This article was first published on August 26 2026 on The Economic Times CFO.com. Please click here to read the article.
For decades, enterprise technology spending followed a predictable pattern. Companies invested in software, infrastructure, and talent, and then extracted value over several years. However, unlike traditional technology investments, AI introduces consumption-based economics where every prompt, model invocation, agent interaction, and inference request carries a potential cost. As organisations increasingly leverage AI, many are discovering that the challenge is no longer accessing AI capabilities, but controlling the economics of operating them at scale.
This change in innovation curve driven by AI has elevated AI from the CIO’s desk to the boardroom. Boards are asking tougher questions: Which investments are delivering measurable business value? What is our RoI on AI initiatives at scale? How do we scale adoption without creating an unsustainable cost structure? For CFOs and CIOs, AI is becoming one of the most important tests of balancing innovation, risk, and shareholder returns. As per KPMG’ International’s AI Pulse survey Q2 2026, 49 per cent organisations have delayed or scaled back AI deployment when benefits are not clearly outweighing costs. This mismatch is often caused by 2 challenges – gaps in AI adoption and gaps in AI cost budgeting. This article will focus on the latter.
The economics of AI change dramatically as organisations move from pilots to production. A proof of concept may demonstrate value at modest cost, but enterprise deployment introduces a far broader cost base: cloud and GPU infrastructure, model licensing, token consumption, data pipelines, cybersecurity controls, governance mechanisms, human oversight, AI talent, and many others. As AI adoption expands across business functions, organisations often realise that their original business case captured only a fraction of the true total cost of ownership and that while AI can generate substantial returns, it needs cost and value to be managed with equal rigor.
As a first priority, CFOs and CIOs should ask early on about the hidden costs that an enterprise-wide deployment entails. Examples of such costs include costs related to legacy system integration (hidden in legacy application budget), periodic model retraining (often not included in the budget or included in cloud budget), AI talent costs (often hidden in HR budget or contractor budget), security and monitoring costs (often shows up as SOC overheads) and others.
Post approval, CIOs and CFOs should focus on reducing costs using relevant Technology architectures without sacrificing business benefits. As per the aforementioned survey, access to lower-cost, high-fidelity models is the fastest-rising influence on AI strategy today. Employees often default to using the most powerful models for every use case, creating avoidable costs. Instead, enterprises should adopt techniques such as dynamic model routing, where routine requests are directed to smaller, lower-cost models while complex tasks are escalated to premium models in alignment with business value and risk, or cascade architectures, which start with a cheap model and escalate only when confidence is low.
Third, all stakeholders need a much clearer, data anchored understanding of AI cost pools. Leaders must identify the major drivers of spend, understand unit economics such as total cost per token or cost per business interaction, and establish show back transparency into how business functions are generating value versus consuming resources. Some companies have gone as far as to create show back dashboards so that P&L centers gain greater insights into their spends. Without this level of visibility, it is hard to optimise AI investments at scale. Any cost optimisation should operate within established governance controls, including human oversight, model validation, security and regulatory compliance requirements.
These capabilities extend FinOps principles into the AI landscape by continuously monitoring demand, consumption, and costs, and recommend techniques to optimise AI costs without sacrificing benefits. Together, they allow organisations to govern AI spending proactively rather than reactively. In many enterprise AI initiatives, the original investment case reflects only a fraction of the true economics, with upfront estimates often representing just 25 per cent to 35 per cent of the total three-year cost of ownership.
The remaining categories become evident much later and are dispersed across functions in a way that makes them nearly invisible to the CFO reviewing the case at the point of approval. Therefore, it’s important for companies to harness AI sustainably, allowing capabilities to compound into an enduring competitive advantage that creates exponential business value.
