AI’s financial blind spot: Why long-term success depends on cost transparency
Presented by Apptio, an IBM company
When a technology with revolutionary potential arrives on the scene, it’s easy for companies to let enthusiasm override budget discipline. Bean counting can seem shortsighted in the face of exciting opportunities for business transformation and competitive dominance. But money is always an object. And if the technology is AI, those beans can add up quickly.
The value of AI is evident in areas such as operational efficiency, employee productivity and customer satisfaction. However, this comes at a cost. The key to long-term success is understanding the relationship between the two so you can ensure AI’s potential translates into real, positive impact for your business.
The AI acceleration paradox
Although AI helps transform business operations, its own financial footprint often remains unclear. If you can’t match cost to impact, how can you be sure your AI investments will deliver meaningful ROI? This uncertainty makes it no surprise that Gartner® in 2025 Hype Cycle™ for Artificial IntelligenceGenAI has entered the “Trough of Disillusionment”.
Effective strategic planning depends on clarity. Without that, decision-making falls back on guesswork and instinct. And a lot depends on these decisions. According to research from Apptio, 68% of technology leaders surveyed expect to increase their AI budgets, and 39% believe AI will be the biggest driver of future budget growth for their departments.
But bigger budgets don’t guarantee better results. Gartner® also reveals that “despite spending an average of $1.9 million on GenAI initiatives by 2024, less than 30% of AI leaders say their CEOs are satisfied with the return on investment.” Without a clear link between costs and results, organizations risk scaling their investments without scaling the value they need to create.
To move forward with informed confidence, business leaders across finance, IT and technology must work together to understand AI’s financial blind spot.
The hidden financial risks of AI
The runaway costs of AI may give IT leaders flashbacks to the early days of the public cloud. When it is easy for DevOps teams and business units to purchase their own resources on an OpEx basis, costs and inefficiencies can quickly add up. In fact, AI projects are avid consumers of cloud infrastructure, while incurring additional costs for data platforms and technical resources. And that’s in addition to the tokens used for each search. The decentralized nature of these costs makes them particularly difficult to attribute to business results.
As with the cloud, the ease of AI purchasing is quickly leading to AI proliferation. And finite budgets mean that every dollar spent represents an unconscious trade-off with other needs. People are afraid that AI will take their jobs. But it is just as likely that AI will take over their department’s budget.
Meanwhile, according to Gartner®, “more than 40% of agentic AI projects will be canceled by the end of 2027 due to rising costs, unclear business value, or inadequate risk controls.” But are these the right projects to cancel? Absent a way to link investments to impact, how can business leaders know whether those rising costs are justified by a proportionately greater ROI? ?
Without transparency about the costs of AI, companies risk overspending, underdelivering and missing better opportunities to drive value.
Why traditional financial planning can’t handle AI
As we’ve learned with the cloud, we see that traditional static budget models are poorly suited to dynamic workloads and rapid resource scaling. The key to cloud cost management is tagging and telemetry, which allows companies to attribute every dollar of cloud spend to specific business outcomes. AI cost management requires similar practices. But the scope of the challenge goes much further. In addition to the costs of storage, computing power and data transfer, every AI project comes with its own requirements: from rapid optimization and model routing to data preparation, regulatory compliance, security and personnel.
This complex mix of ever-changing factors makes it understandable that finance and business teams lack detailed visibility into AI-related spend – and that IT teams struggle to reconcile usage with business results. But without these connections, it’s impossible to accurately and accurately track ROI.
The strategic value of cost transparency
Cost transparency enables smarter decisions – from resource allocation to talent deployment.
By connecting specific AI assets to the projects they support, technology decision makers can ensure the most valuable projects get what they need to succeed. Setting the right priorities is especially crucial when top talent is scarce. If your highly paid engineers and data scientists are spread across too many interesting but non-essential pilots, it will be difficult to staff the next strategic – and perhaps urgent – pivot.
FinOps best practices apply to AI as well. Cost insights can reveal opportunities to optimize infrastructure and address waste, for example by adjusting performance and latency to meet workload requirements, or by selecting a smaller, more cost-effective model rather than defaulting to the latest major language model (LLM). As work progresses, tracking can flag rising costs so leaders can quickly choose a direction that is more promising if necessary. A project that makes sense at X cost may not be worth doing at 2X cost.
Companies that take a structured, transparent and well-managed approach to AI costs are more likely to spend the right money in the right way and get optimal ROI from their investment.
TBM: An enterprise framework for AI cost management
Transparency and control over AI costs depend on three practices:
IT Financial Management (ITFM): Manage IT costs and investments in line with business priorities
FinOps: Optimizing cloud costs and ROI through financial accountability and operational efficiency
Strategic Portfolio Management (SPM): Prioritizing and managing projects to better ensure they deliver maximum value to the business
Collectively, these three disciplines form Technology Business Management (TBM) – a structured framework that helps technology, business and finance leaders connect technology investments with business outcomes for better financial transparency and decision-making.
Most companies are already on their way to TPM, whether they realize it or not. They may have adopted some form of FinOps or cloud cost management. Or perhaps they develop strong financial expertise for IT. Or they can rely on Enterprise Agile Planning or Strategic Portfolio Management project management to deliver initiatives more successfully. AI can tap into and impact all of these areas. By uniting them under one umbrella with a common model and vocabulary, TBM brings essential clarity to AI costs and the business impact they enable.
The success of AI depends on value – not just speed. The cost transparency that TBM provides provides a roadmap that can help business and IT leaders make the right investments, execute them cost-effectively, scale them responsibly, and turn AI from a costly mistake into a measurable asset and strategic driver.
Sources: Gartner® press release, Gartner® predicts more than 40% of Agentic AI projects will be canceled by end of 2027, June 25, 2025 https://www.Gartner®.com/en/newsroom/press-releases/2025-06-25-Gartner®-predicts-over-40-procent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
GARTNER® is a registered trademark and service mark of Gartner®, Inc. and/or its affiliates in the US and internationally and is used herein with permission. All rights reserved.
Ajay Patel is General Manager of Apptio and IT Automation at IBM.
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