Presented by Apptio, an IBM company
When a technology with revolutionary potential comes on the scene, it’s easy for companies to let the financial discipline dampen the excitement. Bean’s count falls short in the face of exciting opportunities for business transformation and competitive dominance. But money is always an object. And when the tech is AI, those beans can add up quickly.
The value of AI in areas such as operational efficiency, worker productivity, and customer satisfaction is becoming evident. However, this comes at a price. The key to long-term success is understanding the relationship between the two—so you can ensure that AI’s potential translates into real, positive impact on your business.
The AI Acceleration Paradox
While AI is helping to transform business operations, its own financial footprint is often unclear. If you can’t connect costs to impact, how can you be sure your AI investments will drive meaningful ROI? Given this uncertainty, it’s no surprise that in 2025 Gartner HypeCycle™ for Artificial IntelligenceGenai has gone into the “trough of despair”.
Effective strategic planning depends on clarity. In its absence, decision-making reverts to guesswork and gut instinct. And a lot rides on those decisions. According to Aptiv Research, 68% of technology leaders surveyed in the survey expect to increase their AI budgets, and 39% believe that AI will be the biggest driver of future budget growth for their departments.
But bigger budgets do not guarantee better results. Gartner® also revealed that “despite spending an average of $1.9 million on AI initiatives in 2024, fewer than 30 percent of AI leaders say their CEOs are satisfied with the return on investment.” If there is no clear link between costs and results, organizations risk scaling investments without increasing the value they are meant to create.
To move forward with good faith, business leaders in finance, IT, and tech must collaborate to gain visibility into AI’s financial blind spot.
The hidden financial risks of AI
The runaway costs of AI may give leaders flashbacks to the early days of the public cloud. When it’s easy for DevOps teams and business units to purchase their resources based on opex, costs and inefficiencies can spiral quickly. Indeed, AI projects are eager users of cloud infrastructure—while incurring additional costs for data platforms and engineering resources. And this is on top of the token used for each question. The decentralized nature of these costs makes it particularly difficult to attribute them to business outcomes.
As with the cloud, the ease of procuring AI leads to rapid AI proliferation. And limited budgets mean that every 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 take over his department’s budget.
Meanwhile, according to Gartner, “more than 40 percent of agent AI projects will be canceled by the end of 2027, due to cost overruns, unclear business value or insufficient RISH controls”. But are they the right plans to cancel? Lacking a way to link investments to impact, how can business leaders know if these increased costs are justified by a proportionally higher ROI? ?
Without transparency in AI spending, companies miss out on higher cost, lower supply, and better opportunities to drive value.
Why Traditional Financial Planning Can’t Handle AI
As we learned with the cloud, we see that traditional static budgeting models are poorly suited for dynamic workloads and rapidly scaling resources. Key to cloud cost management has been tagging and telemetry, which help companies attribute each dollar of cloud spend to specific business outcomes. AI cost management will require similar approaches. But the scope of the challenge goes much further. On top of storage, compute, and data transfer costs, each AI project brings its own set of requirements—from instant 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 granular visibility into AI-related spending—and IT teams struggle to reconcile usage with business outcomes. But without these connections it is impossible to accurately and accurately track ROI.
The strategic value of cost transparency
Cost transparency empowers better decisions – from resource allocation to talent deployment.
Integrating specific AI resources with the projects they support helps technology decision makers ensure the highest value projects are needed for their success. Setting the right priorities is especially important when the supply of top talent is in short supply. If your highly paid engineers and data scientists are spread across too many interesting but unnecessary pilots, it will be difficult to staff the next strategic — and perhaps pressing — axis.
FINOPs best practices apply equally to AI. Cost insight can come at the level of opportunities to optimize infrastructure and address waste, whether by right-sizing performance and latency to meet workload needs, or by choosing a smaller, more cost-effective model instead of defaulting to the latest large language model (LLM). As work progresses, tracking can flag rising costs so leaders can pivot quickly in more critical directions as needed. A project that makes sense at x cost may not make sense at 2x cost.
Companies that adopt a systematic, transparent and well-governed approach to AI spending are more likely to spend the right amount of money in the right way and see the maximum ROI from their investment.
TBM: An Enterprise Framework for AI Cost Management
Transparency and control over AI spending depends on three approaches:
IT Financial Management (ITFM): Managing IT costs and investments in alignment with business priorities
FINOPS: Optimizing cloud spending and ROI through financial accountability and operational efficiency
Strategic Portfolio Management (SPM): Prioritize and manage projects better to ensure they deliver maximum value to the business
Collectively, these three disciplines form Technology Business Management (TBM)—a systematic 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 PhenoPs or cloud cost management. Or they are developing strong financial skills for it. Or they may rely on enterprise agile planning or strategic portfolio management to deliver project management more successfully. AI can draw on all of these areas – and make an impact. By unifying them under one umbrella with a common model and vocabulary, TBMAI provides essential clarity on costs and the business impact they enable.
AI success depends on value – not just speed. The cost transparency that TBM provides offers a roadmap 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.
Sources: Gartner Press Release Gartner® predicts that more than 40 percent of agent AI projects will be canceled by the end of 2027, June 25, 2025.
Gartner® is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the US and internationally and is used with permission. All rights reserved.
Ajay Patel is General Manager, Adaptive and IT Automation at IBM.
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