Mastering AI Subfields: The New Future for Product Designers
Learn how AI and ML fit into the broader landscape of product design.
Have you selected which AI subfield you are going to specialize in? Or will you become an AI generalist? Future design roles will highlight a combination of HCI skills, experience, and AI/ML knowledge.
For example, Amazon will need help with AI search/recommendation business problems. Netflix needs help making recommendations for their viewers. Retail companies need help with their chatbots and utilize natural language processing (NLP) to solve their business problems.
Take a look at the AI and ML sections below, and if you find a subfield that interests you, start learning about it so that you are qualified to apply for design roles with AI/ML requirements.
Computer Science
Computer science focuses on creating software and solving problems using computers and their languages.
- AI and ML
- Software Engineering
- Theoretical Computer Science
- Computer Networks and Security
- Human-Computer Interaction (HCI) (👋 Product designers )
Data Science
Data science comprises methods and systems to extract knowledge and insights from data using computers.
- Machine Learning and Predictive Modeling
- Data Visualization
- Big Data Analytics
- Data Engineering
- Business Intelligence (BI)
- Data Mining
- Natural Language Processing (NLP)
- Quantitative Analysis
Artificial Intelligence
Artificial Intelligence (AI) is about creating machines that can think and do tasks that usually need human intelligence. AI aims to improve efficiency and accuracy and to automate tasks, making them easier and faster for humans.
Subfields with AI:
- Machine Learning (Yes, ML is a subfield of AI)
- Information Retrieval/Search Algorithms
- AI Planning and Scheduling
- Natural Language Processing (NLP)
- Emotion AI (Affective Computing)
- Robotics
- Speech Recognition
- Knowledge Representation and Reasoning
- Computer Vision
Machine Learning
Machine learning (ML) allows computers to learn and improve from experience without being directly programmed. ML automates decision-making and predictions by enabling computers to learn and adapt from data.
Subfields within ML:
- Natural Language Processing (NLP)
- Computer Vision
- Predictive Analytics
- Anomaly Detection
- Recommender Systems
- Robotics and Control Systems
- Speech recognition
- Time Series Analysis
- Bioinformatics
Designer Perspective
When product design roles require AI/ML knowledge. A tech company is asking whether you have the following experience, knowledge and skills:
- Human-Computer Interaction (HCI) (experience)
- AI/ML knowledge (data processing, algorithms knowledge)
- AI subfield (such as search/information retrieval experience)
Does this start to make sense? Traditional product teams are organized differently than AI product teams.
Hiring AI Product Designers
For example, Netflix is hiring a Staff Product Designer, Member Experience.
Here is the Job posting (The link may not work if the job posting has been filled)
Let's look at the requirements:
- The team designs the end-to-end member experience from the moment someone joins Netflix. (HCI requirement)
- This role focuses on emerging and experimental consumer-facing Search and AI experiences powered by machine learning to maximize member satisfaction. (AI/ML subfield)
- Design and build new AI experiences for Netflix product features. (HCI + AI subfield)
- Experience designing and shipping AI or conversational products.
Look, this is what the future of design looks like. You must be a top 10% designer with great HCI skills. Then, you will need to know AI/ML processes. Lastly, you must merge your HCI skills with AI/ML knowledge to become an AI product designer who can operate in an AI product team.
Summary
I don't have to tell you this. But it is time to adapt and learn to be an AI product designer.
Written by Leo Vroegindewey, B2B CX Consultant
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