Presented by Apptio, an IBM company
When a technology with revolutionary potential emerges, it becomes easy for companies to put excitement ahead of fiscal discipline. Bean counting may seem short-sighted in the face of exciting opportunities for business transformation and competitive dominance. But money is always a commodity. And when the technology is AI, those beans can add up faster.
The value of AI is becoming apparent in areas such as operational efficiency, employee productivity, and customer satisfaction. However, this comes at a cost. The key to long-term success is to understand the relationship between the two – so you can ensure that AI’s potential translates into real, positive impact for your business.
AI acceleration paradox
While AI is helping to transform business operations, its own financial footprint often remains unclear. If you can’t link cost to impact, how can you be sure that your AI investment will bring meaningful ROI? This uncertainty is not surprising given that Gartner® Hype Cycle™ for Artificial IntelligenceGenAI has gone into the “trough of disillusionment”.
Effective strategic planning depends on clarity. In its absence, decision making becomes dependent on guesswork and instinct. And a lot depends on these decisions. According to Apptio research, 68% of technology leaders surveyed expect their AI budgets to increase, and 39% believe AI will be the biggest driver of their departments’ future budget growth.
But bigger budgets don’t guarantee better results. Gartner® also revealed that “despite spending an average of $1.9 million on GenAI initiatives in 2024, less than 30% of AI leaders say their CEOs are satisfied with the return on investment.” If there is no clear relationship between costs and results, organizations risk increasing investments without increasing the value they should be creating.
To move forward with well-founded confidence, business leaders in finance, IT and technology must collaborate to gain visibility into the financial blind spot of AI.
Hidden financial risks of AI
The uncontrolled costs of AI may remind IT leaders of the early days of the public cloud. When it becomes easier for DevOps teams and business units to purchase their own resources on an OpEx basis, costs and inefficiencies can add up quickly. In fact, AI projects are avid consumers of cloud infrastructure – while incurring additional costs for data platforms and engineering resources. And this is on top of the token used for each query. The decentralized nature of these costs makes it particularly difficult to attribute them to business results.
Like the cloud, the ease of AI procurement makes AI diffusion faster. And limited budgets mean that every dollar spent represents an unconscious compromise with other needs. People are worried that AI will take their jobs. But there’s a good chance 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 insufficiently rigorous controls”. But are these the right projects to cancel? In the absence of a way to link investments to impact, how can business leaders know whether increasing costs are justified by a proportionately greater ROI? ,
Without transparency into AI costs, companies risk overspending, under-delivering, and missing out on better opportunities to drive value.
Why can’t traditional financial planning handle AI?
As we learned with the cloud, we see that traditional static budget models are poorly suited for dynamic workloads and rapidly growing resources. The key to cloud cost management has been tagging and telemetry, which helps companies attribute each dollar of cloud spend to specific business outcomes. AI cost management will require similar practices. But the scope of the challenge goes much further. In addition to the costs of storage, compute and data transfer, each AI project brings its own set of requirements – from quick 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 IT teams struggle to reconcile usage with business outcomes. But without these connections it is impossible to track ROI accurately and precisely.
Strategic Value of Cost Transparency
Cost transparency empowers better decisions – from resource allocation to talent deployment.
Linking specific AI resources with the projects they support helps technology decision makers ensure that the most high-value projects are given what they need to succeed. Setting the right priorities is especially important when there is a shortage of top talent. If your highly paid engineers and data scientists are spread across too many interesting but unnecessary pilots, it will be hard to staff the next strategic – and perhaps stressful – pivot.
FinOps best practices apply equally to AI. Cost insights can uncover opportunities to optimize infrastructure and address waste, whether by right-sizing performance and latency to match the workload requirements, or by selecting a smaller, more cost-effective model rather than defaulting to the latest large language model (LLM). As work progresses, tracking can flag rising costs so that leaders can move quickly in more promising directions as needed. A project that makes sense at X cost may not make sense at 2X the cost.
Companies that take a structured, transparent and streamlined approach to AI costs are more likely to spend the right money in the right way and see optimal ROI from their investment.
TBM: An Enterprise Framework for AI Cost Management
Transparency and control over AI costs depends on three practices:
IT Financial Management (ITFM): Managing 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): Better prioritizing and managing projects to ensure they deliver maximum value for the business
Collectively, these three disciplines make up technology business management (TBM) – a structured framework that helps technology, business and finance leaders connect technology investments to business outcomes for better financial transparency and decision making.
Most companies are already on the TBM path, whether they realize it or not. They may have adopted some form of FinOps or cloud cost management. Or they may be developing strong financial expertise for IT. Or they may rely on enterprise agile planning or strategic portfolio management project management to deliver initiatives more successfully. AI can draw on and influence all of these areas. By uniting them under one umbrella with a common model and terminology, TBM brings needed clarity into AI costs and the business impact they enable.
AI success depends on value – not just velocity. The cost transparency that TBM provides provides a road map that can help business and IT leaders make the right investments, deliver them cost effectively, scale them responsibly, and transform AI from a costly mistake into a measurable business asset and strategic driver.
Source: Gartner® Press Release, Gartner® predicts more than 40% of agentic AI projects will be canceled by the end of 2027, June 25, 2025
GARTNER® is a subsidiary of Gartner®, Inc. in the US and internationally. and/or its affiliates 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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