%20(2).svg)
.svg){kind=icon}
.svg)
.svg)
How Finance Can Step Up
CFOs are uniquely positioned to lead AI governance because they understand both financial impact and cross-functional dependencies. That perspective is critical, especially as many companies are moving fast with little formal guidance.
Following Part 1 and Part 2, this section outlines how to assess vendors, manage risk, and align departments. These may not be traditional finance responsibilities, but they’re exactly where finance leaders can extend their influence, turning experimentation into meaningful progress.
1. Evaluating AI Products
You already analyze purchases through the lens of spend and ROI. But with AI, strategic finance leaders go further to verify that each vendor fits into the broader operational strategy.
Here’s what to look for:
ROI and Time-to-Value
Will this tool replace manual tasks, improve reliability, or speed up reporting? How long until we see results? Ask for hard evidence.
Accuracy and Oversight
What happens when the model gets it wrong? Is there human review built in? Whether it’s driving a forecast, influencing customer messaging, or informing executive decisions, accuracy is critical.
Integration and Scalability
Can the tool plug into your existing systems and scale as data volumes and business complexity grow? Look for clean integrations and reasonable implementation timelines.
Security and Compliance
Does the vendor meet your standards (SOC 2, GDPR, etc.)? Are there mechanisms in place to mitigate bias, hallucinations, or other potential issues?
💡 CFOs should evaluate AI like any major investment, testing underlying assumptions, measuring time-to-value, and tying it back to business objectives.
2. Understanding AI Risks
Benefits aside, AI introduces new layers of risk that must be actively managed. As a partner to the business, you can play a key role in preventing missteps.
Here’s what to monitor:
Input Quality
If the data feeding the model is messy, the results will be, too. Check the inputs (and the process for validating them) before trusting the outputs.
Hallucinated or Inaccurate Content
AI tools can confidently give you the wrong answer. In the wrong context, that’s not just a mistake, it’s a hit to your team’s credibility and the company’s reputation.
Security and Privacy Gaps
Some tools may require access to sensitive information. You need to understand where data is stored, how it’s used, and what safeguards are in place.
💡 Consider forming a steering committee with finance, legal, and IT to oversee AI adoption and maintain quality, accuracy, and reliable oversight.
3. Maintaining a Governance Framework
Strong AI governance creates alignment and transparency. In its absence, teams move quickly, but decisions become harder to track and less grounded in shared priorities.
Core components to include:
Ownership
Assign ownership across stakeholders. With finance in a leadership role, AI efforts are more likely to stay aligned with business goals.
Documentation
Maintain a live inventory of tools, use cases, and access levels—so you can track what’s in use, what data it touches, and whether it’s adding value.
Review Cadence
Set regular check-ins for high-usage systems, examining cost, accuracy, and how AI-generated information is being used. A strong feedback loop creates space to revisit assumptions, fine-tune usage, and course-correct as needed.
Controls
Require vendor audits, human sign-off on sensitive content, and approvals for platforms accessing financial or customer information.
Training & Policy
Help employees use AI responsibly, with clear expectations, defined escalation paths, and an emphasis on sound judgment.
💡 AI governance works best when it’s collaborative. Finance brings structure and rigor. Other departments bring domain expertise.
Your Role Going Forward
AI governance offers a path for finance leaders to step into a broader strategic role—guiding how tools are evaluated, how risks are addressed, and how teams stay aligned as usage accelerates. Most finance orgs aren’t there yet, but the opportunity—to go beyond budget approvals and become the connective tissue between innovation and accountability—is growing.
The next wave of finance leadership will be defined by those who bring clarity to complexity. By setting the right guardrails, surfacing misalignment early, and connecting investments to real business results, finance can turn scattered experimentation into sustainable progress. Those who thrive will be the ones who shape the systems, set the standards, and help the business scale with speed and control.
What's Next in This Series
Part 1: From Number Crunching to Strategic Command covered how AI is redefining the work of the finance team, and what changes for you as a leader.
Part 2: Evaluating AI Across the Business explored how to assess AI investments in product, GTM, and operations through a financial lens.
Part 3: Leading AI Governance focused on why finance should lead AI governance, and how to build the right controls for scale.
With this no-nonsense playbook, you’ll know how to lead your company through AI adoption with financial clarity, operational control, and strategic focus.
Billing and Revenue Recognition—Built for SaaS CFOs
AI is transforming how business gets done. Your billing system should keep up. Subscript extracts terms from contracts, generates accurate invoices, and manages revenue recognition, so you can close the books with confidence and capture every dollar you’ve earned.
🔎 Ready to see Subscript in action? Schedule a demo today.
