Chandra Janakiraman: belief

Transcript context

…So the first disclaimer is I'm not sort of an AI futurist, but I read all the emerging stuff that's coming out, most of the emerging stuff that's coming out and use some of the tools. And it's fascinating what's happening. And I think it will definitely have an impact on strategy formulation. So what I'll share is what resonates for me in the context of strategy formulation with AI. So right away, I think the basic idea is everybody should be using assistance in the strategy formulation process with the basic tools that we have. And there are two ways to get AI to assist you in the strategy formulation process. The first is to support the preparation phase in terms of research. And this could be competitive analysis and, for example, you could do trend analysis from a vast library of competitors' release notes. And you can sort of say, "Okay, what are the themes of investment of a competitor's release notes?" Or you could do a reviews analysis of a competitor product and sort of understand what's resonating for users, what's not. And you could also ask a tool like ChatGPT to do a head-to-head comparison between a few players on a certain dimension and really give you a heat map of how they all stack up. And you could also ask an open-ended question like, "Hey, why is this new product so successful? Why is it getting so many users?" And there's some good hypothesis that you usually get. So really leverage in sort of the preparation phase from a competitive analysis standpoint. The second one is in this idea called generating mock strategies. And I know Claire Rowe at your summit spoke about this, and I think this is absolutely right and should be a critical input into the strategy process, which is asking these tools for a mock strategy. And I call this a mock strategy because it's kind of almost the answer but not quite the answer. So what I've found is that these mock strategies, like let's say I support VRChat now and I ask it like, "Hey, what should VRChat do? How should we grow?" And it generates a mock strategy. What I've found is that it's, one, surprisingly good. It's incredibly well-informed and well-articulated. I've also found that its biggest strength is also somewhat its weakness, which is these mock strategies tend to be pretty comprehensive and extensive, and there's an investment recommendation in a vast number of areas. So basically, if you remember, the core of strategy is really to be very targeted and to be very focused. So these mock strategies become an interesting input and the burden is still on the team to down-select into the most important areas for investment. So forcing that choice still, I think, is a human element and needs that layer of additional judgment, which is very context specific to the company. So this is sort of right away, I think people should be doing this. hoice still, I think, is a human element and needs that layer of additional judgment, which is very context specific to the company. So this is sort of right away, I think people should be doing this. I think more medium term and probably not too distant future and likely sooner than we all think, there's probably going to be, sort of the model that resonates with me is sort of the multi-agent model, which is you probably have different components of the strategy workflow automated. So you probably have a strategy agent, you probably have a roadmap or feature agent, you have maybe a engineering agent, and these can communicate amongst each other to cycle through results and iterate. And I think Armand Ruiz from IBM has some good definitional frameworks on some of the stuff. He shares it often on LinkedIn. But let's take a simple example. I can easily imagine something like this for a topic like onboarding. So every company and product team obsesses about their onboarding experience. And today, there are advanced experimentation frameworks. So imagine you're expecting a large surge in traffic. There's these sort of experimental frameworks like multi-armed bandits that can really help you get to the optimal sort of variation very quickly in real time. And there are variations of that, like contextual multi-armed bandits, there's combinatorial bandits. But the interesting thing is they still rely on human design of the variations, the different variations that you test. Even though the experimentation framework is very sophisticated, the variations are still human generated. Now imagine if those variations could actually be generated through generative AI and could be plugged into the advanced experimentation frameworks. The possibilities become infinite. And really, you might be surprised by what you find is the winning onboarding experience that you couldn't even have humanly imagined. And it might be different for every user, different for every sort of territory, etc. That is such a cool idea, I just want to say. This agent that's just running, thinking about ways to optimize your onboarding, coming up with concepts that you probably review, like, "Cool, let's try it." And then it does it, ships an experiment to run it and just is constantly optimizing your onboarding. Holy shit, that's an awesome idea. I think everyone will have this. And then what that makes me think there's going to be some company that's built the best onboarding agent. That's what you're going to be paying for.…

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