Get closer to your customers with AI Studio

A pivotal moment arrived when our team found itself yet again rushing to deliver insights without adequate time for thoughtful analysis or meaningful product collaboration. 

"I was hoping to put together that interactive insights workshop," one researcher lamented, "but I'm already getting pulled back into the trenches to screen participants for our next project."

This comment struck a chord and became our collective enough-is-enough moment. Too much of our valuable time was consumed by operational tasks, leaving precious little leftover for high-impact work like synthesizing insights, shaping strategy, and influencing product direction. This imbalance wasn't merely frustrating; it was unsustainable.

So we channeled frustration into change. We began reimagining our research workflows with AI, aiming to liberate ourselves from repetitive tasks and refocus on what truly matters: uncovering and championing customer needs.

We're not alone in this challenge. At Asana and throughout the industry, UX researchers report spending up to 60% of their time on manual work: writing screeners, coordinating with stakeholders, and other administrative tasks. This inefficiency slows progress, sure, but it also fundamentally diminishes the strategic value research brings to product development and customer experience.

At Asana, we saw an opportunity to rethink how research teams work and to do so with AI and humans working together. If you're grappling with similar challenges, take a peek at how we’re using AI to redesign our research and customer insights processes.

“Finding the match between the biggest pains on your team and what AI can do is what will make you successful,” said Beth Toland, Head of Experience Research at Asana. “That is where the real opportunity lies.”  

Amplifying research impact with purpose-built AI

AI Studio is Asana's no-code workflow builder, and we’ve developed use cases specifically for UX researchers and customer insight teams. Unlike generic AI tools that offer broad functionality, our workflows tailor our use of AI Studio to the unique work of customer research, from planning research objectives to analyzing results and sharing insights across the organization.

What sets AI Studio apart is its seamless integration with existing research frameworks and methodologies. Rather than disrupting established processes, it enhances them, allowing researchers to focus on what they do best: understanding customer needs and translating those insights into actionable recommendations.

Our team has been heads-down using AI Studio to transform how we gather and leverage customer insights, and the results have been remarkable. We've identified three key use cases where AI Studio has delivered exceptional value, not just for our research team but for anyone who creates or applies customer insights, including R&D and Marketing teams.

From pain points to solutions: AI-powered research workflows

AI can accelerate many research tasks, so where to start? Our approach was to begin by mapping the team's key workflows and identifying where researchers were feeling the most pain. That was the foundation we used to then identify what problems AI was best suited to address. For us, that turned out to be in the planning and analysis phases. 

These three use cases demonstrate how AI Studio can transform your customer insight workflows:

Use Case #1: Discovery and project planning

What this is: AI Studio streamlines the research planning process by translating product briefs into research objectives and generating comprehensive project plans based on those objectives. The system suggests appropriate methodologies, sample sizes, screening criteria, and timelines based on your specific goals.

Workflow improvement: The platform analyzes your research questions and automatically creates a structured project plan with recommended research methods, participant profiles, and timelines. It identifies potential bottlenecks and suggests alternative approaches when appropriate.

Benefit: This reduces the time it takes to scope a project and build it in Asana, minimizing unnecessary back-and-forth and reducing demand on subject matter experts. According to our internal surveys, recruitment, site securing, and operations are the biggest sources of research project delays (36.3%), and AI Studio specifically addresses these pain points.

Use Case #2: Data collection and analysis

What this is: 

• AI Studio automatically analyzes customer interview recordings and transcripts, extracting key insights aligned with your predefined research objectives. The system uses advanced natural language processing to identify relevant information and quotes that address your specific learning goals. 

• AI Studio also creates a searchable knowledge base of all your customer research, allowing people to quickly find relevant insights from past studies. What makes this especially valuable is that AI can interpret nuanced questions and retrieve relevant information even when the questions don't exactly match the original text.

Before AI Studio: When doing initial analysis of interviews for real-time updates to stakeholders, researchers would spend nearly 30 minutes per interview reviewing recordings, making notes, and writing summaries to share with stakeholders. This process was both time-consuming and inconsistent across team members. 

