A general checklist for discussing AI workflow scope, data handling, review controls, and evidence requirements. It provides no legal classification, compliance determination, or certification assurance.
Flowtaris
Enterprise AI Perspectives
Document the AI-assisted workflow, intended use, affected records, users, and decision boundaries. Identify where outputs inform or trigger actions and who is responsible for review. This checklist does not classify a system under any law or determine which obligations apply. Obtain implementation-specific legal and compliance review where relevant.
Discuss data access, handling, validation, exception review, approval, monitoring, and audit records. Define evaluation criteria and evidence needed to verify the proposed controls. Agree retention and documentation requirements with the relevant specialists rather than assuming a universal period or certification standard.
Identify decisions that require human judgement and define review, correction, escalation, and incident procedures. Evaluate whether records adequately support investigation and oversight. Revisit controls when workflows, data, models, or integrations change. Flowtaris incorporates governance considerations into solution design; this does not establish a certified compliance module or guarantee legal compliance.
Document the intended workflow, users, data sources, and decision boundaries.
Agree validation criteria, human review responsibilities, and escalation procedures.
Confirm evidence, retention, and applicable requirements with the relevant specialists.
Review deployment locations, data access, and third-party processing arrangements.
Identify decisions affecting people and define appropriate review procedures.
No. It is a general planning checklist and makes no legal determination. Applicability, roles, classification, and obligations require review of the specific use case by appropriate specialists.
No compliance status is established by this article. Review the intended use, data, decision effects, deployment arrangements, and relevant requirements for the specific workflow.
Discuss workflow scope, data sources, access controls, validation results, review responsibilities, audit records, and operating procedures. Relevant specialists should confirm the evidence and retention requirements for the engagement.
Robotic Process Automation promised to transform finance. Instead, it created brittle bots that break on day one. Discover how Large Language Models and multi-agent architectures are now delivering on that original promise — and why every CFO needs a migration plan.
After analysing 50 Fortune 500 GenAI-in-finance deployments, the data is definitive: GenAI reduces AP processing costs by 68% on average, with payback periods measured in months, not years. Here is every number you need to make the business case.
Take our 3-minute diagnostic to get a personalised AI automation roadmap for your finance team.
