AI for user research: A complete guide to transforming your research process

Summary

• The challenge: Research teams spend 60-80% of their time on manual tasks like transcribing and coding, leaving little time for strategic insights

• AI adoption surge: Product professionals increasingly use AI in workflows for analyzing research data, generating transcriptions, and crafting research questions

• Enterprise pressure: Teams face growing time and bandwidth constraints requiring streamlined research workflows

• Proven ROI: Organizations using AI report 15.8% revenue increase and 22.6% productivity improvement

• Four transformation areas: Harvard Business Review research identifies supporting current practices, replacing with synthetic data, filling gaps, and creating new insights

• Miro's AI advantage: Create with AI, Sidekicks, and research-to-backlog workflows transform scattered data into actionable insights

• Real success: Companies like Munich Re use AI-powered collaboration to build customer-centric products faster

Ever feel like your research team is drowning in data but starving for insights? You're collecting hours of interview recordings, mountains of survey responses, and endless feedback—but turning all that raw information into actionable insights feels like an impossible task. Meanwhile, product decisions need to happen faster than ever, and stakeholders are asking for research findings yesterday.

Sound familiar? You're not alone. This challenge has become the norm for research teams everywhere, from scrappy startups to Fortune 500 companies. But here's the thing: there's a better way to work.

AI for user research definition: Your new research superpower

So what exactly is AI for user research? Think of it as having a research assistant that never sleeps, never gets tired of repetitive tasks, and can process volumes of data at superhuman speed. AI for user research encompasses the tools and methods that use artificial intelligence to automate, enhance, and accelerate various aspects of the research process—from data collection and analysis to insight generation and report creation.

But here's what it's not: a replacement for human researchers. Instead, AI amplifies your existing skills and frees you from the tedious work so you can focus on what humans do best—strategic thinking, creative problem-solving, and building empathy with users.

The AI advantage: What's different now?

According to Harvard Business Review's latest research, gen AI offers firms unprecedented opportunities to understand customers, better assess the competitive environment, and push data-driven decision-making deep into their organizations. These fall into four categories: supporting current practices for collecting data and generating insights by making them faster, cheaper, or easier to scale up; replacing current practices by leveraging synthetic data (artificially generated data that mimics real people's behaviors and preferences); filling existing gaps in market understanding by obtaining insights and evidence that aren't available in conventional data; and creating new types of data and insights.

This transformation is happening across the industry. As Gartner reports, AI leaders continue to face challenges when it comes to proving GenAI's value to the business. Despite an average spend of $1.9 million on GenAI initiatives in 2024, less than 30% of AI leaders report their CEOs are happy with AI investment return. But the organizations that get it right are seeing real results: According to a recent Gartner survey, respondents reported 15.8% revenue increase, 15.2% cost savings and 22.6% productivity improvement on average.

AI for user research methods: Transforming every stage of your process

1. Research planning and question generation

Before you even talk to a single user, AI can help you craft better research plans. AI-powered tools can:

• Generate comprehensive research questions based on your objectives

• Suggest relevant methodologies for your specific use case

• Create participant screening criteria

• Design interview guides that cover all crucial areas

2. Data collection and analysis

This is where AI truly shines. Instead of spending hours transcribing and coding, AI can:

• Automatically transcribe interviews with high accuracy

• Identify key themes and patterns across multiple sessions

• Detect sentiment and emotional responses

• Extract specific insights related to your research questions

• Generate preliminary codes for qualitative analysis

3. Synthesis and reporting

Perhaps the most time-consuming part of research—turning findings into actionable insights—becomes dramatically faster with AI:

• Automatic generation of executive summaries

• Creation of user personas based on research data

• Development of journey maps from interview insights

• Production of stakeholder-ready reports with key quotes and evidence

4. Continuous research and monitoring

AI enables ongoing research at scale:

• Monitor user feedback across multiple channels

• Track sentiment changes over time

• Identify emerging user needs from support tickets and reviews

• Generate regular research updates without manual intervention

How to use AI for user research tools: The Miro advantage

When it comes to implementing AI in your research workflow, the key is finding tools that integrate seamlessly with your existing processes. This is where Miro's AI-powered features become your secret weapon for transforming research chaos into clarity.

Create with AI: Your research brainstorming companion

Miro's Create with AI feature acts like having a research strategist on your team 24/7. Need to generate research questions for your upcoming user interviews? Create with AI can suggest comprehensive question sets based on your research objectives. Planning a workshop to synthesize findings? It can help structure your agenda and create frameworks for analysis.

The beauty lies in how it works with your existing content. Select some sticky notes with user feedback, and Create with AI can help you:

• Cluster insights by themes

• Generate hypotheses from observations

• Create affinity maps automatically

• Transform scattered feedback into structured personas

Sidekicks: Your AI research partner

Think of Sidekicks as your personalized AI assistant that understands your specific research context. Unlike generic AI tools, Sidekicks learn from your board content and research patterns to provide tailored suggestions.

For example, if you're analyzing interview transcripts on your Miro board, a Sidekick can:

• Suggest follow-up questions based on emerging themes

• Recommend additional research methods

• Help prioritize insights based on frequency and impact

• Generate action items for your product team

From research to backlog: The complete workflow

One of the most powerful applications of AI in user research is transforming insights directly into actionable product improvements. Miro's AI capabilities can help you bridge the gap between research findings and development work.

This video demonstrates how to take raw research data and systematically convert it into prioritized development tasks—eliminating the traditional gap between research and execution.

