So today, I'm gonna be talking about, and I think everyone, feel free to put in the chat what your background is.Are you in product, are you an engineer, or trying to get into the product, or just curious about the AI and how it's changing the job and the landscape and everything. But today, I'm gonna be talking about future proofing your career. If you're a product leader, what are some tactical and practical steps that you can take, with all the changes or in this age of AI? I am Shweta Grewal, and I'm gonna give a quick round of introduction. This is me. I'm Shweta Grewal. I'm chief AI product officer at Boston New Technology. My background is in product tech, AI, and start up. I'm a start up adviser as well where I work with tons of founders on different industries.

So it's industry agnostic. Have been helping many women, whether they are in students, whether they're mid professionals, or they're just curious about whether coming from non tech background when and how they can learn AI and how they can incorporate into their, personal and professional lives. Also, I'm on advisory board at a couple of different places, community builder. If you want to talk to me about go to market, I am happy to talk to you. This is me. You can scan my QR code. Happy to chat and connect on LinkedIn. So today, as I was saying, as a product leader, when you are thinking about now we are in the age of AI, how can we start thinking about whether you're working in a company?

It's a, you know, small startup companies or enterprise or large scale companies. This is the this is some, date these are some data points that you should be thinking about. So for example, 88% of the organizations at the moment are using AI in the least of one business functions, whether it's engineering, marketing or sales, product organizations, though, they have started using AI. Out of that, 5.5 have generated some impact using AI. 28% of the CEOs own the direct AI governance. So as you're using AI, it's very important to have some cadence and governance. Right? 75 of them are hiring will require AI skills. So if you're thinking about looking for a job, you have to think about how I can use AI because companies are looking for not just, like, using some elements, but how you can really implement in your daily life and see and show the outcomes and the results.

56% have shown the workers who are getting hired have AI skills at the moment. And then 91% will continue learning more critical than ever because, companies are investing a lot of money as well as they are investing in a lot of AI tools, how they can use in the daily productivities in their work that will generate more faster ROI. So we'll talk more about that. These are some architecture I wanted to share with you folks. As the product leader, there are some job architecture. Right? So if you're a product, leader, you know, you go through the feature backlog owners. There are couple of features or the products you might be launching at the same time, and then it becomes very critical to think about in these four different areas how you can use AI and how you can make AI as your buddy to help you move forward. So one bucket is feature backlog owner, and now you have to think differently. Like, how can you use AI as an intelligence orchestra? Right? Second is road map manager.

Now you have to use AI to signal to strategy. What signals are you getting? That way you can use AI to help you move forward. Stakeholder prioritization, as we all know, as product leader, we do every single day. That will help us now with the AI, ethical AI steward. Right? How we're gonna use AI, whether it's AI agents, whether it's in workflows or products. The other fourth one is KPI tracker because as product leaders, KPIs on the numbers means a lot in the metrics to help and track what is next. And that's when we have to think about adaptive learning leader. Moving on, and just another statistic is 81% of senior leaders are now expected to drive or use AI into their products and the features.

And I'm sure you might have seen that as well in the organizations. They have this road map where they're thinking, like, how in the next three months and six months or within a year, we can use AI powered product or AI features or in general, how we can incorporate AI in different, departments or the functions, I would say. So as we are thinking about AI, right, as we're thinking about, okay, in my role, how can I use AI? This is some, I would say, terminologies or the fluency stack as there is a tech stack. This is something that you can use, to get yourself upscale your skills. So for example, I would say product leaders. Right? They are couple of them are from, the tech background. Some of them are not from the tech. They're nontechnical leaders. Right? Now with the AI, we all know, that everyone can code. Right? Or we can learn to code even if we don't have that experience.

Nowadays, product leaders are expected to have that information or the skills where they know, okay. If I want to launch something, a prototype, you know, you can build the whole prototype, I would say, or the whole first stage of your product within three, four hours, which you had required before, at least, I think, six to eight months. Right? We have we have to design and use the engineers' help and all those things. Now as a product leader, you can have that within a couple of hours. So these are three, buckets or the levels, I would say, where you have to think about how you can build your own fluency stack. So first one is AI aware. You have to every single day, I would say there is so much going on. Right? Like, you learn about the new terminologies. There is something coming up. Like, you have been using some LLM, and now they have upgraded.

So there is a lot of thing going on every day, but you have to start with the foundation. You have to, like so AI aware. Right? Maybe take some there are tons of online courses available as well. So just to share, like, you know, learn about Gen AI or Microsoft AI fundamentals. That that's where you know what the terminologies are, what it means, you know, AI, Gen AI, or RAG, all those different things, when you become aware of those terminologies, it will help you to connect the dots. The second thing I would say, there are different LLMs available right now. And as a product leader, you will learn what would help me best. So for example, perplexity is greater when you're doing research. Right? Cloud is great as well. So different, LLMs will help you with different there are tons of tools as a product leader that I use as well, like Craftful, check PRDs when I write PRDs.

