AI Engineering
Enterprise AI Change Request Automation
This project focused on removing manual effort from enterprise Change Request preparation. Engineers previously had to inspect Jira and Bitbucket data, understand project context, and manually write Run Plans, Test Plans, and Validation Plans into ServiceNow. I built an automated workflow that brought those steps together into a single production-ready process, and the front end of the CR report experience was designed in Figma to make the output clearer and easier to review.
90%
Faster CR prep
<5 min
Avg turnaround
3
Plans generated
1
Production workflow
The Story
Why it mattered
Preparing Change Requests required engineers to manually inspect Jira tickets, review Bitbucket repositories, infer project context, and write structured plans before copying everything into ServiceNow. The process was repetitive, slow, and prone to inconsistency.
How I approached it
I built a backend workflow that pulled structured inputs from Jira and Bitbucket, combined them into a unified payload, and sent that context to Azure AI Foundry to generate Run Plans, Test Plans, and Validation Plans. I also shaped the front end of the CR report in Figma so the generated content could be reviewed clearly inside the enterprise reporting experience.
How the workflow runs
Capture the request context
Pull Jira issue context, repository changes, and implementation details into a single workflow.
Generate structured change plans
Use Azure AI Foundry to draft run plans, test plans, and validation plans automatically.
Publish to ServiceNow
Deliver the completed Change Request content back into the enterprise process with minimal manual intervention.