The noise about AI is deafening. It is not all bad, there is a dire need to put a filter on it.
The product managers need to work with varied data depending on the goal of the stakeholders and the objective of the product.
Product Managers is a generic term that has been widened into different types of roles and responsibilities within multiple industries needing a wide range of skills. Let’s try to break down the different types of Product Managers.
Types of Product Managers
The role of the Product Manager is diversified to reflect the complex landscape of the Product Development Process. Depending on the core nature of the product and the objective of the product enhancement, a product manager could be wearing multiple hats.
Broadly Product Managers can be classified as per Skills, Domain, and Product Requirements.
Listing the different types of Product Managers as -
- Technical Product Manager
- Growth Product Manager
- Data Product Manager
- UX / UI Product Manager
- Domain Product Manager
- The domain can be Healthcare, Fintech, InsurTech, Web3, etc.
For each of these product manager types, the product management process sits at the core where the approach requires consistent quality of upskilling and learning new trends required in a Product Manager role.
Read more about the Product Management approaches from my last blog post here -
5 simple ways to get started in Product Management
AI is emerging as the flagship feature for every Digital Product that is released today. For a Product Manager, it is imperative to understand the different types of AI as well as different use cases of AI to integrate into the Product.
Different types of AI
The term AI is used interchangeably for everything. In the Digital Product world, AI has been in use for a long time.
Consider Spotify’s Recommendation Algorithm in use, Google Translate, and all the ways it has helped me settle into my new life in Germany. These are everyday uses of AI in our lives which we have been experiencing firsthand for a long time.
So what makes Open AI so special that it is synonymous with the use of AI?
ChatGPT is a landmark product that integrates multiple types of AI such as ML, NLP, and Generative AI to provide a seamless experience for users.
As a Product Manager, I am in awe of it.
All this being said, I want to provide a raw differentiation of multiple types of AI that a product manager might use to integrate into a digital product or use it to ease their life a bit in this ever-changing landscape of Product Management.
The way I see it there are broadly 4 types of AI -
- Machine Learning
- Natural Language Processing
- DeepML
- Generative AI
This is not news, we all have come across these terms but as a Product Manager, I want to highlight the importance of using AI correctly.
Different Ways to Use AI for Product Managers
GenAI for Product Lifecycle Management
The new kid on the block, per se, where the tech world now uses GenAI as synonymous with AI as a whole.
As a Product Manager, it is very tempting to run each problem statement through something like ChatGPT and then reverse engineer the solution from the suggestions provided by ChatGPT. It may not be a bad idea but it is important to hold the context and form your storyline.
I enjoyed this post about using AI in product management -
Teaming Up with AI: Your New Best Friend in Product Management
The other side is that Product Managers should be aware of the different use cases for different types of AI. In addition to knowing the product it is important to know the basics of AI.
ML for Technical Improvements and Efficiency
The most popular form of AI that has been used in digital products for a long time.
Imagine a Fraud Detection System or a Recommendation Algorithm, it is a Machine Learning Algorithm in action. In fear of going too much into specifics of Machine Learning, from a product manager’s perspective, a basic knowledge of Machine Learning algorithms and the way to use structured data or unstructured data to implement such algorithms in the products will be of immense benefit.
Here’s a course I completed that added immense value -
Machine Learning for Big Data and Text Processing: Foundations | Professional Education
The crux of it is that when there is a feature request to integrate AI in a digital product, it is the job of a Product Manager to interpret the term AI in the best way possible for the stakeholders and the users.
NLP and Deep Learning for natural language processing and image processing
Diving into a more niche type of AI, Natural Language Processing(NLP) and Deep Learning are used in crucial industries.
ChatBots used for sentimental analysis or image processing in Healthcare applications are some of the uses for NLP and Deep Learning respectively.
Product Managers working with sensitive applications within industries like fintech and healthcare can greatly benefit from knowing the basics of these types of AI.
Conclusion
AI has already become an integral part of our professional life. It is creeping rapidly into our personal lives as well.
To drive the change a Product Manager not only needs to be well versed with the product they are developing but also with the emerging tech they are trying to integrate.
A Product Manager needs to find the right approach to integrate AI in their products while using AI the right way to not fall behind the competition.
