Mapping Your AI System Landscape

Building Your AI System Inventory

The process of constructing a robust AI Management System (AIMS) begins with a clear understanding of your organization’s AI landscape. This understanding is encapsulated in an AI System Inventory, which is crucial for effective governance and assurance. The aim is to identify and document all AI systems in use, which may often be hidden within various tools and departments.

Understanding the AI Landscape

AI systems can be surprisingly elusive. They may be embedded in vendor software, automation tools, or running as experiments across different departments. This makes the first task defining a clear scope to map out the AI systems being utilized. As the saying goes, “You can’t govern what you can’t see.”

The mapping process is not merely an academic exercise; it involves a critical conversation with stakeholders to ensure everyone understands the boundaries of the inventory. Questions to consider include:

• Which departments are included in the mapping?
• What types of AI systems are essential for initial governance efforts?
• Are both internally developed and vendor-provided AI systems included?
• Do you have the necessary access and authority to map these systems?

Defining Use Cases, Capabilities, and Systems

Once the scope is defined, it is time to start mapping out the AI systems, their capabilities, and how they are utilized in practice. For example, in the realm of talent management, an organization may deploy two AI systems: TalentMatch, a recruitment platform, and PathFinder, a career development suite.

While these may appear as separate tools, mapping reveals shared capabilities that create dependencies in their use. For instance, TalentMatch can analyze resumes and predict job fit, while PathFinder can identify skill gaps and generate personalized development plans. Documenting these interconnections is vital, as it can highlight how capabilities interact and affect various use cases.

Key Definitions

To better understand the mapping process, here are some critical definitions:

• Use Case: A specific situation where AI technology is applied to achieve a business objective.
• Capability: A distinct function that AI can perform, such as resume analysis.
• System: The technological implementation that delivers these capabilities.
• User: Someone who directly interacts with the AI system.
• Stakeholder: Anyone affected by or interested in the AI system’s operations.
• Misuse Case: A scenario where the system’s capabilities could be exploited or misapplied.

Identifying Stakeholders and Misuse Scenarios

Mapping stakeholders is crucial, as they provide different perspectives on potential misuse scenarios. For example, a manager could misuse AI capabilities intended for talent development to identify employees likely to leave the company. Identifying these risks early allows for better governance and monitoring.

By documenting relationships between users, stakeholders, and potential misuse cases, organizations can build a foundation for robust AI governance and accountability, ensuring that systems are not only effective but also responsible.

Step-by-Step Guide to Building Your AI Inventory

To systematically document your AI landscape, follow these steps:

1. Start with a simple spreadsheet to provide flexibility and accessibility. Create separate tabs for Systems, Capabilities, Use Cases, Users, and Stakeholders.
2. On the Systems tab, include basic information: system name, owner, vendor, and description.
3. In the Capabilities tab, list every distinct AI capability and note which system implements it.
4. On the Use Cases tab, document the use case name, description, and primary business objective.
5. In the Users tab, list every type of user who interacts with the AI systems, specifying their roles and departments.
6. The Stakeholders tab should include every group affected by or interested in the use cases.
7. Using a whiteboard, connect elements with Post-it notes to visualize relationships and create a matrix format for use cases, capabilities, users, and stakeholders.
8. Add a Misuse Cases tab to brainstorm potential misuse scenarios.
9. Update the document regularly to maintain accuracy and relevance.

By maintaining this living document, organizations can create a clear picture of their AI landscape, supporting both innovation and responsible governance.

In conclusion, the challenge is not merely defining AI but ensuring that AI systems are developed and deployed responsibly. This foundational work sets the stage for effective governance, allowing organizations to build safe and reliable AI systems.