The AI Research Skills You Need in 2025
AI-powered tools are transforming UX research, introducing techniques like AI-powered moderation and advanced data analysis that are transforming how researchers gather insights and make decisions. Are you ready?
This guide highlights the essential AI research skills for 2025, practical applications of AI, and key considerations to help UX and insights professionals excel in this rapidly evolving field.
Essential AI Skills for UX Researchers
What you need to know as a UX researcher, insights professional or product leader in 2025:
Prompt Engineering: Crafting sophisticated, effective prompts is key to extracting accurate and meaningful insights from AI tools. Get the most from GPTs today!
AI-Moderated Qualitative Research: Mastering AI tools for interviews, data analysis, and reporting is essential—along with understanding their benefits and limitations. Understand when and which tools to use, how to optimize, scale research and blend qualitative and quantitative methods.
Research Repository Analysis: Efficiently query and extract insights from large datasets using AI tools to maximize the value of existing research. Learn how to ask questions of your repository, determine which data to access and when to trust AI — or not.
Partnering with Synthetic Users: Learn how to use AI-powered synthetic customers for initial testing, feedback, and refining ideas before engaging with real users. Add synthetic researchers and designers to develop a multi-agent system.
How AI is Changing UX Research
Efficiency Gains: AI-powered tools are reducing the time and cost of UX research, enabling teams to increase the frequency, speed, and scale of data collection.
Increased Volume: With lower costs and greater efficiency, researchers can explore more questions, conduct more studies, and dynamically organize and seek out answers from massive amounts of data.
Global Reach: AI tools break geographical barriers, allowing multilingual, AI-moderated interviews for global insights — and concept testing without users.
Key Considerations for Researchers Using AI
Mitigating Bias: Recognize and address potential biases in AI models to ensure fair and accurate research outcomes.
Validating AI Insights: Always verify AI-generated results to avoid "hallucinations"—plausible but incorrect information.
Participant Awareness: Account for the possibility of participants using AI tools to generate responses, which could affect data quality.
This post features highlights from Brilliant’s recent AI + UX Coffee Talk session, "Future-Proof Your Career: The AI Research Skills You Need in 2025," on LinkedIn Live. Watch the full session replay for more insights to help you adapt, innovate, and thrive as a researcher in the AI era.
Next Steps:
Watch the Replay: Access this and more AI + UX Coffee Talk session recordings here.
Explore Recommended Resources: Check out AI and UX by Gavin Lew and Ralph Schumacher Jr. and Sentient Design by Josh Clark and Veronika Kindred.
Join Future Events: Join Brilliant’s newsletter to stay updated on upcoming AI + UX Coffee Talks.
