What’s the difference between AI and machine learning?

Machine learning (ML) is a branch of artificial intelligence (AI) focusing on creating computer algorithms to execute tasks without explicit programming. ML learns from data—it recognizes patterns, makes predictions, and adapts to changing inputs—simulating a form of "human-like" problem-solving. At its core lies data—algorithms trained on extensive datasets to recognize patterns and relationships, enabling predictions on new, unseen data. This training involves various techniques, enhancing AI capabilities across diverse domains. The impact of machine learning is seen in AI applications in our daily lives, such as predictive text on smartphones, customer service chatbots, and social media algorithms tailoring content recommendations. Recognizing ML as a dynamic field, ongoing research refines algorithms and introduces new approaches. As ML techniques evolve, AI systems expand their capabilities.

Exactly *how* does AI support personalization?

a) Segmenting customers: AI refines segmentation by considering behaviors and marketing purposes. b) Simplifying data: AI streamlines UX research by effectively filtering large data sets, implementing accurate processing techniques, and enabling personalized surveys. While AI offers benefits, its potential misuse emphasizes the importance of maintaining human research expertise. c) Predictive capabilities: AI aids UX researchers in predicting behavior, sometimes tapping into data beyond human monitoring capabilities.

How is AI used to enhance the user experience?

AI can improve UX by personalizing content, predicting preferences, and automating routine tasks. Through machine learning algorithms, AI analyzes user behavior, tailoring interfaces and recommendations. Chatbots and virtual assistants provide instant support, improving accessibility. AI-driven insights from data analytics refine products, ensuring continuous improvement. AI optimizes interactions, streamlines processes, and anticipates user needs, fostering a more intuitive, satisfying user experience.

What’s the difference between predictive and generative AI?

Predictive AI analyzes data to make predictions or classifications based on patterns, aiming to forecast future outcomes. It relies on historical data to make informed decisions. Generative AI, on the other hand, creates new data or content. It generates novel outputs, such as images, text, or music, often without explicit human input, showcasing the ability to produce original content. While predictive AI focuses on forecasting, genAI emphasizes creative output and new information.

How are governments regulating AI?

Policymakers worldwide are struggling to keep up with the speedy progress of AI technology. This has resulted in a fragmented regulatory system that heavily relies on self-regulation by tech companies. An absence of unified rules raises concerns about job automation, deepfaking images, and the development of self-aware AI. The US is notably grappling with balancing regulating AI risks and fostering its transformative potential, with legislative efforts still in flux. On October 30th, 2023, the Biden administration passed an Executive Order outlining New Standards for AI Safety and Security.

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GuidesResearch methods

Arriving at actionable user insights faster with AI-powered research

Last updated

9 January 2024

Author

Claire Bonneau

Reviewed by

Jean Kaluza

Chapters

1. Current state of AI adoption in user research

2. Concerns about AI-supported customer insights

4. A 7-step research strategy framework

5. Using AI to conduct efficient user research

Overview

Creating research questions with less effort and bringing ideas to life sooner Expediting data analysis and predictive modeling

6. Personalizing customer experience with AI

7. Overcoming AI adoption challenges

8. AI won’t replace human nuance

Using AI to conduct faster, more efficient user research

As you can see, gathering insights has traditionally been a lengthy process—a primary reason that AI is poised to be a real advantage.

Gone are the days of manually trawling endless data. AI-based tools are ready to do a lot of the heavy lifting, allowing you to focus on things that truly require a human element.

Here are a few areas where you can put AI to work like the assistant you’ve always dreamed of.

Creating research questions with less effort and bringing ideas to life sooner

Developing research materials and questions takes time, effort, and a lot of discussions. Perhaps you know what it's like to sit in on an ideation session and, by the end, feel like your brain is bursting from information overload. Or maybe you’ve felt the tediousness of brainstorming for materials like discussion guides.

If you break these processes down, they involve a significant number of steps, usually including

Determining what you already know
Figuring out what you need to find out
Agreeing on research
Landing on a hypothesis to test
Crafting excellent (clear) questions
Creating an affinity map
Developing a discussion guide, from start to finish
Testing your questions before posing them to participants

While you’ll still have to brainstorm to discover the most relevant topics, nowadays, you can plug your chosen topics into a tool like Notion AI, and it’ll write a discussion guide for you based on your bullet points.

It can even change the tone of your text or translate it for international participants.

If you feel your thoughts are almost coherent but want to spend less time on document creation and formatting, a tool like this could be your dream come true.

Again, check any genAI output to ensure nothing weird slips in.

Bringing your ideas to life sooner and testing usability

Sometimes, product development can be incredibly resource-intensive and still miss the mark 😭. For instance, some startups try to fill market gaps that simply don’t exist or focus too little on the UX. For instance, closing gaps between market understanding and UX is a significant area where AI comes in handy. Your ideas will take shape quicker than ever when you use AI for

Prototyping—you can transform late-night musings into rapid prototypes, efficiently validating practical value.
Market insights—AI can aid in identifying and filling legitimate market gaps.
Usability testing—AI enables the automation of usability testing through human-like bots. These bots are improving at conducting live chat interviews, facilitating virtual focus groups, and executing usability tests.

Ultimately, you could recover plenty of precious time to focus on applying your research findings.

Expediting data analysis and predictive modeling

AI, specifically machine learning, accelerates traditional survey analysis. In the past, analysis has been a challenging area for researchers without coding skills, but that’s changing. Significant advances include:

Text mining—swiftly analyzes extensive text, like call center transcripts, extracting valuable insights without human effort.
AI can supercharge sentiment analysis, determine feedback tone, and offer real-time adaptability and nuanced sentiment tagging.
Topic modeling identifies recurring themes in interview transcripts, streamlining analysis.
Automated thematic analysis extracts novel insights, while tools like Grain automate reporting, saving time and providing a concise overview of customer insights.