The best AI tools for UX : Graphic shows two welders as an abstract interpretation of adding AI to your UX toolbox

The Best AI Tools for UX Research & Design

The team at Marvin scoured the web to find the best AI tools for you to integrate into your UX research and design workflows.

11 mins read

“AI is a brilliant tool for people to be more productive.”

Don’t take it from us. That’s Bill Gates speaking at Davos 2024. 

As AI pervades industries across the globe, it’s already making headway in UX. Several new  tools are popping up out of nowhere. Existing ones are adding AI capabilities to their product offerings. Recently, we examined the transformative impact of AI in user research.

However, some researchers and designers are still skeptical of AI use.  

We understand their reservations. We’re here to convert the non-believers. AI will never replace human researchers and designers. Of that we’re convinced. It is also capable of augmenting our work so we can focus on deeper, more meaningful analysis.

We’ve scoured the web for the best UX AI tools for you to integrate into your workflow. Here’s what we’ll cover:

  • Benefits & Limitations of AI in UX
  • Where to Use AI in the UX Design Process
  • Choosing the Right UX Tool with AI
  • Top AI Tools to Augment UX Workflows
  • Best Practices for Integrating AI into UX

Get ready for helpful tips, tricks and tools to elevate your research and design.

Call out visual that says: AI will never replace good research. But it will make your job more joyful.

The Pros and Cons of AI in UX

AI promises to have a big impact on a UX teams:

  • Productivity & Efficiency – Designers and researchers can automate mundane tasks to improve productivity. Automation saves time and human effort. This accelerates a product’s development time.
  • Financial Standing – Outsourcing certain tasks to AI reduces designer and developer costs. This impacts overall profitability.
  • Creativity & Agility – AI’s ability to endlessly churn output allows designers to generate several new design concepts. Overcome design bottlenecks with AI. 

Let’s further examine the benefits and limitations of implementing AI in the UX process.

Benefits of Using AI in the UX Process

Traditional research methodology involves manual and tedious work. It’s slow — taking up a huge chunk of a researcher’s time. Not anymore, thanks to AI.

The benefits of using AI in the UX process are aplenty:

  1. Workflow Automation. AI automates repetitive and mundane tasks such as scheduling interviews and sending notifications. It can also conduct more advanced tasks such as user testing and data analysis, allowing researchers and designers to focus on more strategic work.
  1. Automated Data Collection. Collate data from multiple sources such as social media websites, website analytics, surveys, focus groups and usability testing. Tools can categorize and organize data without the need for human intervention.
  1. Real-time Data Analysis & Insights. Analyze large datasets faster with pattern recognition and anomaly detection. AI uncovers trends, patterns and insights missed by humans. It also analyzes data faster and more accurately than humans. This allows for a deeper understanding of user habits, preferences and needs. 
  1. Reduce Human Bias & Error. Manual transcription and traditional analysis gives rise to human error. AI mitigates this by removing human involvement until it’s necessary.
  1. Personalization. Companies continuously observe and record user behavior. AI algorithms can handle large volumes of this user data. AI provides customized recommendations tailored to various users After analyzing people’s preferences. With AI, interfaces feel tailor made for each individual.
  1. Predictive Capabilities. AI can build predictive models that humans simply can’t.  Algorithms anticipate user behavior and preferences with high accuracy. This opens the door for more intuitive interfaces and seamless experiences.
  1. Content Generation. Generative AI tools can help designers quickly create prototypes and wireframes. Options can be customized to different user groups. AI tools also offer writing assistance in creating audience-specific copy for product content. 
User Research Software Marvin is a Game-Changer

Limitations of AI Design & Research Tools

Leveraging the full potential of AI requires a deep understanding of its inner workings. How does it utilize data and impact users’ lives? As with any technology, it’s important to understand AI’s constraints: 

