All AI use cases at a glance: Why a centralized AI inventory is essential

October 7, 2025

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Article by Marcus Belke, AI regulation

Marcus Belke

CEO of 2B Advice GmbH, driving innovation in privacy compliance and risk management and leading the development of Ailance, the next-generation compliance platform.

We see it every day: companies use AI selectively, but nobody has the big picture in mind. Emails asking "Can we use tool X?" are no substitute for a control model. This is how data migrates to external systems, Compliance remains reactive and management makes decisions without an overview of the situation. The AI regulation (EU AI Act) adds to the pressure. However, only with an AI inventory, workflows and reliable documentation can a solid basis for decisions be created.

What is an AI inventory?

A AI inventory (AI Inventory) is a systematic directory of all applications, systems and projects within an organization in which Artificial intelligence (AI) is used or planned.

However, in order for AI to become controllable and auditable, many companies are currently lacking some basic building blocks. Although initial tools or individual guidelines often exist, a consistent framework is rarely established. Managers in particular report that Transparencyprocesses and reporting do not converge. This is precisely where the key gaps become apparent:

Overview of all AI activities
Companies need to know at all times: Who is using which AI, for what purpose, with which data and in which department? Only this basic view creates a common picture of the situation and prevents risks from remaining in the dark. Without such an overview, all risk work remains speculation. Management and Compliance can then only react instead of acting with foresight. A central AI inventory makes dependencies visible, uncovers duplications and shows where sensitive data is being processed. This creates a basis that makes targeted checks, approvals and measures possible in the first place.
Operational relief
Checklists alone are not enough. A company cannot legally review each use case individually. In global organizations in particular, dozens of projects quickly arise in parallel. Without a systematic solution, specialist departments and compliance teams become bogged down in manual coordination. This is why roles, escalation and timing must be defined in a central workflow so that decisions can be made in a comprehensible, comparable and scalable manner.
Management Reports
In addition to to-do lists, successful company management also requires comprehensive reports. These show the status, maturity levels, approvals and trends in the individual areas. Only with consolidated key figures can the Management Board and supervisory bodies make well-founded decisions. Such reports make it clear where bottlenecks exist, which departments need to catch up and which use cases are particularly strategically relevant. Without this reporting, governance remains a collection of individual measures instead of a reliable basis for management.

Clean separation of use case, tool and model

Before moving on to concrete implementation, it is important to clearly define the basic terms. Governance can only be developed sensibly and misunderstandings avoided if everyone involved has a common understanding. Governance often fails because of the terms used.

A Use Case describes the specific purpose of an AI application and the effect it is intended to have, for example automated text analysis in the Marketing or a forecast in the financial sector.

The Tool is the visible interface or platform via which the use case is implemented, for example an app or a dashboard.

The real centerpiece, however, is the AI model. It contains the trained intelligence, entails strengths, limits and risks and ultimately determines the quality of the results.

A single use case may well combine several models. Only when all three levels (use case, tool and model) are clearly documented and evaluated separately from each other can comprehensible and reliable decisions be made.

From AI inventory to approval

The path to responsible AI is not a loose combination of individual measures, but follows a clearly structured path. This describes the steps that every company should systematically go through in order to Transparencytraceability and regulatory certainty:

Use Case capture. The purpose, data types, internal or external impact and user qualification must be recorded. Risk indicators are already created here.
Link tool and model. Model cards provide information on suitability, limits and versions. If one is missing Model mapthe mandatory data is entered manually.
Risk scoring and recommendation. The system aggregates the risk factors of the use cases, the model risk and the cloud classification into a recommendation for the decision.
Release with conditions. The decision can be approved, rejected or approved subject to conditions. Each decision is documented with a time stamp, name and conditions.
Re-audit in time. Changes to data, parameters or models trigger partial checks.

Metrics that management understands immediately

A meaningful presentation is required in order to gain real control capability from the collected data. In Ailance AI Governance, for example, the following metrics are an integral part of the dashboards and provide managers with crucial information at a glance:

Deployment Maturity by Department. Where is which department, pilot to production.
Use Cases approved by Compliance. Approval status with conditions.
Inventory Risk. Concentration of risks, hotspots.
Status AI Use Cases. Pipeline from proposal to retire.
Use Case Risk by Department. Distribution so that resources are used where it counts.
Impact and frequency of use. External impact and frequency of use as an early warning signal.

The right visualization on the dashboard makes AI governance not only understandable, but also controllable in everyday life.

Tip: Manage all AI projects centrally, audit-proof and legally compliant with Ailance AI Governance

Implementing the AI inventory in practice

A governance process is only effective if it not only exists on paper, but is also implemented in the form of clear, automated workflows. A solid AI inventory is characterized by the fact that mandatory fields are consistently queried, escalation paths are clearly defined and roles are assigned with clear responsibilities. This ensures that no decision is made incompletely or without the correct technical review.

In Ailance AI Governance, for example, an automatic Data protection impact assessment (DSFA) is triggered as soon as personal data come into play. Equally important is the question of reliable evidence. This includes detailed audit trails that document every step, clear dashboards with the most important key figures and automatically generated reports or presentations that can be presented directly to the Management Board or the Supervisory Board. Audit can be used.

This creates governance that takes the pressure off day-to-day operations, fulfills regulatory requirements and at the same time strengthens the trust of management and auditors.

Request a demo: Experience a complete AI inventory with approval path, DSFA triggers and management reports in Ailance. Secure your personal appointment now.

Marcus Belke is CEO of 2B Advice and a lawyer and IT expert for Data protection and digital Compliance. He writes regularly about AI governance, GDPR compliance and risk management. You can find out more about him on his Author profile page.