So trying to understand what tools or LLMs will be helping me as a product leader and then start and try using those. Second is AI proficient, meaning, as a product leader, we have or product managers, we have different stages. Right? We have to go and do the research, user, research, whether it's understanding the customers, whether writing the PRDs. I would say run three AI assist assisted user research session, which will help you to compare from what you did before AI came in versus how it will changes when you're using AI. I would also say build some prompt libraries for yourself, which will have, you know, for the PRDs you're writing, the user stories or the competitive analysis and the go to market. Explore the different tools, as I was saying, that will help you to narrow and you know, you can even use the tools or elements to summarize what you have done, you know, reaching out to the stakeholders, creating the reports.

So as a product leader, you'll be saving a lot of time and you'll be focusing on the critical work. The third level, I would say, AI strategic. It's not just about using AI and how you can use, but how you can be more strategic in using those AI skills or the LLMs or the tools or the road map. Right? Define your organizations. Think about what you want as a product leader in the next six months and next one year, and then think about how AI is helping you versus where you want the human in the loop. Right? Something that you can think about is building a monthly AI council where you bring in all, PMs, you know, folks from data, legal, ethical, departments, and engineering in one room and have that conversations, have the boardroom because AI is not just a function of for the product leaders.

As a product leader, where we are launching the products, we have to bring in so many different, stakeholders together. Right? And this is one step if you take forward where you're having this AI council bringing altogether different functions and having those conversations will really help you to move forward in your, launches. This is, this is a master AI product decision framework, I would say, as we think about what should I be and when do I start. Right? So this is something I wanted to provide you the framework where you can even start thinking about. Right? So these are, again, are in four different buckets, which I'll talk in a minute. But only, as I'm having conversations with different product leaders or industry leaders in different organizations, they are thinking, like, the research also was done where only 64% of the organizations have started using AI in some way.

Right? Whether they're just using chat GPT or some LLM tools. Right? It has not fully incorporated using AI end to end. And, again, it will depend on the learning, the change, that's happening in the organizations, how comfortable, and the training as well. So training is another part, that I'll I'll talk up in a minute. It's also required. Right? Even, let's say, if you're in an HR department, what does that even mean or look like to use AI? Right? And how and what functions you can use? So to start with, the first bucket is the AI value metrics. So this is where I have listed as high and the low risk. Right? So something is of high value, but it has low risks. That's something you can build now.

That's how you can do the cost, I would say, the risk benefit analysis, the high value plus high risk. So pilot. When you're launching the products with using AI and it all depends whether what industry you belong to. Right? If it's a regulated industry, for example, it's a health care, health tech, fintech, legal tech, those industries which are have dealing with sensitive information, that's where you want to pilot first and test it out rather than fully launching it. See if the guardrails are making sense. Because if you, you know and also, like, in the health care industry, if you're using the AI powered products, and you have to see if it's helping the patients or if it's helping the customers and how they're reacting. So a lot of thing goes into it when I you are using AI. So they these are some, value in the risk analysis.

I'm not gonna go over it, each, but this is something you can think about, like, the AI value metrics. The second one is the signal to strategy loop. So think about it, has four different things. Right? Detect. What signals are in your data? If you have lots of data, I'm pretty sure, like, every industry have the data, you have to detect, okay, what did I have, decode what the user truth does it reveals, what product bed does it validate, and how fast can we test and learn. Because every time when we're launching, products, I was talking to one of the industry, leaders where they were have they had, like, five customers, and they were thinking, like, okay, One customer does not want us to train the the data or train the model, and they had to shift and do everything manually.

Right? So you have to find the signal and strategy if they're working with different customers, whether it's 50 customers at the same time or 100 customers, you have to really change your strategy based on what they really want. And if they're like, hey. Don't use my data. I want you to so you have to change your strategy, and that will also depend how you have to train your team. The third bucket is the ethical AI checklist. So, it's very important. It's not just about launching faster using AI to launch something fast to your product. I know we all want to have that competitive edge, but it is also really important how we can implement ethical AI in our product and the features.

As a product leader, it is very important. And I would say it's not just a role for the product leader, but it is a role for the whole organizations, and every department plays a key, role. So who could if you launch something, who will be harmed by this? Who is excluded? What data has been used? Who can get affected? And how we can monitor that? And the fourth bucket is the ROI measurement framework. You as you're doing all these, changes or all this implementation, you really have to track the ROI. Right? What is the revenue? Are you generating, some incoming revenue? The customer acquisition cost, the risk that you're taking. What's the baseline before and after the AI implemented?

Did Did we even really see the changes and what changes we saw? Right? So this is a a quick framework. Redesign the product strategy muscle. So as we are thinking about using, you know, upscaling our skills, as we think about different frameworks and different, departments. We have to think about different, I would say in three buckets, which I've listed here, strategic foresight practices. So, it starts from bottom up as well. I would say start with your product teams. Tell them encourage them to do some monthly review of competitor AI signal scans. Map a two year AI exposure curve, which will tell you, like, what AI features will be required or will be used in the next six months or a year. What was done a month before will not be even relevant. Right? So things are changing faster. So you have to keep on testing.

