Artificial intelligence is transforming the corporate landscape faster than any technology in recent memory. Finance departments are leaning into the change by investing in predictive forecasting tools and software that automates reconciliations.
However, this acceleration of spending also increases the risk of misalignment. When businesses purchase new tools without clear ROI models or the support of focused workforce upskilling, the time to value is extended.
CFOs and controllers are positioned to bring order to this complex time of change. Effective oversight ensures AI initiatives deliver measurable financial impact rather than becoming another line-item experiment.
Why AI Procurement Requires Financial Leadership
Traditional IT procurement focuses on functionality, licensing, and infrastructure. When it comes to AI solutions, the equation becomes more complicated. Organizational decision makers must consider factors such as data ethics, transparency, and business outcomes. So-called “Blackbox” AI solutions can (and should) give business leaders pause due to concerns about how decisions are made and whether bias is present.
Unlike static software, AI models evolve over time. To unlock the full financial value of artificial intelligence, businesses must continually monitor and refine it. The finance department brings much-needed discipline in forecasting and variance analysis, which protects organizations from chasing “AI hype” and instead ensures they unlock actual value.
Establishing a Governance Framework
AI procurement governance is most effective when it mirrors the financial control principles of transparency, accountability, and auditability. A well-structured framework includes:
- A committee of decision-makers that involves finance, IT, compliance, and operations
- Projects that are prioritized on actionable models instead of vendor promises
- Ethical and regulatory compliance
- Thorough audits that assess realized ROI compared to projections
CFOs and controllers are exceptional at institutionalizing governance principles in a way that supports AI adoption. Getting them involved early and often also protects the financial integrity of the business.
Building the Business Case for AI
Finance leaders must hold every AI proposal to the same rigor as a capital investment. Start with three essential questions:
- What measurable outcomes will this tool deliver?
- How will we track ROI?
- Who is accountable for success?
Controllers can support this process by quantifying cost savings, opportunity costs, and labor reallocation from automation. They bridge the gap between the excitement associated with AI and financial discipline.
Evaluating Vendors With Financial Precision
AI vendors often market their solutions as transformative or cutting-edge. While artificial intelligence is already reshaping business as you know it, it’s important to remember that many solutions are immature and lack transparency. Before committing to a vendor or product, you should insist on:
- Short test periods to validate the vendor’s claims
- Data ownership so that the company data remains the organization’s intellectual property
- Systems that can expand to fit your future needs without an exponential increase in cost
- Alignment with your existing technology suite, including your ERP and analytics platforms
These principles mirror how controllers validate accounting software. Integrity and traceability are, and should always be, guiding objectives.
Partnering With IT and Compliance
While the finance department needs close oversight into AI procurement, it is not a substitute for IT or compliance. CFOs bring fiscal accountability. Your IT department is responsible for delivering technical insight, and compliance professionals protect your business by promoting adherence to relevant regulations.
Together, these groups can define AI risk categories and separate low-risk automation from high-impact decision models that require greater oversight. When they work together, these teams promote innovation while protecting the business from the inherent risks of mass data consolidation.
Controllers can be even more involved in the process by leading ROI tracking and model validation after the artificial intelligence platform goes live. These financial feedback loops keep leadership informed about actual vs. expected performance.
Tracking ROI Beyond the First Year
Unlike traditional software, AI ROI compounds or decays over time. CFOs should track return on investment using both hard and soft metrics. Examples of hard ROI relevant to AI include cost savings and revenue lift. Soft metrics include time reallocation, accuracy, and risk reduction.
Both are relevant and interconnected. For example, a reduction in manual hours can lead to cost savings while also promoting time reallocation to more useful tasks. Reviewing these metrics quarterly helps the finance department recalibrate AI investments and sunset tools or tactics that simply aren’t working.
AI Accountability Is Financial Accountability
The age of AI calls for a new kind of financial stewardship. If your business is ready to step into this phase of technologically driven innovation, it must double down on fiscal discipline. CFOs who apply rigorous financial oversight to AI procurement protect both their budgets and their organizations’ credibility.
Your finance team should start by defining ROI up front and enforcing transparent governance from day one. Additionally, it must validate outcomes on an ongoing basis to ensure that emerging tech contributes to measurable enterprise value. In doing so, the CFO becomes a strategic innovator.
