Case Study: Improving Customer Experience and Internal Workflows Through AI
Background
Project
AI Integration
Tools: Figma, CoPilot, Azure DevOps, Jira, Confluence
Role
Product Designer at The Myers-Briggs Company
Year
2023-2025
As part of an internal AI advocate group at The Myers-Briggs Company, I partnered with engineering to explore how generative AI could enhance both internal workflows and customer-facing experiences. With sensitive IP and complex user needs, we approached integration carefully by balancing innovation with security, scalability, and user trust.
The Impact
- Facilitated early AI exploration through cross-functional collaboration, documentation, and presentations.
- Designed and prototyped an AI chatbot concept for both internal knowledge search and customer support.
- Preserved and repurposed research after budget cuts, enabling a faster pivot when the project resumed.
- Delivered accessible, on-brand UI and collaborated on prompt strategy with research and CX teams.
The Opportunity
How might we apply AI to improve our internal processes, and enable customers to better apply type knowledge?
When The Myers-Briggs Company launched an internal AI advocate group, I was an early and active contributor, driven by a strong interest in emerging technologies and their application in product design. I partnered closely with an engineering lead and Microsoft representatives to evaluate how generative AI, particularly Copilot, could improve both internal workflows and customer-facing experiences. My background in documentation, strategy, and UX positioned me as a bridge between disciplines—translating technical capabilities into user-centric use cases.
Research & Discovery
Understanding technocal Constraints and Capabilities
We collaborated in secure sandbox environments to protect sensitive IP, like MBTI content. I helped define exploratory use cases, attended integration calls, and synthesized findings into shareable notes and presentations to keep stakeholders aligned. I also supported testing strategy—evaluating model parameters like temperature to find the right balance between conversational tone and factual accuracy. To ensure AI-generated responses aligned with our standards, I worked with our Type Experts to validate outputs against certification guidelines.
Ideation & Concepts
creating Clarity
Two high-impact opportunities emerged:
B2C Career Chatbot: Helped users explore assessment results more deeply through contextual prompts and natural conversation. I designed mockups to illustrate how a chatbot could guide users through reflective career exploration.
Internal AI Knowledge Assistant: Solved knowledge management pain points by enabling semantic search across fragmented documentation.
creating Clarity
I presented my mockups of how to integrate the B2C concept into the UI, providing some options on how prominent we wanted the AI Chatbot to be, whether the user should go to a specific page for conversations or have a floating action button follow them around the site. Stakeholders were hesitant to have the floating button as they wanted to ensure the users read through and engaged with content on the site prior to the AI chat. I communicated that if we want users to be able to ask it really specific questions that it should be a floating button so that they could trigger it at any time and ask it about content, or careers, on the page. Making the user go to a dedicated page would force them to lean on their working memory too much to remember the exact data or content they wanted to ask questions about.
B2C Career Chatbot: Helped users explore assessment results more deeply through contextual prompts and natural conversation. I designed mockups to illustrate how a chatbot could guide users through reflective career exploration.
Gathering feedback
We were able to present the status of our research into AI during part of a companywide meeting. This allowed us to share our knowledge and findings, as well as share the initial concept for customer facing solutions. This also allowed an opportunity for coworkers, stakeholders, and board members to provide feedback or additional ideas.
A temporary setback
Unfortunately, the company experienced layoffs and the AI initiative was paused due to budget cuts. However, we had carefully documented our explorations, ensuring the work could be seamlessly revisited when the opportunity returned.
Iterating
new opportunities
Although budget cuts paused the initial initiative, our early research laid the groundwork for future exploration. A year later, as we expanded virtual certifications, we saw renewed interest from MBTI Practitioners seeking deeper insights into specific type applications. While our internal experts offered this guidance, the model wasn’t scalable. We identified an “AI Type Expert” as a promising use case, one that could deliver personalized, expert-level knowledge at scale while allowing us to test emerging technologies with real users.
In this next phase, the company opted to use a new AI provider to accelerate development. I collaborated with cross-functional teams to refine the user experience of a customer-facing AI chatbot, providing design feedback on layout and tone, improving accessibility and contrast, and ensuring visual consistency with our design system. I also worked with stakeholders and CX teams to define six pre-populated prompts that would guide user interactions.
The updated solution was tailored specifically for MBTI-certified practitioners, a persona we were focused on improving the experience for and creating deltight. We used the chatbot to surface a wide breadth of validated Type knowledge in a conversational format. This proved especially valuable for newly certified practitioners, who now had an intuitive way to access deep content and confidently apply Type concepts in their early client work. The feature was released to a pilot group of internal users and customer testers, with results pending.
Outcome & Impact
Enabled seamless cross-functional collaboration by documenting early research, coordinating with engineering and Microsoft partners, and synthesizing findings into accessible presentations for broader teams.
Delivered flexible design solutions by creating a prototype for a customer-facing chatbot and later refining a new implementation to match updated business needs and tooling.
Adapted to shifting priorities during budget cuts by preserving and organizing foundational research and mockups, enabling a smooth restart months later.
Created practitioner-centered value by launching an AI tool that helped newly certified MBTI practitioners easily access trusted type content, increasing their confidence and effectiveness in applying their training.