Problem Statement
The existing workflow builder was functional, but poor data visibility made it difficult for users to debug and test their automations. That friction was a barrier to adoption, increased support volume, and left the product unprepared for Budibase’s planned shift to AI-first automations—which would depend on users clearly seeing data flowing through each step.
Design Process
User-Centric Research
I used Maze to design and run a paid research study with 25 active users. By analysing their feedback and quantitative data, a clear pattern emerged: the main problems were poor data visibility and unclear error messages. Users consistently highlighted a need for a better debugging experience, more intuitive testing, and a cleaner overall interface—requirements that shaped a scalable, highly visible data framework rather than one-off UI fixes.
Driving Executive Alignment
Armed with compelling data from the study, I presented my findings and a clear design vision to the company’s leadership team. My proposed data framework and user-backed evidence secured their buy-in and helped shape the roadmap for both the immediate redesign and Budibase’s longer-term pivot toward AI-first automations.
Solution: A scalable, highly visible data framework
| Improvement | Change | Benefit |
|---|---|---|
| Data In / Data Out | Inline tabs were added to show the data flowing into and out of each step of a workflow. | Provided complete transparency for easy data tracing. |
| Inline Error Logging | A dedicated tab now displays clear, understandable error messages for any failed step. | Eliminated guesswork and sped up troubleshooting. |
| New Sidebar Panel | All workflow controls were moved out of the main canvas and into a dedicated, contextual sidebar. | Created a decluttered UI and a cleaner editing experience. |
| Enhanced Canvas & UI | Zoom and pan controls were added, and the overall layout was made more compact and organised. | Improved management of large, complex workflows. |
Impact & Results
| Area | Outcome |
|---|---|
| User Adoption | The redesign led to a significant drop in user complaints, indicating a sharp rise in satisfaction and adoption of the feature. |
| Strategic Alignment | The data-driven roadmap secured executive buy-in and aligned the product with Budibase’s pivot toward an AI-first automations platform. |
| Support Overhead | Clearer error logging and an intuitive UI led to a marked decrease in related support queries from our user base. |
| AI-first groundwork | The scalable, highly visible data framework laid the structural foundation needed for AI-powered automations to sit on top of workflows users could already understand and trust. |
Conclusion
By translating insights from 25 users into an end-to-end UX redesign, I established a scalable, highly visible data framework that solved immediate debugging and adoption problems while laying essential structural groundwork. That foundation positioned Budibase for its strategic pivot into an AI-first automations platform—so the next phase of growth could build on workflows users already understood, not start from scratch.