Given that product managers regularly use data to support their hypothesis about new features, where do they fit into the AI equation? Likewise, how can they overcome knowledge gaps and lead successful product launches utilizing machine learning?
The answer is not as complicated as it may seem. A product manager with a solid grasp for the tactical process can rise to the challenge.
What product managers need to know about AI
Product Managers can often be confused about how deep their knowledge on AI should be. Here are a few points that can clear few of those confusions.
1. Understand the viability of integrating AI in the product
Basically, product managers need to understand what AI can do, and it’s limitations. Understanding this help product manager determine whether AI is even a viable solution to integrate in the product. Moreover, knowing what data is available, and how it can further AI development allows the product manager to steer the product tactically.
2. Understand how to integrate AI and improve AI models
Next, the product manager should know how AI will integrate into the current product, and how to improve the AI models moving forward.
These are a few questions that can help propel efficiency in the machine learning model.
- Does the collected data aid future initiatives?
- What could the other data sets be useful for future AI capabilities in the product?
The key is not to get bogged down with implementation level questions and stick to the high-level strategy.
3. Test your knowledge of useful applications of AI
A good exercise is to test your knowledge of useful applications of AI by thinking about how it can aid your own company. A good place to look is at your rule-based systems that depend on certain classifications.
Here at Leapfrog, we came up with managing employees through supervised learning. This system depends on classifications such as whether people are high or low performers, their total length of employment, and employee feedback. We can use this to determine goalposts of what employees can achieve in 3 months, 6 months, or in one year.
Continued data and feedback over time will grow these models. Ultimately, we can strengthen company culture, morale, and growth by investing AI into our employees. Looking at simple internal solutions allows us to start flexing our machine learning muscles.
A Venn diagram showing how deep learning is a kind of representation learning, which is in turn a kind of machine learning, which is used for many but not all approaches to AI. Each section of the Venn diagram includes an example of an AI technology.
How to structure AI products
A product manager can use their strategic knowledge to structure an AI project appropriately. The following high-level steps can guide the flow necessary for an AI project.
1. Get the data
Some clients may desire an AI solution, but will not have the tools in place for this to be viable. Without the right set of customer data, you will not be able to apply it to an AI problem strategically.
2. Explore and analyze the data
How can I use the data? Does this data support my product hypothesis? What initial findings do I see? The product manager can engage with the data at a high level to see if this fulfills the needs of the feature in question.
3. Validate the hypothesis with users
As with any project, it is imperative that we validate our assumptions with our user. Testing our initial ideas will allow us to make valuable iterations throughout the product cycle. By starting small and iterating, product development can gain speed.
4. Create quick and dirty models
The team can use the initial research and hypothesis to begin building quick models. Multiple questions will arise, but don’t lose sight of the initial hypothesis and goal.
5. Tune the models
Iterating on the initial models will continue to confirm or deny the original hypothesis. We can ask ourselves, “What is the minimum level of functionality that is acceptable?”. Keep tuning this model until reliable results are delivered.
Ask the right questions
At the end of the day, the most significant measure of success will be if a product manager can ask the right questions at every stage of the product cycle. They are steering the features from hypothesis through development.
Success in machine learning projects is ultimately measured similarly to regular development projects. If the product manager can retain their tactical approach, keep the team aligned around key product goals, and stay focused on the experience of the end-user, then the project will be in good hands.
About Leapfrog
Leapfrog is a full-stack technology services company that specializes in SaaS products, Web and Mobile Applications, and AI.
Our world-class teams have capabilities such as product design ( UI and UX), Front End and Back End Engineering, DevOps Engineering, Product Management, Data Engineering, and Machine Learning.
Want to take the next leap and learn more about integrating AI in your product?
