The following is a guest post from Dean Quiambao, partner at Armanino. Opinions are the author’s own.
CFOs are under pressure from two directions at once.
On one side, capital markets remain selective. IPO activity is uneven as investors scrutinize spending decisions and favor companies with clear value creation and growth stories. They expect finance leaders to justify every dollar.
On the other side, organizations hear from seemingly every corner that they need to move aggressively on artificial intelligence or risk falling behind.
CFOs are expected to walk a tightrope, supporting big investments in artificial intelligence while simultaneously proving those investments generate measurable returns. It doesn’t help that many companies hamstring CFOs with an overly narrow definition of return on investment.
Across the market, we still see business leaders use cost reduction and efficiency as the primary measurements of whether an AI investment is worth the expense. CFOs ask questions like: How many hours did we save? How much manual work did we eliminate? How many people can we operate with?
Those are fair questions. Many of the first-generation enterprise AI tools focused heavily on productivity. Finance teams used them to automate reconciliations, while customer service teams slashed response times. It’s reasonable to measure AI in terms of efficiency gains when they explicitly marketed themselves as efficiency tools.
But I think many organizations stopped at “efficiency” and never looked further into what this technology can do. The problem with measuring AI only through efficiency metrics is that it frames technology as a defensive investment instead of a strategic one. “Do this or get left in the dust by early adopters.” If the only outcome being tracked is expense reduction, it creates a blind spot for the areas in which AI can create far more enterprise value.
I see businesses unlock real enterprise value when they take the next step and link AI initiatives directly to outcomes. Instead of asking, “Is this saving us money?” they look at the speed of customer acquisition and in-market moves. They look for AI tools that can improve their forecasting and decision-making. These CFOs look for the ways this technology can give them genuine competitive advantages.
AI ROI is becoming a strategic measurement problem
Instead of measuring cost and efficiency, AI-forward competitors build organizations where automation supports faster execution, leaner decision-making and greater scalability across the business. Over time, those advantages compound.
CFOs in these organizations see the opportunity costs in underestimating how quickly AI can reshape competitive positioning inside an industry. And their role, increasingly, is to demonstrate to their peers how AI plays a role in divining the creation of future enterprise value and how capital should be allocated to support it.
The organizations I see that have stuck the landing with AI implementation share a common trait: CFOs who avoid measuring AI strictly within the finance function.
By that I mean, effective CFOs work with operational leaders before investments are made, and make room for the views of leaders and workers outside of pure finance. They are sitting with sales, marketing, product and operations teams to define what success actually looks like before technology is deployed.
You need everyone at the table, because AI outcomes rarely show up cleanly in a single department. An automation initiative inside customer support may improve retention. Better forecasting tools may improve inventory management and cash flow simultaneously. AI-enabled analytics may strengthen both marketing efficiency and revenue growth.
AI is ultimately a capital allocation decision
Many CFOs are still approaching AI as an IT expense and an efficiency play. If you want to see AI as the strategic capital decision it actually is, you will need a broader definition of ROI.
This larger view should not exclude efficiency. Cost savings and automation are still important, but it’s the start of the conversation, not the end of it.
Next, look at revenue impact. Can the tools materially influence business outcomes that spur growth? Can you draw a clear line between AI and customer retention, or faster product development or more effective pricing strategy?
Finally, weigh the long-term strategic value of your AI investment. Faster decision-making, better data visibility, stronger forecasting capabilities and improved organizational agility will not show up on day one of your implementation, but they are worth tracking over time
If CFOs want to weigh AI’s real potential, look beyond dollars saved and measure how it helps organizations move faster, make better decisions and create opportunities competitors cannot.
