AI Impact - Separate SDLC and AI usage metrics into categorised panels

Problem

The current iteration of the "comparison table" visualization AI Impact Dashboard shows both SDLC and Code suggestion usage metrics in a single table.

This can be confusing as it may look like either all metrics are SDLC metrics or all AI related metrics.

AI Impact Dashboard

This update will also improve the Value Streams Dashboard by breaking the table down into smaller groupings.

Note: the goal of the "comparison table" visualization AI Impact Dashboard is to help decision-makers understand how different metrics are performing relative to each other or to identify trends and patterns in the data.

Proposals

• Option A - Separate metrics by using different panels
• Option B - Separate metrics by adding a collapsable sub-header within the table
• Option C - Add a way to filter out which metrics to show/hide
• Option D - Leverage row headers and sub-headers

❖ Figma project →

Preferred proposal

• Option A - Separate metrics by using different panels

Rationale:

• This option solves the problem of chunking the information
• This option will be the easiest to implement and will reduce the need of adding complexity to existing UI patterns (See comment: #461748 (comment 2037477370))
• This option is easily reversible (See two way door decision)
• Since the Created by GitLab dashboards serve as a showcase of what is possible to create, we should set an example of what good defaults and best practices look like. In this case making sure each panels answers a specific set of questions at a glance. See Option A - custom dashboard example.

Next steps:

• Update the comparison table the AI Impact dashboard (Proposal) (This issue)
• Update the comparison table in VSD (Proposal) (Separate issue)