What skills do you need to be an AI product manager? How does an AI product strategy differ from traditional hardware and software product management? What is the recipe for success? Why should a company have an AI product manager? See how it all comes together in this comprehensive guide on product management for AI products and services.
AI product management can seem complicated if you’re coming from a traditional product management role—there are so many new concepts to learn. However, if you build the capability to nail down an AI product strategy, your company will benefit substantially from better (and newer) products and services. We’ll start with the fundamental questions: 1) What is AI product management after all? 2) Why should my company hire an AI product manager?
Next, we’ll review some core principles of effective AI product management, and go through the steps to help guide AI efforts for business leaders: Define your goals; develop a high-level roadmap; establish a process for developing features; build features that meet those goals; launch those features into production. Finally, we’ll talk about how launching the first products, what makes them successful, and a few future considerations.
AI product management and AI Product Manager
To create successful AI products and services, companies need to have a dedicated AI product manager. But what is AI product management in the first place, and what skills does this role require?
AI product management is the process of overseeing the development and deployment of artificial intelligence products and services. This involves working with cross functional teams of engineers, data scientists, and other stakeholders to define requirements, design solutions, test prototypes, and deploy finished products. In contrast to traditional software or hardware product management, the AI product manager must possess specific skills to manage the unique challenges posed by artificial intelligence applications. These include:
- Strong engineering expertise to understand complex technical problems and designing elegant solutions
- Proven experience in data science and machine learning to effectively manage and utilize data
- Excellent communication skills to facilitate collaboration between stakeholders and ensure that the product goals are clear
- Product management experience to understand how to bring a product from concept to market
- AI domain expertise to identify opportunities and navigate the ethical concerns surrounding artificial intelligence.
AI product management requires more than just data science skills—it requires an understanding of marketing and business strategy as well as various aspects of design. A quick Google search will reveal countless job descriptions for AI product managers with very little in common among them besides experience with machine learning models. That can be frustrating if you’re trying to determine whether or not you have what it takes to be an AI product manager.
There are several differences between managing products in AI vs. managing legacy products, such as network equipment and software solutions. With those types of products and services, there’s usually some kind of established market where demand is relatively stable (even if there are spikes). In contrast, AI-related markets tend to be less predictable due to growing data-driven applications, where opportunities pop up based on real-time customer behavior.
For example, if you want to build a self-driving car AI systems, you need to understand what types of sensors are needed on such a vehicle. But then you need to understand where those sensors will be placed on your vehicle and how they will interact with other components inside your car. And then there’s more work still—you have to figure out how those pieces are going to interact with each other and whether they can be made in time. It’s important not only that you know what technology exists but also what it can’t do yet.
The role of AI product managers is essential for companies looking to create successful AI products and services. With the right skillset, an AI product manager can help turn a company’s vision into reality. Read further tips for AI product managers.
Back to Fundamentals: the AI Roadmap
Companies should seek to create long-term visibility into their current brand while they also figure out what role an AI product will play in future efforts. That way, consumers will understand what you do right now and also have an idea of what you plan to do next—and why that matters to them.
In other words, AI products require companies to be clear about where they want to go and how AI strategy can help get them there. This requires having a vision for your business as well as your customers’ experience, ensuring business success. It means creating a roadmap for AI that maps to where you want your business to be in three years or five years or ten years down the road.
Having this clarity is critical when marketing any kind of product or service, but especially so when marketing something new like AI. It takes time—it takes planning—but if done well, it can set up companies and brands for long-term success by helping people see what’s coming down the pike before those changes become disruptive forces on society at large.
Planning, Researching, and Defining the Market
The first thing AI product managers do is to define a market opportunity. The goal is to find out whether there’s a need for an AI product in that market. It is important to consider that with AI products and services the potential opportunity may or may not come from your industry niche, hence a broader scoping and research is a must. As the opportunity is identified, you can begin creating your strategy around customers who are buying other related products or services in that space.
Once you know who you’re targeting, it’s time to conduct research. You’ll want to look into customer satisfaction, needs, pain points, demographics, company size and structure (especially if you plan on selling directly), competitors, trends in their industry as well as their business model. This will help you get a better idea of what features they should have, how much they should cost, and how they fit into your overall business model. All of these things will help shape your AI product strategy.
