The Problem
Context.
Lack of visibility created mistrust
Enterprise users needed a way to use AI confidently within existing workflows. Initial testing showed users were unsure how AI results were generated and how reliable they were.
Manual review slowed productivity
Enterprise users needed a way to use AI confidently within existing workflows. Initial testing showed users were unsure how AI results were generated and how reliable they were.
Teams needed accountability & auditability
Enterprise users needed a way to use AI confidently within existing workflows. Initial testing showed users were unsure how AI results were generated and how reliable they were.
Goals & Success Measures
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- Reduce time to complete a task by 20%
- Increase user confidence with AI outcomes
- Ensure workflows met compliance needs
My approach
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User Research
I conducted interviews with 8 enterprise users and mapped common workflows to identify friction points and trust gaps.
Systems Thinking
I analyzed dependencies across policy, data, and UI touchpoints to ensure consistency across the ecosystem.
Iterative Prototyping
I tested 3 design iterations to validate clarity, speed, and usability.
The solution
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The final design made AI actions transparent and traceable, with clear feedback and contextual guidance.
- Clear status & feedback messages
- Visible audit trails
- Inline guidance to set expectations
Impact & outcomes
What feedback said
While not all metrics were quantifiable at this stage, qualitative feedback clearly showed improved clarity and efficiency.
- 🕒 Estimated 25% faster task completion
- 👍 6/8 users reported improved trust
- 🔍 Compliance reviewers felt more confident signing off
- 🕒 Estimated 25% faster task completion
- 👍 6/8 users reported improved trust
- 🔍 Compliance reviewers felt more confident signing off
- 🕒 Estimated 25% faster task completion
- 👍 6/8 users reported improved trust
- 🔍 Compliance reviewers felt more confident signing off
- 🕒 Estimated 25% faster task completion
- 👍 6/8 users reported improved trust
- 🔍 Compliance reviewers felt more confident signing off
Reflection
What I learnt and key takeaways
Designing for AI requires trust, clarity, and accountability. Users value transparency even more than speed — and involving them early reduces risk later.