AI Product Management

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Achieve Success in AI Product Management [Complete Guide]

When it comes to advanced technological developments, AI product management continues to shine. Nonetheless, a clear roadmap towards successful implementation of AI tools may look daunting. In this guide, we strive to shed light on key challenges you might have been wrestling with, and offer proven strategies and practices that lead to a triumphant AI product strategy. Whether you are a startup or a well-established corporation, you'll find useful insights drawn from comprehensive case studies and personal experiences to ensure success in your AI journeys. So, let's jump right in!

Understanding AI Strategy in Product Management

First and foremost, to align your AI strategy with your business goals, it is essential to understand the critical differentiators between conventional product management and AI product management. This understanding can allow you to leverage both the similarities and differences for your strategic advantage.

Key takeaway: Unlike traditional products, AI products are research-based. Therefore, they necessitate distinctive strategies and considerations, especially when it comes to data governance, user discovery, UX, and bridging gaps between research and engineering teams.

The Three Pillars in the Generated AI Flywheel

When striving to actualize successful AI products, consider these three crucial sectors in the generated AI flywheel:

  1. Data: The quality and quantity of data are significant factors influencing the performance of AI models.
  2. Algorithms: How well do you understand your products' position in your AI value chain? Have a handle on observability and interpretation of your AI models performance.
  3. User Feedback: Never underestimate the power of user feedback. It can supply insights on UI/UX, governance, ethics, and privacy matters – all of which are critical for improving AI-powered products.
    1. Case Study: Harnessing AI for Efficient Enterprise Infrastructure

      To give you a practical taste of AI product management, let's examine Neuromodix, an AI startup that developed a product to deploy intensive machine learning models on CPUs instead of GPUs, aiding businesses to tackle expensive large scale inferences.

      Key takeaway: By understanding their client's pain points, Neuromodix managed to provide user-friendly, flexible deployment and reduced complexity, leading to faster deployment and cost-effectiveness. This case highlights how identifying and meeting users' needs proves to be a critical success factor in AI product management.

      Finding Success: Four Rapid Strategies for AI Product Management

      Finally, based on our discussion so far, here are four concise strategies that can help pave the way towards success in AI product management:

  • Begin small: Start with a small project or a pilot workstream.
  • Prioritize data: Use a data-driven approach for prioritizing your product roadmap.
  • Seek simplicity: Complexity doesn't always mean superiority. Keep things simple for better user adoption.
  • Target the majority: Cater for the majority who are reluctant buyers - make it easy for them to understand the value of your solution.

In conclusion, effective AI product management is all about understanding and managing the intricacies involved in the marriage of technology, data, user needs, and business requirements. Don't be afraid to start small or rely on data-driven strategies to make decisions. Remember, simplicity is key for user-friendliness, and focusing on the majority’s needs often leads to broad-based success.

Video Transcription

We'll get started. And, I've got a lot of material to share with you today as it pertains to AI product management.But before, we do so, I would like to invite you to write in the chat, about, any challenges that you face or you have faced. When you implement these new tools and technologies. So feel free to, write your thoughts, in the chat. Anyone wants to share? Thank you, Joey, for sharing. So where to begin with AI strategy. And I will share a framework with you today, the generated AI flywheel, which I'm hoping it's helpful to to give you a head start on that. Any other thoughts before we move forward with the agenda for today's talk. Okay.

So feel free to continue, like, thinking, and we can come back to that, towards the end since we've got, a lot of things to discuss and, just 20 minutes to do so. So, the agenda for today, is going to be just three parts. So we're very brief, AI deep dive, when it comes to, releasing the landscape of what has happened over the last couple of, years. And, seeing a case study. So actually an AI product that was launched, to help to give you some guidelines in terms of how you can think about launching, these types of products. And and lastly, I will leave you with 4 strategies. 1 how you can implement, your AI product strategy and how you can get started with your past 2 ROI. And I'm hoping to get questions, at the end of the session, but feel free if you have a question to write it in the chat and we'll come back to it.

So a little bit of background about me and how I started in this, space. First, I started Hotlabs which is an AI product management accelerator that helps dictate founders to grow their startups and small meters, medium sized companies to grow their portfolio with AI ML. Before that, I've been, an AI professor at the University of Florida, where I've built a research portfolio across and I've been in product, for the past, years in couple of different startups and sectors, B2B and B2C. And my passion started actually with my PhD from MIT and the chief camera. So I'm really excited to share, some of my, experiences with you today. So, the first part of the talk as we discussed will be, an AI deep dive. And actually, what what's the differentiator between AI product management and how we've been building products, in the past.

