The 5-step CFO AI policy framework
Owning the AI conversation demands clear rules of the road that enable speed and control, while driving innovation forward:
Step 1: Define who owns AI decisions
AI governance should not live solely with IT. When it comes to the finance function, CFOs and controllers should have explicit ownership over how AI is used.
Establish a cross-functional governance committee, supported by an AI policy charter that defines acceptable use and “no-go” triggers; a clear RACI matrix that assigns decision rights across finance, IT, risk, and the business; and a structured meeting cadence to ensure ongoing oversight.
The goal is a single accountable operating model that enables innovative adoption… while protecting financial integrity.
Step 2: Define what counts as AI
Ambiguity is the enemy of compliance. CFOs must clearly define what is “in scope” by aligning on what constitutes AI within the finance function (this could include native ERP AI features, GenAI drafting tools, agents and automations, and Model Context Protocol (MCP) connectors accessing finance data).
Anchor these definitions to core finance processes – AP, AR, close, FP&A, and treasury – to remove guesswork and ensure consistent application across tools and use cases.
To operationalize this clarity, establish:
- An AI-in-finance taxonomy that standardizes how AI capabilities are categorized and discussed
- An in-scope inventory template that documents where AI is used, by process, system, and risk profile
These artifacts create a shared understanding of what is governed, monitored, and controlled, forming the foundation for compliant AI adoption.
Step 3: Define how AI use cases are approved
Finance teams need a single front door for AI requests, supported by clear escalation paths and approval thresholds. Without this, low-risk experimentation and high-risk use cases get conflated, slowing adoption and increasing exposure.
A practical red/yellow/green decision framework creates clear separation between routine and sensitive use cases:
- Green: Routine drafting and summarization with minimal risk
- Yellow: Forecasting and reporting support that requires human review
- Red: Use cases involving PII, deal data, or attempts to bypass audit controls in unapproved tools
To put these colors into practice, establish a defensible approval model that encourages responsible experimentation while preserving control and auditability. This includes:
- A risk-based decision rubric that consistently classifies AI use cases
- A centralized intake and routing workflow that directs requests to the right approvers at the right time
- Clear exception protocols that document deviations, approvals, and required safeguards
Step 4: Define who sees what – and where humans intervene
AI should be a drafter rather than a filer. Any output that impacts the general ledger, management reporting, or external disclosures must be subject to documented human review before it is finalized or relied upon.
This requires explicit guardrails around who can see, use, and act on AI-generated outputs. In practice, this means limiting elevated or “super-user” AI access, establishing clear review and sign-off expectations, and defining evidence and retention requirements that withstand audit scrutiny.
To make these controls durable, formalize:
- Access rules and approval standards that govern where AI can operate and who can act on its outputs
- Evidence retention policies that specify what is preserved, for how long, and for which use cases
- Updated SOPs that embed human-in-the-loop requirements directly into day-to-day finance workflows
Step 5: Define how AI is operationalized and monitored
When AI adoption is left unmanaged, tools proliferate, controls weaken, and visibility fades. Rather than allowing ad hoc experimentation, CFOs should be intentional about where and how AI is operationalized within the finance environment.
Start by prioritizing AI capabilities embedded in core ERP platforms (such as NetSuite), complemented by a defined set of approved enterprise tools. Where finance data is accessed through MCP connectors, permissions, credentials, and ongoing monitoring must be explicitly governed.
To enable scale without fragmentation, put in place a controlled deployment model that includes:
- An approved AI tool catalog that defines which platforms and capabilities are sanctioned for use
- Vendor evaluation standards that assess security, data handling, and controllership impact before tools are introduced
- Connector guardrails and monitoring practices that provide visibility into access, usage, and exceptions
How NetSuite and Accordion help CFOs get there
At the end of the day, AI governance is more than a technology issue. It’s an operating model transformation.
NetSuite provides a unified platform to support that shift. By embedding AI directly within its ERP, organizations can reduce risk exposure while enabling automation and scalable insight.
Accordion layers governance, process, and people over NetSuite’s technology. As a strategic architect and hands-on expert, Accordion helps CFOs design AI policies, manage organizational change, and align adoption to the investment thesis and exit timeline.
Together, NetSuite and Accordion help CFOs deploy faster, innovate better, and drive measurable EBITDA improvement through trusted automation.