Using AI in research is new territory for many in the industry. How can AI become a valuable tool for qualitative researchers to work more effectively with their clients?
By strategically integrating AI into their workflows, they can move faster, work more efficiently, and deliver deeper insights while maintaining the human empathy crucial to qualitative research.
In this episode of “Insightful Inspiration,” Isabelle Landreville spoke with Sidi Lemine, CEO of Jade Kite, about using AI in agile insights.
Aligning with Client Needs through AI
When it comes to qualitative research, the client relationship is everything. Researchers must understand the priorities, communication styles, and even client organizations' internal politics to deliver accurate and valuable insights. Sidi says that a key part of his team's agile approach is using AI upfront to better align with client needs.
"Agility has been a little bit overused over the last couple of years - do more with less, be fast, pivot to break things," explains Sidi. "But to me, agility is about being really focused and determined to get to your goal."
For Sidi, a crucial part of that agility is using AI to better understand your client from the start of a project. Before doing any fieldwork, they use AI-enabled research based on existing data. "That helps us run multiple pilots in a very short span of time, so we can see, 'Okay, this is something we haven't thought about - should we investigate it? Or is this something that keeps coming up, and we're comfortable just validating it in person?'" explains Sidi.
AI as a Brainstorming Tool
This upfront use of AI allows Sidi’s team to sharpen their focus and get very specific on their research plan. But the benefits go beyond simply improving efficiency. Using AI in research can also help the team narrow their scope and brainstorm completely unexpected ideas they hadn't thought about.
With these AI-powered insights, teams can have more meaningful conversations with clients. AI tools are especially helpful in a dispersed working environment, enabling them to quickly distill the most important information for each stakeholder and manage everyone's time, even when the client team is spread across different locations.
The result is a research process that is tailored to the client's unique needs and priorities from the very beginning. The goal is to deliver insights that truly serve the client's strategy, not just complete a research project.
"I think it allows us to test questions so much faster, and be sharper,” said Isabelle. "We used to test questions with people from different languages, cultures, and generations like Baby Boomers, Gen Z, and Millennials. The same word can have different meanings across generations. This allowed us to test questions much faster and improve them."
Maintaining the Human Element
The key to making this AI-powered client collaboration work is ensuring that the human element remains central. "Empathy hasn't changed," affirms Sidi. "We're all still humans, with our aspirations, dreams, and needs. And we have to make the effort to really understand each other."
This is where Sidi's team excels. They don't view AI as a replacement for the qualitative researcher's expertise but rather as a tool to enhance and amplify it. "I have found that just throwing things into Midjourney or Dall-E is so much fun," Sidi shares, referring to popular AI image generation tools. "It adds incredible value and really helps jazz up our workshops and client presentations."
They use these tools to illustrate concepts, bring ideas to life, and align the client on what they’re trying to accomplish. Generating complex, beautiful images in the right style and colors is a huge help for workshops, especially if they’re investigating new industries.
You need reliable information to properly utilize AI in research and help you make better decisions. But you also need emotional resonance and connection with the people you're trying to work with.
AI Research in Action
Sidi points to one client engagement that was a great example of the harmony between AI and human insight. His team was working with the C-suite of a major global energy company and found that a traditional PowerPoint presentation just wasn't hitting the mark.
Instead, the researchers turned to short, high-quality video clips of consumers speaking in their own words. "We found that the impact of these small video reports on the company was considerable," says Sidi. "These deciders were used to looking at someone and listening very carefully to them talk about their own needs. Then they could really think about how to react to that."
These videos were interspersed with immersive, AI-powered exercises to engage the executives.
The End-to-End AI Advantage
So, what does using AI in research look like in action? The blending of AI and human empathy runs throughout the entire research process for Sidi’s team.
Before fieldwork
Before the research even begins, leverage AI to run rapid pilot studies, test concepts, and uncover unexpected areas to explore.
During fieldwork
During the fieldwork itself, AI plays a supportive role. Use collaborative AI tools to help distill what's important and manage everyone's time efficiently.
After fieldwork
In the analysis and reporting phase, AI-powered tools boost the team's ability to uncover better insights. They add data streams from sources like emotion recognition and behaviour analysis. This enables them to have more depth in the analysis beyond just the human researcher's interpretation.
Last Thoughts
The combination of AI and human insight has potential to give qualitative researchers a competitive advantage in the future. AI is another tool that can be added to the toolbelt of any quantitative researcher, enabling more cooperative client relationships and, ultimately, better projects.
