The 7 AI Terms Every CFO Needs to Understand

Enterprise artificial intelligence (AI) is changing the language of business just as fast as its operations.

On Monday (June 22), for example, Santander announced it was extending AI tools to its entire 185,000-strong workforce. And for finance teams needing to infer the meaning of new terms like “inference,” understanding AI means understanding an entirely new vocabulary of value creation, risk management and capital allocation.

At first glance, the shift appears technical. As AI becomes more embedded in enterprise operations, finance executives simply need to learn the terminology necessary to evaluate new investments. But the emergence of an AI dictionary for CFOs is not primarily about technology literacy. It is evidence that a new category of corporate asset is taking shape inside organizations, one that sits somewhere between capital, labor, software and infrastructure.

The rise of AI introduces a new resource constraint: compute. The challenge for finance leaders is that there is no established framework for managing it.

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From Capital Allocation to Compute Allocation

Terms like liquidity, working capital, operating margin and free cash flow have traditionally dominated conversations between finance leaders. Today, a growing number of CFOs find themselves encountering an entirely different vocabulary: inference, throughput, model compression, scaling laws, rack density, AI factories and FLOPs.

The PYMNTS Intelligence report “Smart Spending: How AI Is Transforming Financial Decision Making” found more than 8 in 10 CFOs at large companies are either already using AI or considering adopting it.

Every AI-powered customer interaction, automated workflow, prediction engine and generative application consumes computing resources. Every model requires infrastructure, every inference generates cost and every increase in performance carries implications for capacity, energy consumption and economics.

Questions surrounding AI deployments may sound technical, but they are fundamentally financial. The companies that derive the greatest value from AI are unlikely to be those that spend the most. They will be those that allocate intelligence most efficiently.

Unlike conventional software, costs do not stop once deployment is complete. Every interaction carries a variable expense, and performance improvements often require exponentially greater computing resources while capacity constraints can become strategic bottlenecks. For CFOs, this creates a more nuanced framework for evaluating returns. The value of AI may not always appear as direct savings. Often, it emerges through higher productivity, faster decision-making, greater scalability and improved operational resilience.

Research from PYMNTS Intelligence’s “The Enterprise AI Benchmark Report,” which shows that 71% of executives at companies with at least $1 billion in annual revenue believe that organizational readiness is the chief limitation on AI performance. Only 11% said they think AI technology itself is the primary barrier.

See also: CFOs Suffer From Consumption as Tech Teams AI Tokenmaxx

The CFO Dictionary for AI Compute

The evolution of AI is creating more than new technologies. It is creating new frameworks for evaluating investment itself.

Total Cost of Ownership (TCO):

The most important AI question is not what a model costs to buy, but what it costs to operate. TCO captures the full economic picture, including infrastructure, software, talent, data, governance, energy, maintenance and inference expenses. As AI deployments scale, many organizations discover that operating costs far exceed initial investments.

Inference:

Inference is the economic engine of AI. Every customer interaction, prediction, recommendation or generated response requires an inference—and every inference has a cost. Understanding inference economics is becoming as important as understanding transaction economics in payments or customer acquisition costs in digital businesses.

Compute Capacity:

AI is introducing a new enterprise constraint: access to computing power. Just as CFOs historically managed capital and labor scarcity, they are increasingly evaluating whether the organization has sufficient compute resources to support growth, productivity and innovation objectives.

Cost Avoidance:

Many of AI’s largest benefits do not appear as direct revenue gains. They emerge through avoided costs: reduced fraud, fewer operational errors, lower support expenses, faster reconciliations and improved employee productivity. Finance leaders increasingly need frameworks for measuring value that never appears as a line item.

Shadow AI:

Shadow AI represents one of the largest governance risks facing enterprises. Unapproved tools, duplicate vendor spending, inconsistent controls and unmanaged data exposure can quietly create financial and operational liabilities. For many organizations, the challenge is no longer AI adoption but AI visibility.

Latency:

Once considered an engineering metric, latency is becoming a business metric. Faster AI responses can improve customer conversion, reduce fraud losses, accelerate decisions and enhance employee productivity. The economic value of speed is increasingly measurable.

Return on Compute:

Emerging as perhaps the defining metric of the AI era, return on compute measures the economic value generated from AI processing resources. Just as return on capital became a cornerstone of modern finance, return on compute may become a key indicator of which companies are creating sustainable advantage from AI investments.