The following is a guest post from Ankit Chopra, director of FP&A at Neo4J. Opinions are the author’s own.
Artificial intelligence is moving beyond experimental pilots or isolated use cases toward reshaping how companies operate, compete and grow. The investments in this space are rapidly becoming a core consideration in capital planning. For financial leaders, AI now represents a significant investment, and their teams are increasingly tasked with distinguishing hype from financial reality.
AI holds immense potential, but realizing its true value demands a new level of financial discipline, precise metric alignment and robust risk management. A finance executive’s role is to ensure every dollar spent on AI propels the organization closer to measurable and scalable business outcomes.
AI offers undeniable revenue upside. Embedded in digital products, it enables smarter personalization, faster customer response and more predictive sales cycles. Yet, projected revenue gains can often be overstated or disconnected from actual adoption. The risk for financial teams lies in approving AI projects based on ambitious growth narratives and vague attribution logic.
While AI investments might boost conversion rates or reduce churn, unless these gains can be isolated and tracked over time, they remain assumptions and not proven outcomes. Finance leaders should collaborate closely with business heads to clearly define where AI is expected to drive the intended uplift, ensuring adoption metrics and revenue attribution models are firmly in place before declaring early success. Truly transformative projects often succeed or fail based on real-world usage.
The quiet erosion of ROI
While the initial build or license fee for an AI system may seem contained, the true costs are likely to emerge over time. Retraining models as inputs evolve, maintaining compliance with new AI governance standards and monitoring for performance drift all demand ongoing investment. Specialized tools, data labeling platforms and observability stacks also contribute further to the costs, many with subscription costs that scale with usage.
Data infrastructure represents another significant hidden cost. AI thrives on clean, secure, well-structured data, which all organizations don’t possess readily. Building and maintaining robust pipelines, ensuring data governance and avoiding silos require both technical resources and substantial financial commitment. Financial leaders must assess the total cost of ownership across the full lifecycle of the investment. These include development, deployment, maintenance and scaling. A technically sound model with a poor cost structure is likely to diminish margins over time.
From technical KPIs to business value
Organizations can’t improve what they cannot measure. Many AI initiatives are judged solely on technical criteria like model accuracy, inference speed, or latency. While these are necessary, they are rarely sufficient for assessing business impact.
Strategic AI investments demand business-aligned metrics. For instance, “productivity lift per full-time equivalent” quantifies how AI augments the workforce, measuring additional output or efficiency gained through AI copilots or automation. “Adoption velocity” measures how quickly internal teams embrace AI features thereby signaling whether the solution is creating practical value.
Other emerging measures include “cost per decision” or “cost per outcome,” which reframes AI as a decision enhancer or outcome driver. Revenue metrics like “AI-attributed revenue contribution” measure cross-functional collaboration to determine how much of a new sale or upsell is genuinely driven by AI.
These metrics can foster accountability and also shape future budget allocations by building the narrative for long-term value. The guiding principle is that none of the individual metrics are perfect and organizations need to focus on a combination of key performance indicators that drive the most value in their specific business context
Reshaping the returns landscape
The economics of AI are rapidly shifting. Inference costs, once a limiting factor, are declining due to hardware optimization and more efficient models. However, as costs fall, usage often rises unchecked, leading to new budgeting challenges around overuse or redundancy. The right set of optimizations and alerts on usage can keep these costs in check.
Simultaneously, providers and cloud platforms are adjusting their pricing structures. APIs may become more expensive, rate-limited or bundled differently, catching unprepared teams off guard. AI investments that were viable under one pricing structure can suddenly become uneconomical.
Another evolving trend to watch out for is related to policy shifts. From the EU AI Act to pending U.S. regulations, compliance requirements are expanding, often adding new layers of documentation, oversight and internal governance. Adjusting to policy changes usually comes at a cost that is rarely factored into initial forecasts.
Collectively, these trends point towards the importance of agility. Financial models must be flexible enough to accommodate shifts in regulation, supplier pricing, and usage behavior.
Risk factors and policy considerations
As artificial intelligence integrates more deeply into enterprise processes, the risk landscape expands. Data privacy violations, regulatory penalties and model misuse are tangible liabilities, especially in sectors like healthcare, finance and public services. The absence of a coherent AI governance policy can expose companies to not only brand risk but also financial and legal consequences.
Vendor lock-in is another growing concern. Once AI systems are integrated into core workflows and fine-tuned for domain-specific performance, switching vendors becomes expensive. Over time, this can limit negotiating power and inflate operating costs. Financial executives must also anticipate emerging risks related to taxation, intellectual property rights and the attribution of synthetic content. Depending on the jurisdiction, the development and use of generative models may have implications for transfer pricing, licensing or audit exposure.
Additionally, industry dynamics and business sector trends matter. For instance, a media company might face low risk deploying AI for content generation, whereas a financial institution introducing AI into credit decisions needs to meet much higher thresholds for explainability, audibility and compliance.
Managing AI as a strategic portfolio
Artificial intelligence should not be evaluated as a single asset; it should be managed as a portfolio of strategic bets. Some projects will deliver rapid return on investment, others will require longer gestation periods, while some may fail. What truly matters is establishing the right governance to track performance, applying a shrewd financial lens to evaluate cost and risk over time and maintaining the discipline to divest or reinvest accordingly.
Ultimately, the companies that lead won’t necessarily be those with the most advanced models or access to huge capital. They will likely be the ones where finance teams ask sharper questions, track the right outcomes and align every dollar of AI spending to measurable and enduring business value.
