In many organisations today, artificial intelligence (AI) solutions are fundamentally different from traditional IT assets. Where a conventional IT asset might be a server, an application, or a licensed piece of software, an AI solution often comprises many interacting sub-systems, data pipelines, model training and deployment systems, APIs, user interfaces, monitoring, feedback loops, vendor services, and so on. Under new legislative directives, organisations need to maintain the use of AI, the type of use case, and the underlying LLM. More specifically, the potential risk the AI use case might bring to society. As in finance, when transactions are dynamic and in flux, real-time monitoring and control are vital. Because of this complexity and dynamism, organisations need a dedicated AI management system rather than treating AI as “just another static application”. Some of the core drivers for this need include:

• Visibility & risk management: Without knowing what AI systems exist, how they’re used, and who owns them, you are essentially operating blind. One article states that “without a clear AI inventory management, risks go unchecked.”[1]
• Regulatory and governance readiness: As regulations such as the EU AI Act emerge, organisations will be required to demonstrate governance, accountability and transparency of AI systems. An inventory is a foundational piece of that.[2]
• Operational control and lifecycle management: AI systems evolve — models get retrained, data sources change, business use-cases shift. A static asset list won’t suffice; you need a system to support lifecycle and change management. [3]
• Innovation and scalability: To scale AI safely, organisations must balance innovation with governance. A management system helps standardise how new AI systems are introduced, reviewed, and tracked.[4]

In short, treating AI like traditional IT is insufficient. You need dedicated infrastructure, processes and oversight. And one of the first pieces of that infrastructure is the AI inventory.

What is an AI Inventory?

Within the broader AI management system, one of the foundational components is the AI inventory (also called an AI register). Let’s clarify what it is, why it matters, and how it differs from a traditional IT asset inventory. An AI inventory is a repository or catalogue that captures and is maintained with detailed information about each AI tool, model, system or use-case within the organisation. It includes:

• System name, version, deployment status
• Purpose of the system
• Data inputs and outputs
• Ownership (business unit, accountable organisation)
• Vendor/third-party details if applicable
• Risk classification, frequency of use
• Technical metadata (model type, licence, APIs, etc)

For example, one guidance defines an AI inventory as “a detailed list of the AI systems developed and used within your organisation, including the specific use cases for those systems.” Creating an AI asset inventory is the essential first step in establishing AI governance. Regulatory frameworks such as the EU AI Act, ISO 42001, and the NIST AI Risk Management Framework (AI RMF) now make this a foundational requirement. It’s no longer just a best practice—it’s becoming a compliance expectation.

Why it matters

• Visibility: Without an inventory, you may be unaware of many AI systems in use (including “shadow AI” where business units adopt tools without formal oversight).
• Governance and risk control: With the inventory, you can assess which systems are high risk (sensitive data, automated decision-making, vendor black-box) and prioritise governance accordingly.
• Compliance readiness: Having accurate, up-to-date inventory data helps satisfy audit, regulatory, and internal oversight requirements.
• Operational accountability: It clarifies who owns what, what the system’s purpose is, how often it’s used, and what data it touches — making lifecycle management possible.
• Innovation enablement: Rather than stifling innovation, a good inventory structure helps guide it by making transparent how new use-cases enter, get assessed, and are tracked.

How it differs from traditional IT asset inventory

While IT asset inventories (hardware, software, servers, licenses) capture important details, an AI inventory must go further in several ways:

• AI systems often span multiple components (models, pipelines, data, user interfaces) and evolve over time; traditional assets are more static.
• AI requires metadata about data sources, model versions, training/serving environments, drift/monitoring — largely absent in classical IT asset tracking.
• AI use cases may be embedded in business processes with greater variety (e.g., HR screening, customer service bots, predictive analytics) and different risk profiles. Traditional assets often exhibit more homogeneous risk within a given asset class.
• There is a higher incidence of shadow adoption, rapid experimentation, and decentralised ownership in AI, which makes discovery and governance harder.

Therefore, the inventory must be designed with AI's unique nature in mind.

Inventory Objective & Procedure

An organisation can approach the objective and procedure of building and maintaining an AI inventory — aligning with existing IT asset inventory practices, but adapted for AI.

Objective

• To capture all AI systems/tools in use across the enterprise (including in-house, vendor, dev/test, pilot).
• To provide a single source of truth for AI assets: what exists, where, how used, by whom, with what data, under what risk.
• To enable governance: oversight, risk classification, lifecycle management (deployment, retirement, retraining).
• To support communication with stakeholders (business owners, compliance, audit, legal, and executives).
• To reassure and engage employees and business units (see next section) so that usage is transparent, safe, and supported.
• To integrate with existing asset-management and IT governance structures, rather than reinventing everything.