  1. Context – AI can’t understand how output feeds into study goals or research questions. AI has no background information about the product or users, and it doesn’t lean on insights from previous research. Systems don’t know what factors are most important to the researcher. They can’t ask pointed questions and alter the course of interviews. 
  1. Loss of Human Touch – AI lacks basic awareness of human psychology. It doesn’t have human empathy, creativity and emotion. For instance, in logo design: AI may be able to suggest color palettes. What it CANNOT do is capture the subtleties of human emotion – the type of reaction it evokes in users. 
  1. Ethical Considerations – All AI tools are trained on biased datasets (systematic bias). Any tools that claim otherwise are just plain wrong. Using a non-representative dataset introduces statistical biases. AI doesn’t discriminate and uses biased input if provided with it. It falls on the researcher to take this into consideration and produce bias-free insights. 
  1. Noisy data – AI algorithms are constantly referred to as a ‘black box’ – we might never know their inner workings. Some tools generate content without attributing the source of the information. A lack of citation means that AI output can’t be validated. Furthermore, it can be influenced by questionable sources (plenty of those out there!) and throw out inaccurate output. It also can give you two different answers for the same question –  there’s no consistency. A lack of transparency around the process introduces the element of doubt in AI output. Validate AI powered output before using it in analysis. 

AI will always need humans to corroborate results. Researchers and designers must ensure that AI has carried out its task correctly and fairly. Humans will always be responsible for final decision-making.

For a deeper dive, here’s more on the merits and demerits of AI in UX.

AI and the UX Design Process

AI can lend UX professionals a helping hand during different phases of the design process. Identify pain points or areas that need improvement in your existing workflow. Use these questions understand more about where you can integrate AI:

  • What are my current roadblocks?
  • What tasks need optimization?
  • What’s the expected outcome?

Check out our guide on how to use AI every stage of the UX process.

Designer Francois Bouniq-Mercier created this stunning visualization of the design process.

UX Process Stages Graphic

This gorgeous graphic showcases a classic UX design process based on design thinking principles. (As we continue, you’ll notice we’ve used it to identify the stages ripe for AI use.)

Researchers at Linköping University in Sweden investigated how to augment UX research and design with AI. They interviewed several UX professionals to find out how they were incorporating AI into their workflow.

Participants used generative AI tools like Midjourney, ChatGPT and Dall-E. 

Some queried the tools, looking for inspiration as they began the creative process. Others used it for benchmarking, editing color palettes and changing UI elements.

A general consensus among interviewees was AI will increasingly become part of their workflow. Output they received was of high quality and required very minor manual changes. Tools like Midjourney allow designers who aren’t experts in 3-D modeling to quickly iterate on designs. AI may even start to make design recommendations after reviewing final prototypes and user behavior.

It’s all about finding an AI tool that complements your existing processes.

Choosing the Right AI Tools for UX

Since all the hype surrounding AI from ChatGPT’s release, companies are racing to roll out new AI functionality. Be wary of tools slapping on “AI” in their marketing just for kicks (and clicks).

Features and functionality aside, consider these important factors when comparing AI tools:

  • Costs
  • Scalability
  • User Friendliness
  • Integrations

Don’t forget to pay heed to these important considerations before diving into a comparative analysis of the tools out there:

  • Business Goals. What business objectives does the project help satisfy?
  • Project Needs. Whether it’s a survey, user testing or data analysis – what does the project (and regular projects) require?
  • Features and Compatibility. What features do you need for this and future projects? (More in the section below)
  • Training & Support. What training resources and support does the company provide to ensure effective adoption and use of the tool?
  • Scalability & Flexibility. Will the tool be able to satisfy not only current needs, but future ones as well? UX Tools must adapt to evolving project needs and growing needs of the UX research process. 
  • Secondary Benefits. Look out for versatile AI tools with features that support other areas of the UX process. For example, Marvin acts as an AI research assistant, facilitating data collection and analysis in one place. It also seamlessly integrates with all your existing tools. Two birds with one stone. That’s Marvin.
  • Data Privacy Compliance. What regional and international regulations must be adhered to?