So, learning from previous products and previous launches, elements, large language models have been around for the past few years, but actually we've got NLP and previous, previous innovations in the past. So learning from those first myth is that launching these AI products, follows the same fundamental principles that we've followed over the past decades. So there are a lot of similarities that we are seeing, when it comes to strategy, when it comes to, when it comes to owning, the the competitive advantage, for example, for for these, product portfolios, But, really where, the differentiator is, is that we need to understand, that these products are research based.

And similar with previous, AI innovations when it came to NLP, Machine, vision, computer vision. We need to, have that cadence with research teams and engineering teams and also, bridge that gap with user discovery, user research, UX, and a lot of new implications when it comes to governance and data because there's a lot to track and to evaluate with, LNMs. And we need to make sure that we improve, the performance of them as well. So to give you, an example of a previous product. Some of you may remember, this, IBM Watson's oncology project. And there there this was canceled after $62,000,000 investment. Why this happened? Because training data was not actually representative of what the doctors, guidelines where. So it's really, the low data quality that define those, limiting factors of AI models and led to the constellation of the project.

So it's really understanding that data principles and governance, and we'll see that in the framework that I'm gonna share with you today. Another myth is that we are really in this, phase right now. What I would call is the wow factor. Which really drives those AI product successes. So a lot of people are thinking that they will buy this product simply because it's the AI technology. That is so innovative. So cool. However, in reality, we really need to, understand the long term experience, but what's the value on and also, efficiency and trust in these products. So, to give you an example of, a past, startup, so I'm key robot it started, with building interactive toys that, basically built it feel alive. It was, when emotional intelligence was starting to become an emerging tech at that point a couple of years ago, and, we see that this novelty tech actually, overkilled the functionality aspect.

So we see that the startup declared bankrupt see after raising more than 200,000,000 in investments. And, that Another example where we see that the user aspects and the usability of the of of the features that were developed were not actually were not, depicting the technology that and the problem that it was solving. Myth number 3 that I want to share with you is that a lot many times and especially with small, and, companies and startups, we see that there is, this tendency from executives or founders to to believe that complexity equals superiority in their products. So what I mean with that is that many times, the complex, more complex technology is is that that would be their the key differentiating factor that, will define, how these AI products are appealing and or effective. However, in reality, what we see, and I will share an example with you, in the next slide, is that we really want this products to be, 1st of all, user friendly. As I mentioned, usability. Not only from the UX standpoint, I will share that slide with you, in a bit, solving real customer problems and efficiency.

Because we need to make sure all these 3, are resolved. And, you can't imagine, like, how many times I I've seen founders and products overlooking these aspects, because again, as I mentioned, these are AI research products. And we need to make sure that we set realistic expectations up front so that we avoid any, issues of misunderstanding and misaligners with customer needs. So to give you an example 7 dreamers was a robot. It was designed to actually do laundry for you. However, the product over promise the needs. And there was significant effort that was needed, in order to do a laundry. Basically, you had to pre fold your laundry. So the product was not delivering its service, and the company closed after raising about a 100,000,000 investments.

So you can see how important it is to have that radical calendar when you are developing products, with your teams. And of course, that, helping a stakeholder management with engineering research teams, because this there's a lot happening every day. So having that open funnel and communication with everybody is really the key. Now, I will share with you some frameworks and, an overview because we've seen a lot from an engineering standpoint, and I'm breaking it down from a product perspective, but it comes to really understanding these generated AI concepts. So, a lot of you may have seen, like, similar graphs in the past in terms of how a lens work and, just to give an overview, imagining that we have a prompt, we want to, let's say, make a cake. For instance, there are a lot of processes that need to be orchestrated, in order for this to happen. This is not the point of the talk today.

But just to give that idea, if in order to have that output with the needs that we want, with the perfect cake that we want our user to to be pleased about. There there will be a lot of processes and not just the input and the data that we put in the system to to be of good quality and of good quantity as well. But a lot of training processes, pre processing, the fine tuning aspect of the evaluation process, so that we have an output that, is not harmful for the users and also, can be evaluated properly. So how do we start, you know, with all of that? So I developed that, generated AI flywheel that encompasses, these a lot of aspects in 3 main pillars. So the 3 pillars that I would like to share with you And these are not fundamentally different from previous, projects that you may have been running with, AI and ML. First of all, data. Of course, we know that data is a fuel of other lines, like, in previous projects that you may have been running with ML.

Algorithms, the AI models themselves, but, also equally important that I want to point out it's the user feedback. And that's a key differentiator when it comes to, these projects because in a lot of times, it's overlooked, and this is something from a product standpoint that we need to pay attention a lot. So that we integrate that early on in the equation. And we keep those they are users informed and updated, as we, develop the products. And we keep iterating on them. So there are a lot of aspects that we want deep dive today, but there is a lot of material that I've developed and having to share more links with you if you're interested. So when it comes to data, a lot of a lot of aspects, which I have gathered is quality, quantity, and safety.

When it comes to AI models, it's important to understand where your product is positioned in the AI value chain. There is a tech stack. Happy to share more about that. If ever, if anyone is interested, observability of the models, like, how is your modem performing over time? Because you will have your launch, but then you need to be continuously monitoring and observing how your models are improving as you get more feedback from your users. In how you interpret those results and how you can continue to get buy in, from your executive teams, and founding teams. When it comes to iterating with new, different variations of those models. And finally, user feedback, not only from a UI UX some point, but also from a governance ethics, some point privacy, a lot of concerns.

And how do your users perceive that product? Given that now there is this AI component to it. So with that, I give you an overview of how you can start thinking with your new product launch or integrating new features, that are AI driven in your products. And now I will walk you through, an example, case study. From a company that, is developing AI power products, before we close with the strategies that I have for you towards the end of the presentation. So the case study, is, based with a company, a startup, series a startup. Which was founded out of research, and, the reason I picked that startup is because of the fundamental important problem that they've been solving, which is basically the, to deploy all these, intensive and heavily, computational heavily heavy models on CPUs.

Because as we all know, these models have been running primarily on GPU. So it's a lot of specialized hardware and teams to, orchestration of these different layers to work synergistically together so that we can have these, products, running smoothly. And the reason that this is important, there is a spike especially with enterprise level software is that this year, 75% of organizations predict that will shift from actually piloting, to operationalizing AI that's, based on Gartner's, reports. And that this is the boom that we expect to see in enterprise level software. Now at the same time, we see that large scale inference for enterprises, is very expensive. So 80% of ML cost comes from inference. If you've been running ML projects in the past, that's something that is very concerning. So to get, into the specifics, I want to share with you a case study.

With one of neuromodics customers. This is, real case study, but with the fictitious customers, just for data privacy, reasons. So we'll go through that together. We see that Jack is one of the lead, data scientists in that fictitious, retail chain company. And since, they want to actually, install, CCTV cameras in all their stores and expand their portfolio, Jack is tasked to actually find ways to be able to track people's movements and insights that can help really expand the stores, experience and and layout. So, out of of that, Jack, is really facing, an important problem, which is basically how he can run, those cloud based AI inference models. Without such a high latency and cost. Because right now, on large cloud providers, it's very expensive operation to run that, on GPU's. So Jack is looking to deploy those models directly on CCDV cameras, and he wants to solve that problem. So let's see the customer pain points here.

So first of all, we see we know that edge devices of the cameras that these stores have installed don't have GBUs. And secondly, we know that if he is running, inference, which is the model prediction on CPUs that that has very high latency. So how is he going to actually run this ML model own device without sacrificing performance of these models because that that's the key. So regarding pain point number 1, that edge devices don't have, GPUs. We know that this is a significant problem when we look at enterprise a model inference because 75% of those run on CPU. So, actually, Jack cannot run his inference of multiple locations due to GPU and availability.

And, what he what he's currently doing before neuromagic neuromodix product is the 6 step process. We don't have so much time to go through it, in detail, but just to give you an idea, he would select the model, select hardware, then improve, the model based on, yeah, each different cameras settings and configurations. Then test it out and finally evaluate it and scale scale that fleet of CCTV ecommerce essentially over for the the, course, over the the different retail locations that, the smart shopper manages. So this is a very complex process for Jack, because, he would need to run different models, with multiple hardware configuration systems, and this is a very time consuming and expensive process. And what that means for him and his team is that he would need skill and manage all these, ML inference models, at a very high cost, in a very complex process. So now, the process is actually, simplified, because now you can see that instead of doing all that work, and worrying about deployment and different models running in different devices.

He just needs to, test models, monitor them, and make sure that they can scale. So you can see clearly here where the the the products in this case Neuromodix product provides a differentiating value for for the customer. And, just to summarize, the value that that the customer got from using the product, GPU, performance when running on, CCTV cameras, which are which are not now expensive to run on customizable, settings. Yeah. It was a flexible deployment for them and it reduced complexity. So, in fact, some metrics, seven times faster deployment, and now the client could install double, people in their stores. So you can see, and I will walk through some of the strategies that neurologic did to actually be able to to lead to that, result.