Procedure

The procedure to draft a high-level roadmap can be:

1. Discovery/audit – Start by identifying business units, teams, and functions where AI might be used (including pilot or informal tools).
2. Define metadata schema – Decide what fields your inventory will capture (for example: system name, version, license, cost, deployment type [web interface/installed/API], purpose, frequency of use, stakeholders, accountable owner organisation, vendor/third-party details) — as in your supplied template.
3. Leverage existing IT asset inventory process – If you already maintain an IT asset register, leverage that infrastructure (e.g., linking to ServiceNow, Jira or other CMDBs). The guidance recommends modelling the AI inventory after existing IT asset inventories and following similar processes.
4. Data-gathering – Use multiple methods to populate the initial inventory and then maintain it (see next section).
5. Risk classification/prioritisation – For each entry, assign a risk level (low/medium/high) based on criteria such as data sensitivity, automated decision impact, and regulatory exposure.
6. Governance integration – Link inventory entries to governance processes: who reviews, how often, what triggers updates, and what monitoring happens.
7. Maintenance / life-cycle management – Because AI is dynamic, ensure you have scheduled reviews (e.g., quarterly) or triggers (model update, deployment change, vendor change) to keep the inventory current.
8. Reporting & dashboards – Provide dashboards/summary views for executives, compliance, and risk teams so they can see at a glance: number of AI systems, risk distribution, ownership, and status.
9. Communication & change management – Make sure employees and business units know about the inventory, understand their role in reporting, and feel safe disclosing AI use.

Communication, Employee Reassurance & Culture

A strong AI management system isn’t purely technical — culture and communication are vital.

Communication importance

• Leadership must clearly communicate why the AI inventory exists: not just for compliance, but for business trust, operational control, and risk mitigation.
• Business units should be informed about how AI systems will be tracked, what data will be collected, and how ownership and responsibilities are defined.
• Transparency helps ensure that employees and units know: this is not about catching people out, it’s about enabling safe, aligned AI usage.

Employee reassurance

• It is essential to ensure that employees understand that no one will be reprimanded just for disclosing that they are using or experimenting with an AI tool. This encourages openness and reduces shadow-AI.
• Organisations should provide clear guidance on what constitutes acceptable AI usage, how to record or disclose it, and confidentiality/protection of employee disclosures.
• Emphasise leadership support for transparent disclosures: this reinforces the message that reporting AI use contributes to the enterprise’s overall risk management and governance efforts.
• When employees feel safe and supported, more accurate and complete inventory data will be reported — thereby reducing hidden risks and improving governance.

---

Inventory & Data-Gathering Methods

Populating and maintaining the AI inventory requires a mix of technical tools and human-centric processes. Below are recommended methods, along with notes on how they map to your listed fields and templates.

Systems and network discovery tools

• Use discovery tools to scan the network or asset inventory for known AI-related software, APIs, vendor modules, and machine-learning platforms.
• Data-flow diagrams (DFDs) and metadata mapping help identify how data moves in AI systems (which data sources, which models, which outputs).
• Use Active Directory or other user-access management systems to identify who has access to model training or inference systems, which helps trace ownership and risk.
• Collect metadata (version numbers, licences, deployment type, vendor details) from system logs or tool-registries.
• These tools help uncover both officially sanctioned systems and “shadow AI” (unauthorised or unmanaged AI tools). One author notes: “begin with visibility: identify every AI system … document what each system does, what data it touches, and who owns it.”

Surveys

• Deploy structured surveys across business units asking about AI tool usage: e.g., “Which AI tools or services (vendor or in-house) do you currently use?”, “What is their purpose?”, “Who owns them?”, “Which data do they consume?”, “Deployment type (API/web/desktop)?”
• To encourage honest responses, ensure anonymity or confidentiality where possible, reducing fear of reprisal.
• Use quantifiable questions (drop-downs, check-boxes, standardised options) so the results can be aggregated and analysed.
• For example: “Frequency of use: daily / weekly / monthly / ad-hoc”; “Deployment: web interface / installed app / API”.
• Surveys provide breadth of coverage, especially for tools that aren’t yet formally approved or tracked.

Interviews

• Conduct interviews with key teams — especially development/innovation groups, business units experimenting with new AI use-cases — since they may be using tools informally or in pilot mode.
• Use standardised questions to avoid incomplete or unusable data. For example: “What AI tools are you using?”, “What is the purpose?”, “Which data sources?”, “Who is responsible?”, “What’s the deployment status?”, “What risks/actions have been considered?”
• Avoid overly open-ended questions which can generate rich insight but may be hard to aggregate. The structured approach aids aggregation, reporting and governance.
• Interviews are especially useful for capturing experimentation or shadow AI which may not yet appear in formal tracks.

Documentation and integration

• Regardless of the discovery method, organisations should maintain artifacts such as an AI policy, inventory documentation, asset registers, and link them to standard asset-management platforms (e.g., ServiceNow, Jira).
• Documentation should reflect that the inventory is a living document and not a one-time project. One guide states: "For businesses adopting AI, maintaining a comprehensive AI inventory is no longer optional—it’s a necessity."
• Ensuring the inventory integrates with other governance tools (model registries, MLOps tools) will support automation of updates and risk-monitoring.

