetermined and predefined. So you just come in as an associate product manager or a product manager and execute. So you need to have really good execution jobs and then you keep delivering results. Now as you move along the ladder, you take on more ambiguity. That's why you see the far end of the product leadership is all about ambiguity and nothing is clear over there. And as part of your job, you are required to drive clarity from that ambiguity. So leaders, most of the times they get involved with the strategy definitions, the three year planning, the architectural planning, identifying problems, prioritizing problems and all those things where it is like completely ambiguous. There may be a thousand things that you can go and do, but you need to be laser focused on what you wanna do and how you wanna prioritize, which is best for the company. So let's take that map and apply it in my product management journey so far and I'll break down the different elements involved in the journey so far. So if you look at who I am basically, I'm a very creative person. I'm an empath. I like, I mean, I'm really good at empathizing with people, their emotions, their feelings, why they are doing certain things, what are they here for, what are their goals, et cetera. So this has basically helped me to understand the customer from what are their needs, what are their culture needs, what are their emotional needs, et cetera. So these are my core human traits, creativity and empathy. And now if you look at the functional attributes of the product manager, you typically get to design customer experiences or user experiences. You have to write user stories. You have to translate the user stories to product specifications. A product manager typically solves problems and then like I said, the more senior they become, the more they work on strategy and problem discovery, problem definition and then prioritizing what problems to solve first. Now these are functional attributes of a product manager. Now, if you're a product manager, if you have been in the product management space for a while, you may have come across terms like a functional requirements and the non-functional requirements of the specifications. So this is more on that terms where functional attributes are what are your core part of the job, like what are you paid for you to do. And then there are other non-functional attributes of a PM, which means that there are some of your personal individualistic skills that you bring to the table, which is not directly related to how your product behaves but how you execute, how you work with others and how do you interact with your organization. And those I define as the non-functional attributes of a product manager. So some of the non-functional attributes of the product managers here are accountability, interpretability, ethics, leadership and influencing stakeholders. So by accountability, I mean, if a chief product officer is coming up and saying that, hey, we as a company, we need to focus on this space for the next three to five years and we need to invest all our resources into this space, then that person is accountable for that decision and the strategy. So if something goes wrong, then that person is responsible for the outcome. And interpretability means communicating to the stakeholders that I make this decision because of X, Y and Z. So that anyone in the future or anyone who's not available in all the meetings can easily read a piece of doc or a future hire can come and read a piece of internal wiki or something and then understand why a decision was made and what were the inputs and assumptions that were considered at that point in time which led to that outcome. Now that's interpretability. Ethics is something which you were more responsible for the broader community, earth, sustainability, et cetera. So as a leader, everyone is required to be ethical in nature so that again becomes a non-functional attribute. Again, the more hire you're growing the corporate ladder of a product manager, you need to demonstrate leadership and influencing the stakeholders in order to make the best decision for the customer and for the product. So now these are the non-functional attributes of the product manager. Again, I would like to, I trade that the more you move from left to right, your needle moves from clarity to ambiguity where you're more clear on what you're working on on the left side of the spectrum and on the right side of the spectrum, you have no idea what you're gonna be working on and there's a lot of data, quantitative and qualitative data that goes into the decisions that you make as a senior product person. So that being said of a product manager's value proposition, now let's talk about the AI value proposition. Again, now it's the same map here of someone who is from the junior PM to a product leader role. You have the clarity to ambiguity spectrum here. Now, revisiting some of the common things that product managers do according to their role is more tactical and as they are new or they are relatively in a junior position and as they move to a higher position or a senior position it's more ambiguous and more strategic. So for example, an associate product manager gets to work with the design. They may work with the designers to come up with the UX. They write the different documents like product requirement documents or the market requirement documents, the business requirement documents, et cetera and the product manager, they start writing user stories. They are expected to be aware of agile methodologies like scrum, et cetera and then they are expected to make decisions based on the trade-offs, et cetera and a senior product manager strategy and a product leadership is more of problem discovery and vision, et cetera. So now where does AI play a really good part? So if you look at the left end of the spectrum that's we have AI tools currently that do these things really well. That's why that's what I call the current state of AI. Like we have AI tools to convert what you prompt to beautiful mops AI can write documents for you the first draft of the documents at least. AI can give you a project plan of what your process should be based on your company data and it can also help you come up with the first draft of your marketing blogs or take notes for your customer research, et cetera. But the more right you go in the spectrum which is where the leadership and more ambiguity exist. It's an ambiguous area where AI currently may not be able to perform as good as it does on the left end of the spectrum. And that's because there are a lot of ways there's no one correct answer for executing the right strategy. There are so many ways people execute that people and companies execute strategy. So the way AI does cannot be like one right answer. And again, that comes to the interpretability where if you were to ask a person to execute a strategy you can understand what is their rationale and why they made the decision. Whereas if you feed a strategy into an AI