Privacy Concerns

Companies constantly recruit participants and users for interviews, focus groups and surveys. It’s their duty to protect user data at all costs. Concealing people’s personally identifiable information (PII) is of utmost importance.

Choose a tool that incorporates these data security measures:

  • Data Anonymization – remove PII from any collected or stored data
  • Data Encryption – prevent unauthorized access to sensitive information
  • Compliance – ensure tools abide by regulations. These include local data protection laws, industry norms and ethical guidelines. 
  • Limited Data Collection – minimize unnecessary data collection. Focus on collecting what matters.
  • User Consent – choose tools that are transparent with their data usage and security.

Marvin uses advanced privacy filters to blur faces and scrub out any PII from interviews recordings. It’s HIPAA, GDPR and SOC2 compliant, so your user data is always protected.

Top AI Tools to Augment UX Workflows

With this newfound knowledge of what to look out for, it’s now time to pick a tool. Without further ado, let’s dive into the universe of UX tools equipped with AI features. Here’s a laundry list of tools, categorized based on phase of the above design process. 

[It should go without saying, this list is not exhaustive. New tools launch every single day, and we’ll continue to monitor the space. These products are heavy hitters in the UX space as of March 2024.]

Phase I – Discover

Stay up-to-date with the market. Look for tools with data analysis capabilities. Algorithms analyze large and complex datasets accurately and help uncover insights rapidly. Conduct a competitive analysis. Market research tools can scrape and analyze data from competitor websites, social media and customer reviews to identify patterns and industry trends.

Tools can even catch insights that might’ve ordinarily been missed. They also carry out data analysis tasks in a fraction of the time.

Tools: IBM Watson Discovery, Google Trends, SEMRush, Research AI, Qualtrics, Marvin

Phase II – Define

Researchers or designers could use some help. Let AI tools become your new virtual assistant. Software can now transcribe user interviews as you conduct them, freeing up a designers mindspace for the questions ahead. From summarizing long transcripts to creating notes and synthesizing insights, let your research assistant conduct preliminary analysis for you. 

Tools for Research and Design Assistance: Adobe Sensei, GitHub Copilot, UX Pilot, Stable Diffusion, Marvin

Plus, all that data needs a place to live. Learn more about the universe of research repositories.

Phase III – Ideate

Brainstorm ideas and concepts using content generation tools. Create user journey narratives and maps to understand more about their experiences. Tools use sentiment analysis to analyze social media, forums and feedback to craft detailed user personas. 

Tools: ChatGPT, Blush, Canva, InVideo, TheyDo, QoQo

Use generative AI tools for UX & Product Writing. Populate wireframes with audience specific copy that’s optimized for search engines and different user personas.

Tools for UX Writing: Writer,, Jasper, Content Bot, Grammarly

Phase IV – Prototype

Automate design workflows. AI tools can generate Ul layouts from user requirements and design principles. Generate interactive and realistic wireframes in minutes with prototyping tools. Simply write a prompt and let the tool create several options for you to choose from. Designers can test and iterate on designs much faster with a shorter feedback loop. Save time and effort with these tools. 

Tools for Prototyping: Uizard, InVision Studio, Ando, Midjourney, Framer, Fronty, Visily, Botpress, Prott, Mockplus, Galileo AI, Relume

The above products create wireframes that serve as a foundation for design. You can then edit and tweak elements according to your liking. Tools offer color palette matching, font suggestions and provide access to a large library of icons and logos. They even provide recommendations, helping designers in making informed choices. 

Use AI to create stunning user interfaces. To enhance your UI and branding with AI, use the following:

Tools for UI Elements & Branding: Adobe Sensei, Figma, Canva, Fontjoy, Designhill Al Logo Maker, Khroma, Recraft AI, Sketch2React, Coolors, Colormind, Flair AI, Magician Design, Dall-E 2, Marvel AI

Phase IV – Evaluate

User research and behavioral analysis tools track usage patterns. They use heatmaps, eye tracking, session recordings, surveys and A/B tests to understand how users interact with Ul designs. Some tools offer predictive insights, using historical data combined with Al training data to simulate user behavior or responses.