Building a Vision with Your Team
Building a strong product vision with your team is key to making sure everyone’s on board and working toward shared goals. Before going any further, though, it’s important to make sure you have defined what an AI product or service even is. Different industries mean different things when they say AI or AI technology.
For example, while some think of AI as any technology that improves functionality over time—such as a thermostat that learns your preferences—others associate it with a specific use case: translation apps like Google Translate or Amazon Alexa products like Echo Show.
Once you understand how you define AI in your space, it will be easier to build up your product vision. If you’re defining AI as anything that makes something more functional over time, then consider asking yourself these questions: What problem are we trying to solve? How does our product address those problems? What features do we need to include?
If you are defining AI solutions as a tool for specific tasks such as image recognition or speech-to-text conversion, then ask yourself these questions instead: What does our user need? How can we best meet their needs? How do we create competitive advantage? Regardless of which definition of AI you choose, keep these three tips in mind when building out your product vision. First, make sure your product addresses real problems people face every day. Second, don’t forget about sales and marketing!
Validation Through Iterative Prototyping
The software can change quickly, and you need to validate each idea thoroughly before moving on. By prototyping multiple times, a product manager can see how an AI project may or may not work out. Each time they receive feedback on a prototype, they should revise their plans accordingly, working across the organization from data scientists to business development managers.
Validation through iterative prototyping is key to knowing whether or not your AI product strategy will be successful. Because data changes constantly, continuous testing helps prevent wasted investment in ideas that aren’t viable or useful. The earlier you can catch flaws in your plan, after all, the less money and time it’ll cost to fix them.
Validating with Real Users
If you’re creating a new product in an existing market, you can use your customers to help you validate your AI-powered product idea. They don’t need to know anything about machine learning, deep learning or artificial intelligence—you just have to ask them what features they want in your next product.
If no one else is using AI-powered products yet, it might take some extra legwork on your part. You might have to dig a little deeper into how people would use your new product or service and why they would choose to do so (instead of choosing more traditional alternatives). But if you can uncover those insights, getting buy-in from other customers should be much easier.
Making It Real
Not knowing your specific customer persona and underlying business process can put an AI product manager at a disadvantage as it will be hard to market your AI product if you don’t know who you’re marketing it to. It’s also important to consider what they might want out of a specific AI solution. After all, it will help guide decisions on whether there is a market for your company’s idea or not.
Consider surveys, interviews, focus groups—whatever data collection tactics would best reflect the needs of your target audience—to help you better define what you need to create to ensure success. The more information you have about how AI could impact your potential customers, from consumer feedback to real-world use cases, then the easier it will be to develop a viable AI product strategy.
The bottom line is that most AI products and services are still new territory for companies looking to bring them into existence; without taking ample time before even considering development, you could end up with an unsellable or nonviable solution. By doing market research beforehand, designers can make sure that they are developing something people want instead of just making something cool because they can.
Launching Your First Release
Now that you have a robust product strategy, it’s time to figure out how to launch your first release. If you’re launching an app or a machine learning service, it might mean creating a public beta. If you’re working on hardware, however, your first release may be selling directly to customers through your website. Regardless of what you decide to do with your first release, do not spend too much time engineering perfection. The most important thing is getting your product into people’s hands so they can start using it—and then iterating based on their feedback.
Future Considerations
AI is still in its early days, but companies are already beginning to introduce products that leverage machine learning, deep learning, and cognitive computing. With that said, there are several future considerations to consider when it comes to the product management of AI. One factor is pricing; most products on today’s market fall into two camps—low-cost consumer tools or high-end enterprise solutions.
It’s easy to see how consumer expectations play a role in success; if a product isn’t priced right, it won’t sell well. However, there’s also an expectation of high quality with enterprise solutions; businesses don’t want flimsy solutions—they expect automation tools to be reliable. This means companies will need to invest heavily in research and development (R&D) as they look toward building better AI products. To ensure they’re successful, they’ll need to create strong partnerships with other firms that can help them build out their R&D efforts.
This is where AI product managers come into play: They’re responsible for ensuring that their company’s portfolio of products makes sense and aligns with the overarching business objectives. When it comes to emerging technologies like AI, being able to predict market trends is important—and having someone who understands both sides can help your company stay ahead of competitors by providing insight about what customers might want next year or five years down the road.
