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When a technology with revolutionary potential involves market, it's easy for corporations to permit enthusiasm to outweigh financial discipline. Given the exciting opportunities for business transformation and competitive dominance, counting all the pieces can seem short-sighted. But money is at all times an object. And when the technology is AI, these results can add up quickly.
The value of AI is clear in areas reminiscent of operational efficiency, worker productivity and customer satisfaction. However, this comes with costs. The key to long-term success is knowing the connection between the 2 – so you may be sure that AI's potential translates into real, positive impact for your enterprise.
The AI acceleration paradox
While AI helps transform business operations, its own financial footprint often stays unclear. If you may't make the connection between cost and impact, how are you going to be certain your AI investments will deliver meaningful ROI? This uncertainty will not be surprising that in 2025 Gartner® Hype Cycle™ for artificial intelligenceGenAI is within the “valley of disillusionment”.
Effective strategic planning depends upon clarity. If it’s missing, decision-making falls back on assumptions and gut feeling. And quite a bit depends upon these decisions. According to research from Apptio, 68% of technology leaders surveyed expect their AI budgets to extend, and 39% consider AI can be the most important driver of their departments' future budget growth.
But larger budgets don't guarantee higher results. Gartner® also reveals that “despite a mean spending of $1.9 million on GenAI initiatives in 2024, lower than 30% of AI executives say their CEOs are satisfied with the return on investment.” When there is no such thing as a clear connection between costs and results, corporations risk scaling their investments without scaling the worth they’re intended to create.
To move forward with informed confidence, finance, IT and technology leaders must work together to realize insight into AI's financial blind spot.
The hidden financial risks of AI
The rising cost of AI may remind IT leaders of the early days of the general public cloud. When it's easy for DevOps teams and business units to obtain their very own resources on an OpEx basis, costs and inefficiencies can quickly escalate. In fact, AI projects are keen users of cloud infrastructure – while incurring additional costs for data platforms and technical resources. And that is along with the tokens used for every query. The decentralized nature of those costs makes it particularly difficult to attribute them to business results.
As with the cloud, the benefit of AI procurement is quickly resulting in AI proliferation. And limited budgets mean that each dollar spent represents an unconscious trade-off with other needs. People fear that AI will take their jobs. But it's just as likely that AI will drain her department's budget.
Meanwhile, in response to Gartner®, “over 40% of agent AI projects can be canceled by the tip of 2027 because of rising costs, unclear business value, or inadequate risk controls.” But are these the proper projects to cancel? Since there is no such thing as a solution to associate investments with impact, how can business leaders know whether these increasing costs are justified by a proportionally higher ROI? ?
Without visibility into AI costs, corporations risk overspending, underdelivering, and missing higher opportunities to create value.
Why traditional financial planning can't handle AI
As we've learned with the cloud, we see that traditional static budget models are ill-suited to dynamic workloads and rapidly scaling resources. The key to cloud cost management is tagging and telemetry, which helps corporations map every dollar of cloud spending to specific business outcomes. Similar approaches are required for AI cost management. But the scope of the challenge goes much further. In addition to the prices of storage, computing power and data transfer, each AI project comes with its own requirements – from timely optimization and model forwarding to data preparation, regulatory compliance, security and staffing.
This complex mixture of ever-changing aspects makes it comprehensible that finance and business teams lack granular visibility into AI-related spending – and IT teams struggle to align usage with business outcomes. However, without these connections, it’s unattainable to trace ROI precisely and accurately.
The strategic value of cost transparency
Cost transparency enables smarter decisions – from resource allocation to talent deployment.
By linking specific AI resources to the projects they support, technology decision makers can be sure that the most precious projects get what they should succeed. When there’s a shortage of top talent, it is especially vital to set the proper priorities. If your highly paid engineers and data scientists are spread across too many interesting but non-essential pilots, it’ll be difficult to staff the following strategic – and maybe urgent – pivot.
FinOps best practices apply equally to AI. Cost insights can reveal opportunities to optimize infrastructure and eliminate waste, whether by right-sizing performance and latency to match workload requirements or by choosing a smaller, less expensive model fairly than defaulting to the most recent large language model (LLM). As work progresses, tracking can show increasing costs, allowing leaders to quickly move in additional promising directions if mandatory. A project that is sensible at X cost will not be value it at twice the price.
Companies that take a structured, transparent and well-managed approach to AI costs usually tend to spend the proper money in the proper way and achieve optimal ROI on their investment.
TBM: An enterprise framework for AI cost management
Transparency and control over AI costs rely upon three practices:
IT financial management (ITFM): Manage IT costs and investments in alignment with business priorities
FinOps: Optimize cloud costs and ROI through financial accountability and operational efficiency
Strategic Portfolio Management (SPM): Prioritize and manage projects to make sure they deliver the best possible value to the business
Together, these three disciplines form Technology Business Management (TBM) – a structured framework that helps technology, business and finance leaders link technology investments to business results for higher financial visibility and decision-making.
Most corporations are already on the trail to TBM, whether or not they know it or not. They can have adopted some type of FinOps or cloud cost management. Or they develop strong financial expertise for IT. Or they depend on project management Enterprise Agile Planning or Strategic Portfolio Management to implement initiatives more successfully. AI can access and influence all of those areas. By bringing them under one roof with a standard model and vocabulary, TBM provides essential clarity on AI costs and associated business impacts.
The success of AI depends upon value – not only speed. The cost visibility TBM provides provides a roadmap that may help business and IT leaders make the proper investments, deploy them cost-effectively, scale them responsibly, and transform AI from a costly mistake right into a measurable business value and strategic driver.
Sources: Gartner® Press Release, Gartner® predicts over 40% of Agentic AI projects can be canceled by the tip of 2027, June 25, 2025 https://www.Gartner®.com/en/newsroom/press-releases/2025-06-25-Gartner®-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-ende-of-2027
GARTNER® is a registered trademark and repair mark of Gartner®, Inc. and/or its affiliates within the United States and internationally and is used herein with permission. All rights reserved.
Ajay Patel is General Manager, Apptio and IT Automation at IBM.
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