AI Governance dashboard: Introducing catalog metrics for AI visibility

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As AI adoption expands across business units and technology platforms, gaining a clear view of AI activity becomes increasingly challenging. Models, agents and use cases are often developed in separate tools, making it difficult for leaders to understand where AI is deployed and where risks may emerge. Gartner predicts that by 2027, 60% of organizations will fail to realize the expected value of their AI use cases due to fragmented governance frameworks. Centralized metrics are becoming essential to monitor AI initiatives and maintain oversight across the enterprise.

What’s new: AI governance Dashboard (catalog metrics)

Collibra introduces the AI governance dashboard (catalog metrics), providing real-time operational visibility into AI initiatives registered across the organization. The dashboard aggregates metrics across AI use cases, agents and models, enabling teams to monitor their AI portfolio through a centralized operational view. Through visual metrics and portfolio indicators, the dashboard helps organizations understand how AI initiatives are progressing across lifecycle stages, assess governance coverage and monitor potential risk signals. Instead of relying on fragmented reports or manual tracking, teams can now view the status of AI initiatives directly through consolidated metrics. Dashboard (catalog metrics) enables leaders and governance teams to quickly understand the maturity of their AI portfolio by combining lifecycle metrics, trust indicators and risk distribution insights.

How the AI governance dashboard (catalog metrics) helps

As AI adoption accelerates, organizations lose visibility into AI initiatives, their lifecycle, and associated risks across teams and platforms. Without operational metrics, they rely on fragmented tools or custom-built integrations, limiting proactive governance.

The AI governance dashboard provides a centralized, real-time view of AI activity. It enables management by exception, highlighting what is stuck, at risk, or non-compliant so leaders can focus on critical issues. With drill-down capabilities, users can quickly investigate and act, supporting faster, more targeted decisions at scale. AI governance dashboard solves for:

• Lack of visibility into the total number of AI use cases, agents and models

• Difficulty tracking AI initiatives across lifecycle stages

• Fragmented reporting across teams and development environments

• Limited ability to understand AI risk exposure across initiatives

• Insufficient operational metrics for leadership oversight

How the AI governance dashboard (catalog metrics) works

Dashboard (catalog metrics) aggregates metadata from AI assets registered in the platform, including AI use cases, models and agents. These assets are connected to governance attributes such as lifecycle stage, ownership and risk rating. The dashboard then surfaces this information through visual metrics that provide a consolidated view of the AI portfolio. The dashboard displays indicators such as the average AI trust score, lifecycle distribution of AI agents, lifecycle progression of AI use cases, model deployment distribution and risk ratings across AI initiatives. By consolidating these signals into a unified dashboard, organizations gain operational visibility into AI initiatives and can monitor AI activity and risk more effectively.

Break down your AI portfolio by lifecycle stage, model composition and risk rating to uncover adoption patterns, maturity gaps and areas requiring attention.

The AI Governance dashboard surfaces catalog metrics and overall trust scores in one place—giving teams a real-time view of their AI portfolio, lifecycle progress, and areas requiring attention.

Why you should be excited for the AI Governance dashboard

The AI Governance dashboard (catalog metrics) provides distinct value to key stakeholders across the organization, such as:

AI Governance Leaders: Gain centralized oversight of AI initiatives and governance signals
Chief Data & AI Officers: Monitor AI portfolio growth and lifecycle maturity through real-time metrics
Data Stewards: Understand how AI systems evolve and ensure governance policies remain aligned
Model Risk Managers: Identify AI use cases and AI assets with elevated risk ratings and prioritize governance actions

The AI Governance dashboard helps these users with:

AI portfolio monitoring: Track the distribution of AI use cases, agents and models across lifecycle stages
Risk monitoring: Identify AI initiatives with higher risk ratings to prioritize governance review
Model oversight: Monitor the distribution of foundational and deployed models to understand deployment patterns

Key takeaways about the AI Governance dashboard

The AI governance dashboard (catalog metrics) provides organizations with a centralized operational view of their AI initiatives. By aggregating lifecycle metrics, trust indicators and risk signals across AI use cases, models and agents, the dashboard enables teams to better understand the maturity and governance posture of their AI portfolio. This visibility supports more proactive oversight and helps organizations scale AI initiatives responsibly.

Join Collibra’s Spring Product Premiere to learn how AI governance dashboard helps:

• Provide visibility across AI initiatives with centralized metrics

• Support governance oversight with lifecycle and risk indicators

• Enable leaders to monitor AI adoption through operational dashboards

Where to learn more about the AI governance dashboard

To learn more about AI governance dashboard (catalog metrics) and the broader Collibra AI Governance capabilities, explore the following resources: