When new technologies arise, companies may become overly enthusiastic, potentially neglecting fiscal responsibility. Such is the case with artificial intelligence (AI), which has shown promise in enhancing operational efficiency, worker productivity, and customer satisfaction. However, the cost associated with AI investments can be significant, raising the question of how to balance return on investment (ROI) with expenses.
AI’s transformative capabilities are evident, yet its financial implications often remain unclear. Understanding the connection between costs and business outcomes is essential to ensure meaningful returns from these investments. Research from Apptio indicates that a considerable percentage of technology leaders plan to increase their AI budgets, with many predicting AI will drive future budget growth. However, larger budgets do not always correlate with improved outcomes. For instance, despite an expected average spending of $1.9 million on generative AI initiatives in 2024, less than 30% of AI leaders reported that their CEOs are satisfied with the ROI.
As AI projects consume significant resources, particularly in cloud infrastructure, the decentralized nature of these costs makes it challenging to attribute expenses to business results. A parallel to this phenomenon can be seen in the early days of public cloud adoption, where uncontrolled spending led to inefficiencies. Gartner predicts that over 40% of agentic AI projects may be canceled by 2027, largely due to rising costs and unclear business value.
Traditional budgeting approaches may not effectively manage AI expenditures. A dynamic cost management framework that includes tagging, telemetry, and integration with financial and business planning can offer improved visibility into AI spending. Transparency in costs can guide decision-making and resource allocation, ensuring investments are aligned with high-value projects.
To address financial management effectively, companies may benefit from adopting a structured approach known as Technology Business Management (TBM), which integrates IT financial management, FinOps, and strategic portfolio management. This framework can enhance transparency and decision-making regarding AI investments, ultimately facilitating calculated expenditures and optimizing ROI.
Source: https://venturebeat.com/ai/ais-financial-blind-spot-why-long-term-success-depends-on-cost-transparency