Case Study
Bridging the gap between UX and AI at enaible.
Abstract
Enaible is an artificial intelligence platform that leverages pre-existing systems data (O365, Salesforce, etc.) to measure and improve productivity in the workplace. I led the end-to-end research, design, and frontend development of a completely new product experience — transforming an opaque, mistrusted AI system into an intuitive productivity tool that users actually wanted to engage with. The redesign resulted in a 64% improvement1 in overall customer product satisfaction and helped solidify over $1.5 million in proposed contract renewals.
Overview
Background
In 2020, Enaible completed research and development on its core AI engine — a powerful system capable of analyzing workplace behavior data across platforms like Microsoft 365 and Salesforce to generate productivity insights. The company needed to create a new product experience to deliver these insights to end users. While a prior product existed, it had diverged significantly from the latest AI engine and no longer supported the company’s evolving business goals.
In early 2021, Enaible brought me on to own this transformation from the ground up. My role spanned the full product lifecycle: leading user research, defining UX strategy, creating all interaction and visual designs, and directing the entire frontend development process.
Interesting Fact
Machine learning algorithms are often considered “black-box” systems — the input and output values are known, but the steps in between are opaque. This opacity has fueled negative sentiment toward certain forms of AI and given rise to entire fields of research like Explainable AI (XAI), which directly informed my approach to this redesign.
Learn more hereProblem
I discovered early on that the outputs from the core AI engine were fundamentally difficult for users to understand. The insights it generated — while technically sophisticated — felt abstract and disconnected from users’ actual work lives. But the comprehension gap was only part of the challenge.
Early client feedback revealed something more concerning: a deep and pervasive negative sentiment toward AI-based management. Users didn’t just find the platform confusing — they found it threatening. A significant number of participants in our initial research viewed the platform as a form of professional “big brother,” fearing their data could be used against them at their employer’s discretion.
It became clear that the challenge wasn’t simply making a better dashboard. We needed to to rethink our entire experience where powerful AI felt approachable, transparent, and genuinely in the user’s corner.
Core question
How do you make an opaque, “black-box” AI system feel trustworthy, transparent, and genuinely useful to the very employees it’s measuring?
Objective
After extensive stakeholder conversations and early discovery work, I helped define the following objective: research, strategize, and design a progressive web app (PWA) experience that helps employees improve workplace productivity through understandable AI insights while increasing positive sentiment towards AI-based management.
This objective carried a dual mandate. On the product side, I needed to deliver a tool that translated 1,200+ possible AI insight combinations into something actionable and intuitive. On the human side, I needed to fundamentally shift how users felt about being measured by an algorithm - moving from fear and suspicion to trust and empowerment.
Success would be measured by improvements in overall product satisfaction scores gathered through a second round of in-depth qualitative research with the same user groups we studied at the outset.
Research
The Kickoff
I began by designing and conducting a qualitative research study with over 25 participants across various client organizations. This was intentionally multi-method: I combined focus group discussions, one-on-one interviews, live observation sessions (watching users interact with the existing interface in real time), and a custom-built guided survey experience I designed specifically to surface emotional responses to AI-based management.
My goal was to go beyond surface-level usability issues and understand the psychological barriers standing between our users and the product. The results confirmed our initial hypothesis - the AI lacked sufficient credibility - and revealed the full depth of user concerns.
Key Findings:
- 82% had difficulty connecting the platform’s productivity insights to their actual work habits - the AI felt abstract and disconnected from daily reality.
- 78% held negative sentiment toward AI-based management (AIBM), viewing it as opaque and potentially punitive.
- 73% said they would be more open to AIBM if their individual data remained anonymous - privacy wasn’t a nice-to-have, it was a dealbreaker.
- 62% wanted to understand which specific KPIs and work behaviors were driving their productivity scores.
The most striking insight was the “big brother” concern. Users genuinely feared their employer could weaponize this data. This reframed the entire project for me: the redesign couldn’t just be about better interfaces; it had to fundamentally change the relationship between the user and the AI.
Identifying Users
After finalizing the initial study, I moved on to updating our core user personas grounded in the behavioral and attitudinal patterns I’d observed. These personas captured not just job roles and workflows, but crucially, each user’s relationship with technology, their openness to AI, and their specific privacy concerns. They became essential tools for validating design decisions throughout the project.
Guiding Principals
I synthesized the research into four guiding principles that became the north star for every decision that followed:- Empower employees to help them better understand their habits hidden in their data
- Explainable AI insights as non-negotiable UX patterns
- Promote career growth & employee independence
- Prioritize user anonymity & data privacy
Our product mindset was shifting from monitoring user data to uncovering personal data insights as an AI coach.
Strategize
Sifting through the data
Now that we had data to work with, it was time to begin planning our redesign strategy. Since there were over 1,200 combinations of AI driven insights at our disposal, we began by utilizing an object-oriented user experience2 approach to prioritize and consolidate them. These insights were then matched with existing user pain points to help us create conceptual features and user journeys.
Time to prioritize
Once we had user journeys and clearly defined feature stories, we prioritized them using a traditional eisenhower matrix.
Phew! At this point we had a plan in place and were ready to move on to design.
Design
Before putting pen to pixel, I established design guidelines across three dimensions to ensure consistency and alignment with our principles throughout what would be a complex, multi-view application:
Visual Language
- Vibrant and engaging
- Minimal yet informative
Messaging
- Motivational yet honest
- Always focused on privacy
Design Architecture
- Scalable/modular design
- Prioritize accessibility
Starting with concepts
Now that we had our design guidelines, we worked on creating various wireframes to help us understand the information hierarchy and interactions of each feature. Many of the wireframes never made it to fully designed prototypes, but it helped save time in the long run.
Hundreds of views
We spent a few weeks creating hi-fidelity prototypes, but needed to be sure that our developers were aware of every edge case. This meant creating hi-fidelity prototypes for each possible path in the user's journey.
Life through animation
We added animations on the most critical features of the platform to give life to the experience. You can see the fruit of our labor below:
Mobile prototypes and alterations
We also created mobile views and various iterations of the desktop design.
Crafting our voice
One of my most impactful design decisions was informed by recent studies2 suggesting that first-person narrative could improve user sentiment toward AI systems. I experimented with multiple points of view and found that giving the AI a first-person voice (“Hi, I’m Enaible. Your new AI coach that’s here to help you get better, work smarter, and achieve more.”) dramatically shifted perception from “corporate surveillance tool” to “personal productivity partner.”
I wove this voice into a custom onboarding experience that introduced the AI’s capabilities, explained how it works, and — critically — made explicit commitments about data privacy and anonymity. This set the emotional tone for the entire product relationship.
The final product
Once we had our final prototype, we revisited our focus groups and conducted the second round of our in-depth qualitative study. The results were promising and indicated we were on the right track with a 64% improvement1 in overall product satisfaction! You can see an example of the finished prototype below:
You can view the finished prototype below.