model you might not be able to understand or decide for why that model predicted the strategy because there are like so many millions of nodes inside which is not practical for anyone to go and say it was because of this node within the model that made this decision. So AI lacks that kind of interpretability and that's why the more ambiguous, the more senior a decision is made you need people making those decisions. That being said, what are some great examples of how a product manager can leverage AI tools in today's world? A lot of product managers or almost every product manager has to conduct user researches and participate in user research. And AI can generate great questions for your user research. Again, automate node taking and insight generation for customer interviews where you just focus on talking to the customers. You can parse the usage analytics to automatically group users into new cohorts where you can find different new segments of users to monetize and for growth hacking. And when it comes to observability and application performance monitoring you can use AI to predict possible root causes for an event and the next one is the stakeholder management. So we currently can use AI to create a project plan based on the company architecture, capture project risks, assumptions, issues and dependencies and communicate to stakeholders by automating all these workflows. You can also use AI to create working prototypes for rapid experimentation which is a very fundamental and crux of efficient product management. And for me personally, the thing I love the most when it comes to using AI to the best of our productivity is overcoming writer's blog by using AI tools to come up with the first draft for with a framework of beautiful strategy doc or coming up with a great blog or launch announcement or go to market emails. And once we have this first draft now you no longer have this writer's blog and then you have inspirations and then you can tweak it to whatever language or whatever you wanna communicate with the value proposition of the work that you're doing. And moving on to the third and final section of this presentation is the product manager powered by AI value proposition. Now you may have seen a lot of these things posted all over LinkedIn and social media that AI is not gonna replace you but a person using AI is gonna replace you. And this is along the similar lines and I just wanna try to break down that line to what actually means in product management context. Now recollecting the spectrum diagram once again you see that these are the different skillset that a product person will need to do their job in their level of seniority. For example, an APM will have to do wireframing or write docs, might have to do system design and so on and so forth. So the blocks that are marked in orange they can be done by AI today. There are a lot of great tools to convert your what you say into wireframes. There are tools to generate text to write your product requirement documents, business requirement documents and market requirement documents. There are AI tools to design what should an efficient what should a great API design doc should look like what are the different parameters that an API design should take consideration of, et cetera. As I said before, AI can also help in user research, competitive intelligence and coming up with defining metrics. The next blocks which are the green blocks are the blocks which we have AI today but they are not that great in delivering those things. For example, insights, we still need a human person to with the contextual institutional knowledge to come up with that insight of why this metric is. So the same goes to new product launch. You may have a lot of obstacles when you're working towards a new product launch. So as a person you need to identify those blocks and resolve those conflicts that arise from a timeline perspective or from a dependency perspective. And this involves working with a lot of humans and communications and making sure that you address the needs of the stakeholders as and when they arise. Again, monetization. There's no one rule to do it, right? There are different strategies to do it based on what your customers are willing to pay for. What are your competition is asking for, et cetera. So again, there's more of ambiguity in that. So there may be AI that does these things well to some extent but still you cannot launch a product with a dollar value just based on AI. Now, the blocks in red are the ones where we need human beings to do those jobs. Like we are all the red blocks here identify are completely ambiguous in nature where you don't know what's out there. You just have to go understand empathize with the user be creative in your solutions and see what works and what doesn't work and then take the company forward one step at a time. For example, product discovery. There may be so many things what your customers might be doing. First of all, who is your customer? Who should be your customer? Who isn't your customer? AI cannot tell all these things at least in today's world. So given all that ambiguity, we need a human person who can be held accountable, who can understand why we are making the decision in that way, who can explain it to the stakeholders who can influence the customers, who can influence the stakeholders, et cetera, to make the product successful. Again, a product leadership areas which consists of ethics, leadership, culture, those things need humans to perform those areas in product management. So what this means to the product managers and the companies? So the left end of the spectrum has a lower dollar value of impact in the organization where it's more executional and transactional. Whereas the more right you go in the spectrum, the dollar value of the impact of the organization is really high. Like a company, a CPU or chief product officer identifying the next huge opportunity would be worth millions of dollars as compared to someone who's working on writing a product requirement document. So as you see, the dollar value of impact to the organization gets higher and higher as you move, as you grow from junior positions to the senior positions in the company. And what this means to product managers with the new AI wave that's coming in, product managers can grow to a senior position much faster than they could in the past. This means that they can use or leverage AI tools for doing their day-to-day jobs, but at the same time spend effort and learn some of the human-only skills. Like I mentioned earlier, like working with strategy and getting comfortable with ambiguity so that young product managers from early on in their career can focus that time in acquiring these high dollar value skills and get to that position faster than ever. So in short, the person who's, the product manager who's using the AI will grow in their career much faster than a person who is not leveraging AI in their product management discipline. Thank you, everyone. I hope you enjoyed the webinar.