Operational architecture for project lifecycle management
The operational model was anchored in lifecycle stages — intake, planning, execution, closeout, and knowledge sharing — each underpinned by tools and automation to reduce manual overhead and improve transparency.
A dynamic intake form was connected to a Jira kanban board, automatically categorizing incoming requests and populating them with key project metadata. Requesters received automated status updates and visibility into next steps, which created a predictable intake process and supported structured prioritization conversations across teams.
Custom dashboards powered by Jira automation displayed live project status, researcher bandwidth, work by product area, and volume over time. These insights enabled more informed resourcing decisions, identified bottlenecks early, and supported advocacy for team capacity.
Templates and workflows were created to support projects of varying complexity — from short usability testing to longer-form generative research. Each included guidance around timelines, stakeholders, and decision points, helping align scope with execution expectations and reducing inconsistencies.
A standardized closeout process ensured metadata and research outputs were pushed into a centralized archive. This simplified future knowledge retrieval, reduced duplicative research, and supported long-term insight reuse. Researchers were prompted to document essential information before shifting focus to new work.
The lifecycle included touchpoints with Legal, Security, Procurement, and Compliance, ensuring research adhered to privacy and ethical standards. These embedded reviews minimized risk and streamlined approval processes for new tools, vendors, and participant management workflows.