Build a product assumption log and see what works and what does not. The second is AI native road map. Classify, I would say, in augment AI, automate AI and amplify. Reserve at least 20 to 30% capacity for AI experiments. Give your team some time where they can just go and learn and do some experiments and tell them what they have learned during this experimentation. Right? Conduct quarterly AI road map stress test. I know, some organizations have this quarterly and some of them are doing monthly. So see if you can do that quarterly and see what, assumptions are you testing out. The third one is data informed decision making. Establish your product, not star metric. Right? What AI impact make it quantifiable and time bound? It is really important.

Just using AI in your product, in your future, and just adding that you think, like, customers would like, would not lead, or would not be sustainable. Right? So in that case, you have to really test it out. As we spoke before, find the signal. Right? That will help you to strategize. And what is the noise? Have those decisions with your teams. Here, we won't talk about building AI ready teams. So as I was saying, when you're thinking about changing organizations or more AI ready, you have to think about how I can help end the team and do stuff. Right? So, for example, what I mean that by that is, hire for learning velocity, not just experience. When you hire the teams or you have the teams already, can you help them, you know, create a road map for them, like, whether it's, you know, like, first month, second month, or third month, how they can learn about AI and what is the future fruit.

And there's something interesting which I will say run biweekly AI show until I think, I've been saying this a lot, and this really helps team to showcase what they have learned and what they can and which department can use. And even, like, not they can even implement in the product. So this is really beneficial where they can some, have some time dedicated to just experiment. And this also falls under bucket of if there is a budget given on what size of the company you are in, if you can have some budget, maybe $200 per month in the AI tools and budget, where the teams can go and explore. Right? I know a couple of organizations, they have at least 40 to 50, different elements and tools that they're using just to see what works well. Not every tool will be, or elements will be, relevant to different functions or different roles, so it has to be a trial and error basis. The fourth one is establish psychological safety for AI. It's okay to make mistakes, but the mistakes have to be, not live. Right?

You try and test it out and see, and you need to have a red team where they can test out and see what's working and what's not working. I would say build dual track career path. So there are AI specialists plus also human centered track. How you can bring those together, I think that plays a bigger role. And then measuring AI adoption, not just usage. You really have to, as a leader, to see if you have an AI on your road map, if if you're using AI powered product, if you are training your teams, you have to really track if the AI adoption has taken place. I think that will help you to see what changes you have to make if you have not already. And here, native AI ethics and product decisions. I would say, I'm not gonna read a lot because I know we have four minutes left.

So really going quickly, build an AI ethics review gate, establish fairness metrics because metrics will play a lot whether it's an AI powered product or feature depending on the geographic language and design for explainability. Right? If you have decisions that you want the users to learn in plain English, that's that's what it's gonna help. Create a human override protocol. If you're made making the decisions in high stakes industries or regulated industries, which is hiring, credit, health care, you have to have some protocols in place and publish your AI transparency scorecards. Moving on, this is just a framework if, whether you are trying to learn or move into the organizations who already have AI or using in some way or hybrid. This is a ninety day future proof blueprint where you can think about what can I do in first month? Right? Thirty days, sixty days, and ninety days, which will help you to, like, not just learn about the the terminologies or basics, but also understanding how AI has been used in different industries, what can I do, and how we can implement?

Second is experimentation. It's learning is great, but, also, once you start experimenting and start using it, it will help you a lot to understand in your organizations, in your product teams, or even in for yourself what, tools or, elements would make sense. Right? And show and tell, as I said, is a great way to learn and share where you don't have to go through by learning by yourself, but you're working with the teams. And the last one I would say is systemize and scale. Having the monthly AI council, which I mentioned before, where you're bringing all the leaders from different departments together and you're brainstorming with them.

So it's not just one person running the show, but all the departments are coming together to make your organization move forward much faster. And have some budget if you can to experiment. I think that will excel a lot. And I would say one thing for the product leaders, if you can generate or if you can think about the product principle document as you're thinking moving into AI, that will really help the organizations of, in a way that, you know, how as a product leader you're thinking about the product and the organizations.

This is, who you must become. So this is nothing but the organization that is seeing most value, out of AI is when they start seeing that as a companion, as a transformation, and rather than getting scared or rather than treating that as just a tool. Right? But really thinking of, like, how can I use AI to really move forward? I have a minute, and this is just a summary. And to wrap it up, I would say in the age of AI, the leaders who future proof their careers are not the most technical. You don't have to be technical. The only thing that you need are you have to be intentional. You have to be ready to adapt to the changes, and you have to be most human. Thing AI cannot replace, your critical thinking, your skills, the empathetic nature.

You have to think, like, how I can make use of AI to help me move forward. I think that's the mindset shift also something is acquired when you have to keep on moving and using AI. And to wrap it up, I'm gonna share it again. This is my, QR code. Feel free to reach out to me on LinkedIn. I know, I don't think we'll have time to cover questions, but I'm just gonna take a look here if you have any. We are on time. So, Laurie, let me know. I think we are good. So I'm just gonna stop sharing. Thank you everyone for joining. This is great. I know we didn't have time to cover, but feel free to reach out to me on LinkedIn. I'm happy to, have conversations, answer any questions you may have. Right?

Thank you.