7 ways to use AI to improve user interviews in 2024

In this article, we evaluate seven ways to effectively incorporate AI into your user interview strategy—without losing the human element. We also cover how AI

• Enhances understanding and connection during interviews

• Improves interview inclusivity and accessibility

• Streamlines administrative interviewing tasks 

1. Craft targeted interview questions

Asking engaging interview questions demonstrates your genuine interest in understanding your users’ needs and challenges, while also helping you create a trusting environment that encourages them to disclose more information. 

However, determining which questions are most relevant to the user can often feel like fumbling in the dark, leading to overlooked pain points or an unfocused interview.

Integrating AI into the interview preparation process transforms this dynamic, generating targeted questions that are most relevant to the individual’s experience and background, rooted in detailed historical user insights, known personas, and behavior patterns. 

Tools and strategies for developing interview questions with AI

• Incorporate predictive analytics software: use platforms like DataRobot or Alteryx AI to predict future user behavior and shape forward-looking interview questions, based on historical data

• Use natural language processing (NLP): analyze previous interview transcripts with IBM Watson or Strong to identify keywords or themes to craft questions for follow-up conversations

• Personalize questions: use tools like Hotjar AI (learn how below), which builds insightful, targeted questions based on your scope, audience, and goals

2. Increase interview accessibility

Accessibility in interviews isn't just about ensuring participants can access and engage with the process—it's about making your customers feel valued and understood. 

A truly accessible interview acknowledges and accommodates the unique needs of each participant, whether it’s language barriers, visual impairments, or other specific requirements.

AI enhances interview accessibility by offering real-time adaptations and solutions that address individual needs, fostering a more inclusive environment.

Tools and strategies for increasing interview accessibility with AI

• Translate the meeting: incorporate tools like Worldly to translate video calls (audio, text, and captions) in real time, in 30+ languages, ensuring customers can comfortably express themselves in their native language

• Check digital compatibility: ensure your interview digital platforms are compatible with enhanced screen readers like JAWS or NVDA to facilitate people with visual impairments

• Adopt AI-driven sign language tools: partner with companies like Signapse AI, which has developed real-time synthetic sign language translators for websites and videos

3. Understand customer sentiment

Evaluating and analyzing user sentiment—the thoughts and feelings customers associate with your product or brand—is vital for continuously delighting your current customers, while also attracting new ones. 

Tools like Net Promoter Score® (NPS®), customer satisfaction (CSAT), and exit intent surveys provide a solid foundation for understanding your users’ baseline perception, but they miss nuances like sarcasm or the contextual reasoning behind inconsistent responses.

Implementing AI tools with advanced sentiment features—like analyzing verbal patterns or emotional undertones—helps bridge this gap, enabling you to discern not only what users are saying but how they’re saying it. 

Tools and strategies for understanding user sentiment with AI

• Use sentiment analysis software: incorporate sentiment analysis tools like MonkeyLearn or Lexalytics to gauge the emotional meaning behind users’ spoken or written words

• Implement emotion recognition tech: use tools like Smart Eye or CallMiner during interviews to interpret the context behind facial expressions and voice intonation

• Survey large-scale focus groups: quickly analyze the overall sentiment of up to 1,000 participants within a few minutes with platforms like Remesh

4. Enhance user personas

A user persona is a fictionalized character based on your current (or ideal) customer, which helps uncover the different ways real users search for, buy, and use your product or services.

As customer preferences, behaviors, and pain points shift, however, there’s a risk of these personas becoming outdated, leading to misguided interview strategies and missed business opportunities.

AI helps shape and inform user persona development by quickly processing vast amounts of up-to-date user data, so you can capture a more timely picture of your customers, ensuring your product is reflective of the current user landscape.

Tools and strategies for improving user personas with AI

• Harness data aggregation: use tools like Databox or Salesforce to consolidate and process multi-source user behavior data—CRM, web, social media analytics—to keep personas continuously informed by the latest insights

• Expand behavioral segmentation: deploy machine learning algorithms like k-means or hierarchical clustering to identify new audience segments based on online behavior, purchase history, or content preferences

• Enhance persona imagery: elevate your team’s emotional understanding and buy-in of each persona by auto-generating customer avatars and mood boards with AI-persona tools like UXPressia or Personify

5. Optimize interview documentation 

Documenting user interviews is essential for transforming lengthy conversations into shareable, actionable insights. Taking notes in real time, however, detracts from your ability to fully engage in the conversation, and manually reviewing them post-interview is time-consuming and susceptible to errors. 

AI improves the interview documentation process by managing administrative-intensive responsibilities—like transcribing, timestamping, note-taking, and highlighting—enabling you to concentrate on the most important task: connecting with the customer. 

Tools and strategies for improving interview documentation with AI

• Transcribe in real time: use tools like Hotjar Engage or Otter.ai to automatically transcribe conversations and ensure no detail goes undocumented

• Enhance audio quality: filter out background noise with platforms like Krisp, ensuring clearer recordings and accurate transcription

• Summarize key takeaways: use AI-based summary tools like AssemblyAI to distill lengthy transcriptions into key learnings

6. Reduce bias

Bias in user research, whether conscious or unconscious, skews results and limits the inclusivity of diverse user groups. For example, an interviewer’s personal beliefs might shape how interviewees are chosen, leading to confirmation bias, where data reflects the interviewer's opinion more than the genuine perspective of the interviewee.

AI helps counteract these tendencies by identifying bias patterns in data collection, paving the way for a more equitable representation of user feedback. 

Tools and strategies for reducing interview bias with AI

• Detect and monitor implicit biases: integrate bias detection algorithms like Fairness Indicators or IBM's AI Fairness 360 to continuously assess machine learning data for potential biases or skewed feedback

• Leverage conversational AI: use AI-powered chatbots like Xenioo or Tars for initial interview screening, guaranteeing uniformity in experience, tone, and questioning for each participant

• Optimize interviewee sampling: use AI tools like Qualtrics’ Research or Hotjar Engage, which sample from vast pools of users with machine learning to ensure a representative mix of participants

7. Improve the post-interview follow-up process

Following up after an interview strengthens your relationship with users, making them feel acknowledged for their contributions and time. Crafting timely and thoughtful follow-up responses can be challenging, however, especially when managing large interview cohorts. 

AI tools streamline this process by enabling you to personalize your post-interview communication with precision and relevance, ensuring users feel valued and understood—and not just like another data point. 

Tools and strategies for improving post-interview communication with AI

• Customize your messaging: use AI writing tools like Phrasee or Jasper to craft personalized emails that highlight key takeaways from the interview and demonstrate a genuine understanding of your customer’s feedback

• Automate follow-up scheduling: implement AI-driven scheduling tools like Motion or Clara to seamlessly coordinate future interviews based on user availability, ensuring a continuous and smooth customer journey

• Track user engagement: leverage Mixpanel’s or Pendo’s machine learning to monitor user engagement post-interview to further refine your follow-up communication strategy

Leverage AI to inform—but not replace—the human element of user conversations

Strategically incorporating AI into your user interviews doesn't just streamline the process—it enhances it, enabling you to have more targeted, relevant, and insightful conversations. 

By adopting a balanced approach that uses AI to inform (but not replace) the human element, you simultaneously improve user satisfaction while unlocking invaluable insights that drive business success.