AI CoE Dashboard

Track deployed opportunities

The AI CoE Dashboard offers a centralized view of all deployed AI use cases within your organization. It enables teams to monitor key operational metrics and financial data, such as ROI, agent performance, time duration, capability, and cost utilization of AI deployments, allowing for effective oversight of enterprise AI initiatives.

Accessible via the meter icon in the left navigation panel, this dashboard is available to both executives and invited operators. Only use cases marked as ‘Deployed’ are displayed. Any updates made to these use cases, such as ROI, agent performance, or cost metrics, are automatically reflected in the dashboard.

Dashboard overview

At the top of the AI CoE Dashboard, the following summary metrics are displayed:

• Summary and business impact:

  • # of active AI solutions / AI opportunities: Displays the number of active deployed solutions versus the total number of AI opportunities submitted across the organization.

  • Time to deploy (avg): Shows the average time taken (in hours) to deploy the listed use cases.

  • Return on Investment (ROI): A month-wise line graph of ROI values showing trends and percentage growth (YoY).

• Infrastructure cost: Shows the monthly operational costs segmented by:

  • Cloud spend: Monthly expenditure on cloud resources.

  • LLM cost: The monthly cost associated with large language model usage.

  • Personal / team: Monthly personnel-related costs.

  • Total cost (Pie chart): Displays the proportional distribution across cloud, LLM, and personal/team.

  • Budget used (year to date): Displays the percentage of the annual allocated budget that has been used.

• Capability: Displays currently available technical personnel.

  • Data scientists: Number of data scientists involved in deployed use cases.

  • ML engineers: Number of machine learning engineers supporting deployments.

• Agent performance: Tracks task activity for all deployed agents.

  • Number of agents: Total agents involved in deployed use cases.

  • Tasks pending: Count of incomplete tasks assigned to agents.

  • Task completed: Number of tasks successfully completed.

Deployed use cases overview

All the deployed use cases, along with their key performance and cost indicators, are reflected in the 'AI Opportunities' table. Each row includes:

Available actions for each deployed use case

Each row in the 'AI Opportunities' table includes an options menu (three horizontal dots). Follow these steps to update or manage individual records:

1. Update data

• Click the three dots beside the use case you want to update.

• Select ‘Update Data. ’

• In the ‘Update Data’ pop-up, fill in:

  • Status: Displays the current stage of the use case. This field is automatically populated and displays 'Deployed' for all entries listed in the CoE Dashboard.

  • ROI and business impact: Enter estimated values to quantify the expected return on investment (ROI) and the projected business value of the use case in the provided fields.

    • Time to deploy: Record the actual time taken to deploy the use case from initiation to production.

    • Current impact: Enter the current measurable outcomes of the use case.

    • Target impact: Specify the intended impact metrics that the use case aims to achieve at full scale.

  • Budget: Overview of financial resources allocated and used.

    • Allocated budget ($): Total budget assigned for this use case.

    • Budget used ($): Amount spent to date from the allocated budget.

    • Cloud cost ($): Expenses incurred for cloud infrastructure/services.

    • LLM cost ($): The cost associated with using large language models.

    • Team cost ($): Total cost of personnel involved in development and deployment.

  • Team: Tracks the number of people assigned to the use case.

    • Data scientists (count): Number of data scientists contributing to the use case.

    • ML engineers (count): Number of machine learning engineers involved.

  • Agent performance: Track ongoing effectiveness of deployed agents.

    • Number of agents: Total agents working on the use case.

    • Task pending: Number of assigned tasks yet to be completed.

    • Task completed: Number of tasks successfully executed by the agents.

    • Accuracy: Specify the effectiveness or success rate of the agents.

• After making the necessary updates, save the changes. Once saved, all updates will immediately reflect in the CoE dashboard. This ensures that performance metrics, financial summaries, and ROI charts remain current and actionable.

2. Update ROI

To update the ROI details of a specific use case or opportunity, navigate to the ‘AI Opportunities’ table. Find the row corresponding to the use case you want to update. At the end of that row, click on the three horizontal dots and select ‘Update ROI’. This opens a pop-up window titled ‘Update ROI.’

In this interface:

• Select year: Use the dropdown to choose the calendar year for which you need to add ROI data.

• Add ROI for months: Enter ROI values for each month of the selected year. All months from January to December are listed.

After entering the monthly ROI data, click ‘Save’ to record your changes. Use ‘Cancel’ to discard edits.

3. Delete

• Click the three dots.

• Select ‘Delete’ and a confirmation pop-up will appear.

• In the pop-up, type DELETE and press ‘Confirm’ to remove the use case from the deployed list permanently.

• You can also press ‘Cancel’ to close the confirmation pop-up without making any changes.

Notes

• Only use cases in the Deployed status appear in this dashboard.

• Executives can view all deployed use cases submitted under their account, including those submitted by invited users.

• Any updates made via the ‘Update Data’ or ‘Update ROI’ interfaces will automatically update the corresponding dashboard metrics and visuals.