Building AI into Finance: What's the Plan? 📊

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During conversations with CFOs this week, one question kept coming up: as AI becomes more embedded, is the role of finance to innovate or to regulate? For some organizations, this is no longer a choice – it’s becoming a necessity. The demand for AI tools in finance is growing rapidly, but so is the need for strong controls. How can CFOs ensure the technology supports their goals without getting in the way?

This issue examines the regulatory aspects of AI adoption in finance, a framework for evaluating AI readiness in complex operations, and three articles worth your time.

This number matters because it shows the pressure many finance leaders face to keep up with technological change. However, the pressure to adopt new tools shouldn’t overshadow the need to establish clear governance and demonstrate value. Moving from just experimenting with AI tools to fully integrating them into financial workflows requires more than technology… it needs new strategies for oversight, measurement, and accountability. The key question now is: How do you evaluate AI tools before they become part of critical financial processes?

If your team is rushing to scale AI without clear controls, you’re missing the first step in building an AI strategy.

At a rapidly growing startup, the issue wasn’t whether AI could automate routine tasks. The problem was that everyone assumed AI could do more, but no one had put a plan in place for monitoring its outputs. By the time they realized the risks, the tools had already been integrated, and the outputs were beginning to influence decisions made by top executives.

When the CFO asks for an update on AI’s effectiveness, you’ll have documented results and a clear understanding of its role in your organization instead of a vague story about what it might do.

Alison Staloch, CFO of Fundrise, discusses how she manages early-morning strategic meetings, important financial choices, and overseeing an expanding technology infrastructure, including AI integration in finance.

My take: The lesson here is that routine is everything. Whether you’re evaluating new technology or leading strategic discussions, having a disciplined approach to both planning and execution is crucial. Fundrise’s experience highlights that a consistent schedule is key to ensuring AI tools are implemented with the right strategy and oversight.

Stress-testing AI tools before deploying them in production is crucial, especially in fintech and enterprise environments. Scaling too quickly without thorough testing can result in serious failures.

My view: This is the warning every CFO should pay attention to. Deploying AI without thorough testing is a recipe for disaster, especially as the tools transition from the lab to real-world applications. With incorrect metrics or flawed models, the risks of scaling AI can surpass the benefits.

As the fintech sector develops, FTI Consulting states that the key to further growth is in licensing infrastructure, including AI-powered financial tools. This allows fintechs to generate more scalable, repeatable value for clients and investors.

My take: The future of fintech depends on infrastructure that can scale easily. Licensing AI tools and tech infrastructure will help firms move beyond fragmented systems and offer more integrated services, lowering operational risk and increasing value for clients. This strategic shift will be key to staying competitive in a fast-changing industry.

Many finance organizations skip a critical step before scaling AI: they rush to deploy the tools, thinking they are ready for widespread use, without first ensuring they have the right infrastructure in place. The result is that AI tools underperform or cause more issues than they resolve.

Can you currently provide a list of all AI tools in your organization, the systems they impact, and how they are performing relative to your stated goals? If not, consider making this your next project.

CFOs are no longer just deciding whether to adopt AI… they’re figuring out how to do it without sacrificing control, security, or operational efficiency. Growing too fast can be just as risky as moving too slowly.

This week’s articles highlight the shift from theory to practice. From Alison Staloch’s disciplined approach to managing growth and technology at Fundrise to the tough lessons learned by those scaling AI too quickly, the main message is clear: CFOs need a structured plan to evaluate AI tools and test their readiness before deployment.

When AI adoption feels like a sprint, it’s important to remember that maintaining a steady pace with a clear plan will help ensure long-term success.

Until next edition. — Marcus Reid, CPA.

P.S. If your organization is expanding AI tools, I want to hear about your pre-scale assessments and evaluation criteria. What frameworks are you using to test if the tools are ready for production? Please reply directly.