But you can see clearly what the value of the product was and why AI was in particular helpful to be able to solve that complex problem for the client. Like, there was no other way that this could be bypassed. So that's the key takeaway that I want you to to remember from that case study. And also when it comes to thinking about strategies, like how in this case, this is an early, product, like, mostly 0 to 1 product that was developed and how in in these cases, we need to make sure that we have that product markets fit. So in the case, of neuromagic, they really establish the open software, standards, by engaging users in their Slack and GitHub communities, which are more than 2000 members, but also validated and incorporated that user feedback of the developers. Directly into their enterprise software, which is the Deeps parts, software that I have here. And the other strategy was to actually engage within the enterprises and clients early on so that you can get those proof of concept out and making sure, at the actual launch, that there is, there is value for the client.

And you can move forward with scaling that effort. So with that, I want to, leave you with 4 rapid strategies that can help you get started and and see results, with your product teams. So strategy number 1, demonstrating in winds by kicking off a pilot work stream. So you cannot shoot for the moon without really establishing those first steps. So to give you an idea, from my own experiences, at PDC, which is a software company, more than 5000 employees. I built an AI team of of 32 people based on a Hackathon project. So they started as an early prototype like we saw in in the case study and, in everything that I talked to you about to you today, starting small with a hackathon, and scaling that, into a separate work stream for the teams.

And you'll be amazed with the results that you can achieve, with that, especially because we said everything related to Gini AI right now is more on the research standpoint. So this will be helpful to, narrow down the risks and eliminate any biases that, your teams might have. Strategy number 2, really focusing on the data driven aspects of prioritizing your product road map. So many times you might have been doing guest work when it comes to which features should be we'd be shipping first. Or, have you acted on intuition when it comes to, what a customer actually needs? So really thinking through that roadmap prioritization from a data driven standpoint. And with that, I'm giving you just instead of thinking from an intuition standpoint, really assessing those feature rollouts from a quantitative perspective.

And you might be thinking like How can I do that? So some aspects, that you can think of, trying or engine what's the engineering effort? What's the impact and what's the time? So as soon as you establish those concrete metrics for your teams, your product launches will not only be more efficient, but also more tactical and more effective. And you will be able to launch, products and features as early as a couple of weeks. So with that, I want to stress out how critical it is to define those metrics and aligning your teams, with them. Strategy number 3, simplicity is the key to success. So with, here it is, how you can get started to spend this is particularly applicable for very large teams.

So, here is an example for a large organization, that, I've been running, an AI product for. So for the Dallas Fort Worth Airport, this large organization, wanted to a scale, AI deployments, and I help them to define what's their strategy and how to use that with a structured framework and using simple instructor mock ups, like the ones that I have here, and I can share that can lead to a strategy that can buy the can provide later on, more than a couple of 1,000,000 of investment, and as well as ROI.

And lastly, strategy number 4, especially, applicable for startups, in my own experience from spinning off, my own startup, from research, I experienced early on a couple of innovators that were very willing to pay for that product. However, the product was not so applicable, and easy to understand for the majority of the, of the customers. So it's important to understand whether the customer needs, how you can find that product market fit for the majority, who will be reluctant buyers. They will not feel that urgent pain. And you will need to make it easy for them to understand, because they always want to make safe decisions. They're not gonna be those risky bets. So making sure that you target that majority. And that's why you can simplicity is very important. In the key to success. So with that, I will close with everything that you see here, which is just a flavor of what I've been doing in the Hatchlabs, product management accelerator, helping companies and startups to innovate, with a set of, steps and frameworks from incubate, which is the pre launch, MVP stage to hatch where they can launch their products and finally grow and scale them.

So with that, I want to thank you so much for your attention and take any questions you have. And also don't hesitate to contact me if you, if you want to stay in touch or if you have other questions that we don't have time to address today. So I'm going to the chat, and Yes. I I believe, we'll ask you have the recording, and I will ask, to Caitlyn's question in terms of sharing the presentation afterwards. From Catherine, calculating the ROI for Genai pilots can be the goldwater, some strategies that you have used. So for that, like I shared. I look at it from the 3 different pillars, like, first of all, when it comes to data?

What's their and and this is all a risk based framework that I'm using when it comes to data. What are the risk that you have there when it comes to understanding your models, what are your risks in your models? And finally, when it comes to users, what are the risks that you're users may or may not adopt, and, calculating your ROI based on the success, the in the likelihood that your risk matrix, is gonna be low, high, or medium. Like, you can set some levers there. If there's a trial and error, I would say that depending on the product and the use case in the industry, this can change quite a lot. So I would say, establishing and, looking for, some, early a pilots and customers who can help, in terms of showcasing that value, like we, explained in the case study today, And then you can use that, as feedback for your next, experiments.

Any other questions? Okay. Sounds like, we're all set. Thank you so much for your time. And if there are any other questions, feel free to reach out. You got my email as well. And, Thank you. Hope you find it, helpful.