What this guide helps you evaluate
technology, finance and governance teams controlling agentic-AI operations, model access and policy at enterprise scale. Use this implementation checklist to turn an approved ai governance platform decision into owned tasks, acceptance evidence and a controlled transition to operations.
This page is designed to help you compare the moving parts, organize due diligence and ask better questions before you commit money, sign a contract or change an operating process.
A useful review starts by defining the business outcome, decision owner, expected term and the evidence needed to validate use-case inventory policy and approval workflow.
For ai governance platform, normalize use-case inventory policy and approval workflow, model risk evidence and monitoring and regulatory reporting integrations and licensing before comparing quotes, vendors, contracts or internal options.
Keep assumptions separate from verified facts. Record the source, date and owner for pricing, legal, tax, insurance, security or operational requirements that may change over time.
What to compare first
- use-case inventory policy and approval workflow
- model risk evidence and monitoring
- regulatory reporting integrations and licensing
- implementation ownership and critical path
- data, integration, configuration and evidence readiness
- acceptance criteria, rollback and handover
Step-by-step process
- 01
Name the implementation owner, executive approver, operational owner and every external dependency.
- 02
Convert use-case inventory policy and approval workflow, model risk evidence and monitoring and regulatory reporting integrations and licensing into testable deliverables with due dates and acceptance evidence.
- 03
Prepare AI use-case inventory, architecture and model-provider map, usage baseline, vendor proposal and governance requirements plus required data, access, configuration, security reviews, training and migration inputs.
- 04
Run acceptance checks against the signed scope, record exceptions and define rollback or remediation actions before go-live.
- 05
Complete handover with operating procedures, support contacts, renewal dates, evidence retention and post-implementation review metrics.
Common mistakes and risk checks
- buying AI tooling before defining control ownership
- optimizing token cost without outcome measurement
- creating new routing or policy dependencies without exit planning
- starting configuration before scope and acceptance criteria are signed off
- going live without an operational owner, support path or retained implementation evidence
- Treating a implementation checklist as a substitute for the signed agreement, current official rules or qualified professional review.
Documents and evidence to collect
- AI use-case inventory
- architecture and model-provider map
- usage baseline
- vendor proposal and governance requirements
Questions to ask before approval
- What must be demonstrably true before go-live can be approved?
- Which dependency can delay implementation even if the selected provider completes its own work?
- How is use-case inventory policy and approval workflow defined, measured and evidenced?
- What changes if model risk evidence and monitoring is higher or lower than the base case?
- Which fees, exclusions, implementation tasks or operating duties sit outside regulatory reporting integrations and licensing?