After AI Studio: The same process takes just five minutes, with AI handling the initial analysis and researchers reviewing and refining the output. The system takes the interview transcript and both summarizes and extracts key information to answer the research goals. It then assigns a review task for the UX researcher to read and approve the summary. From there, AI Studio makes it easy to share the summaries directly with relevant stakeholders. AI can automatically decide which collaborators to cc on its summary or include a preset list each time.

“It’s critical to bring stakeholders along for the ride and help them feel close to the customers we’re learning from. AI Studio has made it much easier to share lightweight insights real-time, before we do deeper analysis and make strategic recommendations,” said Sofia Dewar, Lead AI UX Researcher at Asana

Benefit: This approach helps distill customer insights quickly, reduces manual work, improves quality and consistency of write-ups, and ensures the right people are in the loop so they can extract maximum insight from every customer interaction.

Use Case #3: Feedback loops for continuous improvement 

What this is: We also developed a Smart workflow in AI Studio so that after someone conducts an interview with a customer, that person receives interview-facilitation feedback based on the transcript from their conversation and our team’s documentation on facilitation best practices.

Workflow improvement: Historically, we’ve not had a scalable, reliable way to provide researchers or cross-functional stakeholders with feedback after customer conversations. Using AI in this way was a creative approach to make all of our team’s knowledge and documentation around best-in-class interviewing techniques go further. Before, a motivated person would need to re-read the best practices document and reflect on whether they had used the suggested techniques effectively. Now, AI provides an instant summary, celebrating the places where people have applied best practices and encouraging them to continue to grow where needed. 

Benefit: This Smart workflow has enabled continuous improvement and upskilling across the research and R&D teams when it comes to interviewing customers. It has also enabled the UX Research team’s expertise to be applied in novel, high-impact ways. 

Best practices for implementation

Now that we've looked at a few use cases that we're finding useful here at Asana, we wanted to share some thoughts on best practices for AI implementation in your own research workflows.

Maintaining human oversight

Asana's human-in-the-loop approach serves as our guiding principle. While AI can analyze data quickly and at scale, human researchers bring contextual understanding and judgment that AI cannot replicate. We recommend establishing clear review processes for AI outputs, with experienced researchers validating insights before they influence business decisions.

Designing AI prompts for maximum effectiveness

The quality of AI outputs depends significantly on how you structure your prompts. We've found that the most effective prompts:

• Clearly define the scope and objectives of the research

• Provide context about the customer segments or product areas

• Specify the format and level of detail required for the outputs

• Include specific examples of desired outputs

Common pitfalls to avoid include overly broad prompts and failing to provide sufficient context for the AI to understand the business and product relevance of customer information.

Ethical considerations

As researchers, we also consider the ethical implications of AI-assisted insights. We use Asana's AI Principles to guide us in being transparent with customers about how their feedback will be analyzed, being mindful of the type of information we are giving to AI, and reviewing our AI workflows for potential bias.

Future possibilities

Looking ahead, we see several exciting possibilities for AI in customer research:

Prioritized product improvement recommendations: Systems that analyze customer feedback across channels and automatically generate prioritized enhancement suggestions.

Enhanced user journey mapping: AI-powered visualization tools that dynamically update journey maps based on new customer data from a mix of sources. 

Real-time interview facilitation assistance: Systems that suggest follow-up questions during the flow of the interview, help with time management, and even provide context from past conversations so the customer never has to repeat themselves.

Implementation challenges to watch for include integration with existing research tools, team adoption and skill development, and balancing AI efficiency with the nuanced understanding that comes from direct customer interaction.

Key takeaways

The potential of AI in UX research is clear. By automating routine tasks and making it easier to connect and tap into insights at scale, AI Studio helps researchers focus on what matters most: developing deep customer understanding and translating that understanding into exceptional products.

The key to success lies in designing workflows that take advantage of what AI is good at and what humans are uniquely well suited to own. When implemented thoughtfully, AI becomes a powerful force multiplier for research teams.

We encourage all teams who generate or apply customer insights to explore AI-powered workflows, starting with the use cases outlined above. The future of customer research is here, and it's a future where AI and human expertise work in harmony to deliver ever more valuable customer experiences.