Background
In collaboration with IBM’s Financial Services Innovation team, I led the design of an experience narrative showcasing how watsonx.ai and Automation Decision Services enable more inclusive, real-time lending. The challenge was not only to explain how the technology worked but to make people feel its human benefit.
The goal was to transform abstract, data-heavy AI systems into a story that humanizes automation and aligns executives, bankers, and customers with a transformative lending experience.
My Role
Design Lead
Responsibilities
Persona Development
Tech Mapping
Storytelling
UX Strategy
Deliverables
Storyboard
Wireframes
Demo
Tools
Mural
Figma
Premier
Timeline
1 weeks
Challenge
Banks and lenders face a growing need to evaluate applicants who lack traditional credit histories. AI-powered decisioning offers a solution, yet its complexity often alienates customers and executives alike. The challenge was to design a narrative that revealed the intelligence of automation through an emotional lens, helping audiences understand that smarter systems can also be more human.
Approach
I led the UX narrative and experience design, focusing on a story-driven approach anchored in a persona named Alex.
Crafted a day-in-the-life narrative illustrating how invisible AI processes can lead to visible moments of delight.
Simplified technical complexity through emotional storytelling and mapping back-end automation steps to the proposed front-end UI.
Collaborated with data scientists, marketing, and creative teams to align business goals (AI adoption) with user outcomes (trust, accessibility, empowerment).
Solution
The narrative follows Alex as she navigates the process of buying a new car. Despite her limited credit history, she secures a fair loan and insurance package in minutes. Through AI-driven evaluation of dynamic data points, the system provides a decision that is both fast and fair. Alex’s experience communicates that technology works best when it feels invisible and supports human progress without friction.
Key Success Metrics
The outcomes were tracked through a mix of survey data, content analysis, and internal feedback. Each metric was designed to capture both user understanding and team effectiveness.
40%
Improvement in Comprehension
Method: Post-demo surveys and comprehension testing.
After stakeholders viewed the narrative experience, they completed a short evaluation comparing their understanding of AI lending concepts before and after the demo.
85%
Emotional Resonance
Method: Audience feedback through qualitative tagging.
During internal reviews and client showcase sessions, participants described their emotional response using predefined descriptors such as trustworthy, inspiring, or technical.
60%
Reduction in Technical Jargon
Method: Content analysis and readability scoring.
Before and after versions of the narrative were compared using a readability index and content classification. Reduction in domain-heavy terms was calculated through AI-assisted linguistic audits.
Key Outcomes
The project humanized a complex AI system through narrative design and made the invisible mechanisms of automation feel intuitive and humane. It established a reusable storytelling framework that continues to inform how IBM communicates AI experiences across industries. The outcome demonstrated that thoughtful design storytelling can elevate both brand trust and business adoption.
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