Data fields & standardisation

Your list of data fields is helpful and aligns well with best practices. For each AI system, capture:

• AI system name
• Version number
• Licence (if needed)
• Cost
• Deployment type (web interface, installed app, API)
• Purpose of the AI system
• Frequency of use
• Stakeholders
• Accountable organisation/owner
• Third-party/vendor details (if applicable)

Ensure each field is standardised where possible (drop-down lists, standard categories) to enable aggregation, reporting, and dashboards.

Due to the dynamics of AI and its rapid adoption in organizations, the methods above may not be efficient for data collection. Automated tools for log ingestion, AI use-case interpretation, model enumeration, and the identification of potential violations and risks are more efficient.

Maintenance & Dynamic Nature of AI Inventory

One of the features that distinguishes AI systems (and therefore their inventories) from traditional IT assets is dynamic change: models drift, data distributions shift, new use-cases emerge, business priorities evolve, experiments become production, and some projects get de-scaled. The inventory process must account for this.

• The inventory needs to be living: not a static “once-and-done” list. Organisations are encouraged to embed periodic reviews and triggers for updates when changes occur (model updates, deployment changes, vendor replacements, exit strategies).
• Ownership and accountability must be clear: each entry should have a business owner, technical owner, and a governance/review owner.
• The inventory must include information about retirement or decommissioning of AI systems (just as you would with IT assets).
• Shadow AI (departments adopting AI outside IT/governance oversight) is a real challenge: the inventory approach must include the discovery of these “hidden” systems, or the risk remains unmanaged.
• Integration with lifecycle management (model registry, MLOps, monitoring, performance drift, bias tests) helps link the inventory to operational realities.
• Dashboards and reports should highlight changes (new systems added, retired, risk status changed) to support governance and executive oversight.

Summary & Recommendations

In summary:

• AI solutions are complex, dynamic, and often embedded across many systems and business processes; they differ materially from conventional IT assets.
• To manage them responsibly, organisations need an AI-management system: governance, inventory, lifecycle management, risk oversight, and communication.
• A core component is the AI inventory: a detailed, living repository of AI tools/systems, their metadata, ownership, purpose, risk classification, and deployment status.
• The inventory should be modelled after existing IT asset inventory practices (leveraging asset management platforms and standard processes) but adapted to AI’s specific characteristics.
• Data-gathering should use systems/network discovery, surveys, interviews, and documented artefacts to ensure broad and deep coverage.
• Communication and culture are critical: employees must feel safe to disclose AI use, know that the inventory is for oversight not punishment, and leadership must visibly support transparency.
• Maintenance must be ongoing, dynamic, integrate with governance and MLOps, and include discovery of shadow AI.
• Finally, the inventory and broader management system become strategic assets: they enable organisations to innovate with AI while controlling risk, satisfying regulators, and delivering business value.

Recommendations for next steps:

• Begin with a gap analysis: what AI systems/tools do you already know about? What existing asset/inventory process is in place, and is it sufficient for the dynamics and rapid change of AI use?
• Define your metadata schema for the AI inventory (use the field list you already have) and decide on the tool/platform (spreadsheet, asset-management system, custom tool).
• Launch a pilot data-gathering exercise in one business unit to populate initial inventory entries and test the process. Test the speed of ingestion and maintenance of the register.
• Classify entries by risk level and identify high-risk systems (and the underlying LLM) that require immediate governance attention.
• Communicate broadly to business units and employees: explain what you’re doing, why, how they can participate, and how they will be supported.
• Establish process for ongoing maintenance: schedule reviews, trigger-based updates, dashboard reporting, governance review cycles.
• Link the inventory to your broader AI governance programme by connecting it to model registries, compliance reviews, MLOps pipelines, and audit logs.

Conclusions

Every organisation that uses AI needs a dedicated AI Management System (AIMS) with a central, continuously updated AI inventory as its backbone. 

An AI inventory records each AI system, its purpose, data, underlying models, risk level, and—crucially—its business and technical owners. This enables visibility, lifecycle control, and risk-based governance, and is now a foundational expectation under frameworks such as the EU AI Act, ISO 42001, and NIST AI RMF. 

However, the article also makes clear that manual inventories and surveys are not enough. Given the speed and scale of AI adoption, organisations need automation: tools that discover AI usage from logs and systems, maintain the inventory in real time, and automatically classify risks and potential violations. 

Building on this, a complete AIMS should automatically:

• Maintain the AI inventory as a living register.
• Map each system to relevant laws and standards (e.g., EU AI Act) and alert when behaviour or configuration may breach requirements.
• Assign and track asset owners accountable for managing risks and implementing controls.

If these capabilities are automated and embedded into normal operations, organisations adopting AI can enter and operate in the EU market with far greater confidence—demonstrating governance, accountability, and compliance at scale while still innovating quickly.