The process typically involves:

1. Insight extraction: AI analyzes your research data to identify key findings

2. Problem framing: Transform user pain points into specific problem statements

3. Solution ideation: Generate potential solutions based on research insights

4. Backlog creation: Convert solutions into actionable user stories with clear acceptance criteria

5. Prioritization: Use research evidence to inform priority ranking

Real-world success: Munich Re's transformation

To see how this works in practice, look at Munich Re's approach to user research and product development. The global insurance leader faced a common challenge: how to make customer-centric decisions at scale while managing complex stakeholder needs.

By implementing Miro as their central innovation workspace, Munich Re transformed their research process. Instead of research insights living in isolation, they now flow seamlessly into product strategy sessions and development planning. Munich Re builds customer-centric products with Miro, demonstrating how visual collaboration combined with AI-powered insights can accelerate innovation.

The key to their success? They didn't just digitize their existing process—they reimagined how research, strategy, and execution could work together. Research findings now immediately feed into ideation sessions, user journey mapping, and feature prioritization—all happening on shared visual boards that keep everyone aligned.

AI-powered user research tools: Building your toolkit

While Miro provides the collaboration foundation, a comprehensive AI research toolkit includes several categories of tools:

Analysis and synthesis tools

• Automated transcription: Convert interviews to searchable text

• Theme identification: Surface patterns across multiple research sessions

• Sentiment analysis: Understand emotional responses at scale

Data visualization and insight sharing

• Dynamic reporting: Generate stakeholder-ready summaries automatically

• Interactive dashboards: Let stakeholders explore findings themselves

• Evidence linking: Connect insights back to source data for verification

Research planning and methodology

• Question generation: Create comprehensive interview guides

• Methodology recommendations: Get suggestions for research approaches

• Participant recruitment: Identify and screen research participants

The evolution continues: What's next for AI in research

The field is advancing rapidly. We'll see AI getting more involved in conducting user research, particularly in improving asynchronous studies. Now that it's possible for AI systems to process and summarize video clips, we're likely to see attempts at having AI analyze — or even conduct — usability studies. However, as Nielsen Norman Group notes, current systems are nowhere near being able to "watch" a behavioral research session like humans can and still struggle to provide deep insights.

This reinforces an important point: AI excels at processing and organizing information, but human insight remains irreplaceable. The future belongs to researchers who can effectively combine AI efficiency with human creativity and empathy.

Overcoming common challenges

"AI lacks the human touch"

This is true—and that's exactly why you need both. AI handles data processing so you can focus on building empathy with users and generating creative solutions. Research teams report improved efficiency and faster research cycles while maintaining cautious approaches to full automation, relying on human-in-the-loop safeguards to ensure AI-generated insights stay accurate and meaningful.

"We don't have the budget for expensive AI tools"

Many AI research capabilities are now available at low or no cost. Miro's AI features are included in plans starting with free accounts, making it accessible for teams of any size.

"Our team lacks technical expertise"

Modern AI research tools are designed for researchers, not data scientists. Visual interfaces and natural language processing mean you can start benefiting immediately without technical training.

"We're worried about data privacy"

Choose tools that prioritize security and compliance. Miro, for instance, offers enterprise-grade security features and gives you control over how your data is processed.

Getting started: Your 30-day transformation plan

Week 1: Assessment and planning

• Audit your current research workflow

• Identify the biggest time sinks and pain points

• Set up your Miro workspace with AI features enabled

• Choose 1-2 specific use cases to pilot

Week 2: First implementation

• Use Create with AI for your next research planning session

• Try AI-powered analysis on a recent batch of user interviews

• Experiment with automated report generation

Week 3: Workflow integration

• Incorporate AI tools into your standard research process

• Train team members on new capabilities

• Start measuring time savings and efficiency gains

Week 4: Optimization and scaling

• Refine your AI-assisted workflows based on experience

• Expand to additional use cases

• Share results with stakeholders to demonstrate value

The competitive advantage of AI-powered research

Organizations that embrace AI for user research aren't just working faster—they're making better decisions. Companies that prioritize user research consistently outperform those that don't. Product teams are moving beyond the outdated tradeoff between speed and insights, recognizing that the best products aren't just the first to market—they're the first to meet user needs.

The data supports this shift: 90% of company executives stated in Forrester's State of Generative AI research that they have specific intentions to deploy AI to address both internal and user-facing problems.

Looking ahead: The future of research is collaborative and AI-enhanced

We're entering an era where the best research teams combine human insight with AI capability. AI-powered tools are taking over the tedious, time-consuming parts of research, freeing up UX professionals to focus on strategy and creativity. This isn't about replacing researchers—it's about amplifying their impact.

The organizations that will thrive are those that understand AI as an enabler of better human work, not a replacement for human thinking. They'll use AI to eliminate drudgery while doubling down on the uniquely human skills of empathy, creativity, and strategic thinking.

Start your AI research journey today

The transformation doesn't require a massive overhaul of your existing process. Start small, experiment freely, and let the results guide your expansion. Miro's AI-powered features provide an ideal starting point—they're designed specifically for collaborative research and integrate seamlessly with the visual methods most research teams already use.

Ready to transform your research process? Try Miro's AI features for free and experience firsthand how AI can amplify your research impact. Your future self—and your stakeholders—will thank you for making the leap from manual research drudgery to AI-enhanced insight generation.

The future of user research is here, and it's more human than ever—because when AI handles the busywork, researchers can focus on what they do best: understanding people and driving innovation.