How do I define AIMS scope and conduct context analysis effectively?

Scoping an AIMS is one of the most critical implementation decisions. The scope must be clear, defensible, and aligned with organizational risk appetite and regulatory obligations. It defines which AI systems, business processes, organizational units, and third-party relationships are governed by the AIMS.

Begin by identifying all AI activities within the organization: AI systems in production, AI under development, AI used for decision-support, and AI embedded in products or services. Map these to business functions, data sources, and stakeholders to understand dependencies and impacts.

Context analysis examines internal and external factors that influence AIMS requirements. External factors include regulatory obligations (GDPR, AI Act, sector-specific rules), industry standards, competitive pressures, and societal expectations around AI ethics. Internal factors include organizational strategy, risk appetite, culture, technical capabilities, and resources available for AI governance.

Stakeholder analysis identifies interested parties—customers, employees, regulators, partners—and their needs and expectations related to AI. This informs control priorities: customer-facing AI may emphasize transparency and fairness, while internal AI may prioritize efficiency and auditability.

The scope statement documents what is included and excluded, with justifications. Exclusions must be defensible: typically limited to AI activities that fall below materiality thresholds, are managed under other frameworks, or are out of the organization's control. The scope is reviewed periodically and updated as the organization's AI footprint evolves.