Intuit Design Research Internship
This past summer, I was Design Research Intern on the Virtual Expert Platform (VEP) team at Intuit focusing on AI-Guided Selling. As the primary researcher on the AI-Guided Selling project, I explored how AI could better support sales professionals in their daily workflow. The goal was to understand how to introduce new AI tools that feel trustworthy, helpful, and intuitive, especially in fast-paced sales environments where efficiency and credibility are critical.
Role/Team
Design Research Intern
VEP Team
Timeline
10 weeks
May 2025 - August 2025
Skills
User Interviews
Concept Evaluation
Contextual Inquiries
User Testing
Survey Research
Research Planning
Research Recruitment & Scheduling
Insight Synthesis
Cross-Functional Collaboration
Storytelling & Research Presentation
AI Integration Research
Tools
Figma
FigJam
Zoom
Google Slides
Google Docs
Salesforce (for prototype integration/testing context)
HeyMarvin
Achievements
Top Achievements
Research insights informed foundational design principles for AI products across the company
Reccomended by Sales leadership to collaborate with the AI Center of Excellence, helping define foundational design principles for AI products across the company.
Expanding research scope to include multiple archetypes
Included Customer Success Managers (CSMs) in research project, generating platform-level insights across diverse user archetypes.
Completed a full qualitative research study in less than 10 weeks
Completed a full qualitative study within 10 weeks using interviews, contextual inquiries, and concept evaluation, providing actionable insights that directly informed design and sales enablement decisions. Managed all research coordination, including participant recruitment, scheduling, and communication as well.
Problem Statement
How could AI be introduced not as a rigid tool, but as a helpful companion that supports sellers, builds trust, and fits seamlessly into their daily workflow?
Sales teams faced inefficiencies from scattered information, manual workflows, and limited real-time guidance, which slowed deal progression and reduced confidence. I worked on a team that was building AI tools to support Sellers in their workflows and help reduce these inefficiencies.
The question we faced was simple but critical: how could AI be introduced not as a bossy tool, but as a helpful companion that supports sellers, builds trust, and fits seamlessly into their daily workflow? The goal was to design an experience that enhances productivity, instills confidence, and encourages natural adoption without disrupting existing practices.
My Role
As a Design Research Intern, I:
Led research exploring user needs, motivations, and adoption barriers for AI guidance.
Partnered with design researchers, product managers, and sales enablement teams to translate findings into actionable recommendations.
Focused on early stages of AI adoption in pre-call and in-call workflows.
Guiding Research Goals
I conducted my research project with the 4 research goals in mind:
Understand how sales professionals perceive AI-generated guidance.
Explore factors that build (or destory) trust in AI and drive adoption.
Evaluate early prototypes for usability, workflow alignment, and value.
Inform product design, training, and rollout strategies.
Research Approach
I designed a multi-methods research approach to capture a complete picture of how sellers interact with AI guidance:
User Interviews
Conducted 60-minute sessions to understand workflows, pain points, and attitudes toward AI.
Concept Evaluations
Showed early mockups of the AI tools and watched how people reacted, asking what worked, what didn’t, and how it fit their needs.
Contextual Inquiries
Watched sales team members in action to see how they do their work in real situations.
Secondary Research
Reviewed prior internal studies and recorded sales calls to identify patterns and understand sales workflows.
Synthesis Workshops
Organized findings, identified themes, and generated design and sales enablement recommendations.
Design Implications & Impact
My research generated insights/findings that informed the following high-level outcomes:
Design Principles: Emphasize transparency, trust, relevance, and flexibility in AI guidance.
Enablement Strategies: Hands-on training, peer examples, and ongoing support to encourage natural adoption.
Rollout Recommendations: Prioritize user trust and clarity in internal communications around AI features.
These insights helped guide product decisions and internal strategies for introducing AI tools that feel helpful, reliable, and seamlessly integrated into daily workflows.
Reflections & Takeaways
What did I learn? Alot
Trust is everything
Users only adopt new technology when they trust it. When designing and building new products, focusing on building trust is essential.
Stay flexible
In the research world, anything can change at any moment. You might need to expand your research scope to include new personas, or you might need to pivot during your user interview because your participant mentioned an interesting point that wasn't in your interview script. It is important to stay flexible to uncover what truly matters.
Insights drive impact
Research insights are just words on a slidedeck if they do not improve business outcomes or enhance user's experience. Good research directly informs product changes, enhances the user experience, and drives measurable business outcomes, ensuring that the time and effort spent gathering data actually benefits both users and the organization.
Pujan Thaker © 2025
New Jersey, USA