Common AI Use Cases
These examples show how AI and ML are used in products and how product teams will evolve to incorporate AI use cases.
• AI product teams will specialize in AI subfields.
As AI usefulness improves, more use cases will be identified, making it worthwhile for companies to invest and build AI into product experiences. As a result, tech companies will continue to set up product teams around AI subfield expertise.
In a previous post, I mentioned all the AI subfields you should be familiar with to navigate the AI landscape confidently. I am trying to help you look at AI differently.
AI is not some monolith but a fractured group of many different technologies that are a part of AI. I am writing this guide on AI and ML to help you start your journey with a broad overview of AI. Machine learning is great because it's prevalent across all the AI subfields you will encounter. It will be a good foundation as you get more familiar with combining AI/ML knowledge with your UX skills and methods.
Companies Using AI/ML
The examples below will give insight into how pervasive AI is among companies. I want to point out that sometimes, a company will build a product around just one AI subfield.
Take Grammarly, for example. Their entire product revolves around solving the problem of helping people write well. They use natural language processing (NLP) throughout their product.
Other companies like Meta will use different AI/ML tech branches in many different products across their company.
Designer Perspective
How often have you seen a design role advertised on LinkedIn that mentions the specific product team you will be working on?
Some examples:
- Search, purchase, post-purchase, and loyalty teams
- Growth teams
- iOS and Android mobile application teams
- Developer tools teams
- Enterprise solution teams
Now, think of AI product teams and how they will look differently in the future.
- Search algorithms/Information retrieval team (Netflix)
- Speech recognition (Alexa)
- Computer vision (Pinterest, Meta)
- Natural language processing (Grammarly, Github)
I've already seen a lot of design roles with AI/ML requirements for designing chatbots (NLP) and search experiences (ML).
You will need to understand the technical aspects of AI/ML technology and how they intersect with UX design methods.
References
Written by Leo Vroegindewey, B2B CX Consultant
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