Tools that offer user testing automation can expedite different aspects of testing such as sentiment analysis and usability testing. Some tools include AI-powered tools such as card sorting, tree testing and first click testing. These tests provide valuable insight into the user preferences and inform the design process moving forward.

AI tools can also help with design flaw detection. They provide instant feedback on design choices, potential usability issues and accessibility considerations.

Tools for Usability Testing: Maze, Visualeyes, Brainpool, Optimizely, Dscout, UserTesting, Lookback, Hotjar, Attention Insight

Best Practices for Integrating AI into UX Workflows

Below are some steps on how to best to incorporate AI into your work:

  1. Soft Launch – Start Small. Run tests on a smaller, manageable project to test AI’s handling of data. This allows you to identify and iron out any kinks or inefficiencies. Before releasing it across the entire organization, roll out AI tools on a limited scale. This enables an understanding of whether people are receptive to, and will likely adopt the technology. Here are some ideas about scaling research and design operations.
  1. Data Quality Assurance – Remember, your output is only as good as your input. (We know you’ve heard it before: Bad data in, bad data out.) Focus on good data quality to ensure you’re using datasets that are accurate, complete and consistent. Unbiased and reliable data generates helpful and actionable insights. Set explicit data validation guidelines for data collection to avoid errors and anomalies in the future. Address the quality of your data, don’t neglect it.
  1. Ensure Human Oversight – Keep user experience in mind throughout the process. Sounds simple enough, but it’s easy to become enamored by the capacity of AI. Researchers and designers can lose sight of who they’re designing for. Don’t fall into the trap. Ensure a varied group of individuals review and test the system before launch. 
  1. Validate Regularly – Don’t rely solely on AI’s output. Cross-check AI’s findings with human analysis to corroborate insights accurately. 
  1. Consider Ethical Implications – FACT: AI is trained on biased data. It’s a designer’s duty to ensure that any inherent biases don’t exist in design output. Clearly define the scope of AI used in any project and use it responsibly. Google’s Rida Qadri weighs in on the ethical dilemma facing researchers today.  
  1. Familiarization – Companies are rolling out new AI capabilities at a rate of knots. Stay up-to-date with the latest trends and future developments in the field. Prioritize continuous learning. We share our thoughts on how to master user research software
  1. Training – Establish best practices for employees at the company to follow. Learning the tool’s functionalities is important, but don’t forget to teach users how to interpret and use AI generated outputs. Educate them on how AI could fit into their workflow. Once they learn the ropes, they can offer feedback for improvements. (Marvin customers do this all the time, and we LOVE them for it!)
  1. Iterative Methodology – Iterate your work using AI to meet functional and aesthetic needs. Don’t merely accept the first round of AI generated assets. Keep refining the process until it meets your requirements. Create a feedback loop – test wireframes to get quick user feedback and observe where they fall short. If you don’t like something about a certain wireframe, change it. This creates well balanced and effective designs. 
  1. Collaborate – Constantly communicate with stakeholders, developers and end users. Involve them early in the process. Establish a shared understanding of business goals, the potential benefits and constraints of AI tools. Marrying diverse perspectives and user needs with project objectives leads to a more impactful user experience. Don’t believe us? Learn why industry expert Lou Rosenfeld thinks research can eliminate organizational silos

AI and UX: Better Together

AI’s impact on the user experience can’t be understated. We’re only at the beginning of the story. The rate at which AI tools are being rolled out is staggering. AI will only become larger in terms of its significance and reach.

Design’s mandate doesn’t waiver — let’s create experiences that delight users

AI will increasingly help us on this path. 

Using AI, experiences can be customized to individual needs and abilities. UX professionals can extract meaningful customer insights from feedback at scale. This improves functionality and aesthetics of the final product or service. It forms intuitive and engaging customer journeys.

AI empowers designers to create engaging products with greater purpose. Unlock greater productivity with AI.

Blog hero image by Pete Wright on Unsplash

withemes on instagram