01
Start with scope, role and intended purpose
The same technology can lead to different obligations depending on how it is placed on the market or used. An organisation may act as a provider, deployer, importer, distributor or authorised representative. That role analysis should be recorded for each relevant AI system and kept connected to the system’s intended purpose.
The first operational deliverable is therefore an AI inventory with accountable owners, use cases, model and vendor dependencies, affected people, geographic reach and lifecycle status. Classification without this context is fragile.
- Record the AI system and its business purpose
- Identify the organisation’s role in the AI value chain
- Document the reasoning behind scope and exclusions
02
Translate requirements into controls and evidence
Once scope and risk are understood, each applicable requirement should map to a control, a responsible owner and evidence. Depending on the use case, that may involve human oversight, technical documentation, data governance, logging, transparency notices, risk management, post-market monitoring or incident processes.
A defensible programme keeps facts, legal interpretation and organisational decisions distinct. It also preserves dates, versions and source links so reviewers can reconstruct why a decision was made.
03
Treat compliance as a monitored lifecycle
The AI Act entered into force on 1 August 2024 and its provisions have applied in phases. The European framework continues to be supported by guidelines, standards and implementation material. Compliance records should therefore have review triggers rather than remaining static.
A useful monitoring process detects an official change, verifies it, assesses which systems and controls may be affected, creates owned actions and retains the resulting evidence.