By Ankit Sarawagi, Chief Financial Officer, Verloop.io
Artificial intelligence has become one of the most aggressively funded priorities in the modern enterprise. Boards want it, CEOs champion it, and technology teams are eager to deploy it. Yet the financial outcomes remain far more uncertain than the market excitement suggests. McKinsey reports that nearly nine in ten companies (88%) now use AI regularly in at least one function, a significant increase from 78% last year, but MIT research suggests that only around 5% of AI pilot initiatives translate into rapid revenue gains, while most fail to generate any meaningful, measurable impact on the bottom line.
This gap between adoption and impact is exactly where the CFO’s role has evolved. In the AI era, finance leaders are no longer just budget gatekeepers. They are the stewards of value discipline. The difference between AI as a competitive advantage and AI as an expensive sunk cost often comes down to whether the CFO demands business-grade clarity or allows hype to substitute for fundamentals.
When AI Is a Real Investment and When It Isn’t
The most important shift CFOs must drive is the reframing of AI from “technology investment” to “capital allocation decision.” AI is not inherently strategic simply because it is modern. It earns investment status only when it is directly tied to outcomes such as cost reduction, revenue uplift, productivity improvement, or risk mitigation.
Consider finance operations, where AI has delivered some of the most provable returns. Automating invoice processing, reconciliations, or spend analytics can reduce processing costs by 30-50%, while improving speed and accuracy. These are measurable gains with clear operational baselines and direct margin impact.
The opposite scenario is equally common: organisations funding AI initiatives because competitors are doing so, without a defined problem statement or a financial lever attached. A project framed as “exploring AI for innovation” is not a strategy; it is an uncontrolled expense until it can answer basic questions: What changes in the P&L? What decision improves? What risk reduces? What timeline applies?
This is also why poor foundations turn AI spending into waste from day one. AI does not fix broken systems; it amplifies weaknesses in data, ownership, and process maturity. Many AI deployments fail not because the model is weak, but because the organisation cannot operationalise the output. If there is no clear process owner, no adoption plan, and no agreed metric of success, AI becomes a layered cost rather than a performance engine. CFOs bring discipline by insisting on defined success metrics before funding begins, not after budgets are spent. If value cannot be tracked, it cannot be claimed.
The CFO as the Anchor Against AI Hype
One of the most practical contributions finance leaders can make is changing how AI is funded. Unlike traditional enterprise systems, AI should not be treated as a single large rollout justified by long-term vision alone. The high-performing organisations are adopting phased investment models: narrow pilots, measurable checkpoints, and scaling only when results materialise.
This is where CFOs must be willing to do something that is culturally difficult but financially essential i.e. stop projects early. AI creates a unique sunk-cost trap: once a company has invested in platforms, consultants, and pilots, leadership feels pressured to continue even when business impact is unclear. The CFO’s value lies in preventing momentum from replacing evidence.
Time horizon matters here as well. For most businesses, AI initiatives should demonstrate tangible returns within 12 to 18 months, not vague promises of transformation five years out. The most defensible AI investments are those with immediate operational and financial feedback loops like fraud detection, pricing analytics, supply chain optimisation, credit risk monitoring, working capital forecasting. These are areas where AI can improve decisions quickly, and the CFO can quantify outcomes with rigour.
Ultimately, the CFO’s role in the AI era is not blind participation in technological enthusiasm. It is disciplined decision-making: tying AI spends to measurable levers, ensuring readiness of data and ownership, funding in controlled phases, and enforcing timelines for impact.
