How AI supports qualitative and quantitative research processes
By Ashley Taylor Anderson, Director of Corporate Marketing at Tremendous●4 min. read●Sep 17, 2025
AI is the top theme in every research newsletter, blog, and conference right now. With 89% of research teams already using AI tools regularly or experimentally, and 83% planning to increase investment in 2025, researchers are starting to find value in the technology. The question is: Where is AI driving real impact in day-to-day processes?
Today, researchers are tapping generative AI and agentic tools to handle time-consuming, repetitive tasks like transcription, data cleaning, and insight mining. This frees up time to focus on strategic activities like study design, recruitment, and analyses that support key business decisions.
"In our industry, long days are the norm. AI helps shorten them by handling the mundane tasks and letting us focus on higher-value work," says John LaFrance, VP of Research Methods and Sampling at Escalent.
Where is AI most helpful to researchers in 2025?
Qualitative and quantitative research workflows each have unique needs. AI tools support both types of studies in different ways.
|
Research Type |
Common AI Applications |
Human Expertise Needed |
|
Qualitative studies |
Transcription, analysis, recruitment screening |
Moderation, interpretation, participant verification |
|
Quantitative studies |
Survey design, programming, data cleaning |
Strategic analysis, contextual insights, fraud detection |
Impact of AI and agents on qualitative research
According to a GBK report from 2025, 62% of US researchers use GenAI to synthesize lengthy interview transcripts, 58% use it to analyze data, and 54% to write reports. While AI streamlines back-office tasks, human involvement remains essential for recruitment, moderation, and analysis.
Here’s how teams are incorporating AI tools in their qualitative research processes.
Discussion guide design
Creating a discussion guide from scratch can trigger a serious case of writer’s block. AI helps generate initial question frameworks and suggests alternative word choices that keep interviews focused and flowing naturally.
"AI has […] definitely improved questionnaire design and coding. Tools that streamline open-end responses and surface key themes have come a long way, helping researchers tell better stories," says Danielle Chinitz, Senior VP of Client Experience at OpinionRoute.
Recruitment
Today, panelist selection still requires close human oversight. Qualitative studies typically offer larger incentives, which are an appealing target for bad actors looking to cash in. However, AI tools can assist with recruiting logistics like outreach, verification, and scheduling.
Abhinav Dua, Senior VP and Head of AI & Innovation at Escalent, says: "Recruitment and scheduling still happen manually, but with tools that access calendars, assess moderator fit, and factor in time zones, AI and automation can streamline the process. Screening can also be automated, including LinkedIn profile checks and parsing recruitment grids."
Interviews and focus groups
AI can help research teams scale live interviews and focus groups. AI tools for live transcription and note-taking let human moderators focus on leading effective conversations. And for some projects, AI moderation can help scale qualitative data collection in new ways.
"We've seen a great response to AI-guided interviews, especially with high-end professionals like oncologists,” says Christopher Barnes, President of Escalent. “They feel the AI actually listens, unlike static surveys. It takes some training to handle the specialized language, but once dialed in, the insights are far deeper.”
Transcription and note-taking
Generative AI tools save researchers time by automating transcription, translation, and meeting summarization. These tools give researchers a head start as they close out data collection and move on to analyzing free-form responses.
Analysis and reporting
AI can help identify key narratives and recurring themes in qualitative data sets — but ultimately, researchers need to synthesize insights themselves so they can make credible recommendations.
"AI can be a very useful tool in data analysis, helping us look at different angles. But you still need a human component, someone who can assess all the information and catch what AI might miss,” says Nate Lynch, Owner and Co-CEO of Full Circle Research Company. “Over time, AI will get smarter, but it doesn't replace human insight [or] real human interaction in surveys or interviews.”
Impact of AI and agents on quantitative research
On the quantitative side of the house, people are putting AI to use in survey design, programming, and data cleaning. Given the growing risk of participant fraud, recruitment and analysis are still largely handled by researchers directly.
Here’s how teams are integrating AI tools in their quantitative workflows.
Survey design
Generative AI supports questionnaire design by helping researchers brainstorm meaningful questions and optimize survey structure. Tools like ChatGPT, Claude, and Perplexity can suggest question variations, identify potential biases, and improve the flow of topics.
Recruitment and sampling
According to a Realeyes and Kantar study, 33% of surveys that pass current quality checks are bots or misreported demographics. That means human judgment remains essential when vetting panelists and weeding out bad actors. AI can play a supporting role by automating quota management and scaling targeted outreach.
Amanda Keller-Grill, Senior VP of Global Quality & Research Excellence at InnovateMR, shares: "Recruitment has always been vulnerable to fraud, whether it was click-farms years ago or AI bots today. They're drawn in by a $5 sign-up bonus from most panels, not realizing that you often need to hit a $20 threshold to actually cash out. We see this pattern repeatedly: fraudulent actors trying to exploit the front end of the panel process for quick gain."
Survey programming and data collection
AI can automate survey programming and testing, converting survey instruments into code for market research platforms like Qualtrics or Decipher. This can eliminate manual programming and validation checks that require dedicated technical resources.
Data cleaning
AI speeds up data cleaning by automating coding for open-ends and flagging low-quality data. Third-party tools like Clean ID, OpinionRoute, and Redem paired with custom-built classification models can zero in on fraudulent responses using natural language processing (NLP).
"We're using AI to help detect data quality issues,” says John. “In-house, we're building an open-ended response analysis tool based on a large language model to assess answer quality, not just to flag AI use, but to catch gibberish, nonsense, or weak responses.”
Even with AI tools bolstering fraud prevention workflows, human review is still needed to suss out what’s fake and what’s legitimate so the experience remains seamless for real participants.
Analysis and reporting
While AI can generate quick baseline reports, it lacks the depth and emotional context needed to create client-facing deliverables. Human interpretation remains key for distilling compelling insights to inform high-stakes business decisions.
“If you want deep insights, your team is going to be very involved through the whole process,” says Jennifer Hall, VP of Research Operations at KJT. “When you have a lot of stakeholders and decision-makers […] involved in the research, then you don't want […] to crank out something using an AI or partial AI product.”
Key takeaways
Researchers are adopting AI, but deploying it strategically. Most teams are using AI in some capacity today, focusing on automating high-effort, lower-stakes tasks like transcription, programming, and data cleaning.
Qualitative research benefits from selective AI integration: Transcription and analysis tools save time, but humans still play a leading role in the interview and analysis process.
Quantitative research faces an escalating fraud battle: AI streamlines survey design, programming, and data cleaning. Recruiting and fraud detection still require extensive human oversight.
Effective research requires AI-human collaboration: Teams that use AI strategically can deliver insights faster while empowering researchers to do what they do best: use context and critical thinking to interpret data and apply learnings to real-world scenarios.
