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When a potentially transformative technology emerges, companies tend to let enthusiasm overtake financial discipline. Bean counting may seem short-sighted in the face of exciting opportunities for business transformation and competitive advantage. But money is always an object. And if the technology is AI, those beans could multiply rapidly.
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 understanding the relationship between the two. By doing so, you can ensure that the potential of AI has a real positive impact on your business.
AI acceleration paradox
Although AI is helping to transform business operations, the financial impact of AI itself remains unclear. If you can’t link costs and benefits, how can you be sure that your AI investments will deliver a meaningful ROI? With this uncertainty, it’s no surprise that GenAI has entered the “trough of disillusionment” in the 2025 Gartner® Hype Cycle™ for Artificial Intelligence.
Effective strategic planning relies on clarity. Without it, decision-making relies on guesswork and intuition. And many things influence these decisions. According to Apptio research, 68% of technology leaders surveyed expect AI budgets to increase, and 39% believe AI will be the biggest driver of future budget growth for their departments.
However, a larger budget does not guarantee better results. Gartner® also found that “less than 30% of AI leaders say their CEOs are satisfied with their return on investment, even though the average spend on GenAI initiatives in 2024 is $1.9 million.” Without a clear link between costs and outcomes, organizations risk expanding their investments without expanding the value they intend to create.
To move forward with well-founded confidence, business leaders in finance, IT, and technology must work together to visualize AI’s financial blind spots.
AI’s hidden economic risks
The runaway costs of AI may remind IT leaders of the early days of the public cloud. As it becomes easier for DevOps teams and business units to procure their own resources on an OpEx basis, costs and inefficiencies can quickly escalate. In fact, AI projects are eagerly leveraging cloud infrastructure, but at the same time incurring additional costs for data platforms and engineering resources. And it will be added to the token used for each query. These costs are decentralized, making them particularly difficult to attribute to business outcomes.
Similar to the cloud, the proliferation of AI will occur as soon as it becomes easier to procure. And a finite budget means that every dollar spent represents an unconscious trade-off with other needs. People are worried that AI will take their jobs. But AI has just as much potential to take away departmental budgets.
Meanwhile, according to Gartner®, “more than 40% of agent AI projects will be canceled by the end of 2027 due to rising costs, unclear business value, or poor risk management.” But is it right to cancel those projects? Without a way to link investment to impact, how can business leaders determine whether these increased costs are justified by a proportionately larger ROI?
Without transparency into AI costs, companies risk overspending, under-delivering, and missing out on better opportunities to drive value.
Why traditional financial planning is not AI-ready
As we learned about the cloud, we know that traditional static budget models are poorly suited for dynamic workloads and rapidly expanding resources. Tagging and telemetry are key to cloud cost management, helping businesses attribute each dollar of cloud spending to specific business outcomes. AI cost management will require similar practices. However, the scope of the challenge extends further. In addition to storage, compute, and data transfer costs, each AI project comes with its own set of requirements, from rapid optimization and model routing to data preparation, regulatory compliance, security, and personnel.
With a complex mix of ever-changing factors, it’s no surprise that finance and business teams lack detailed visibility into AI-related spending, and IT teams struggle to align usage with business outcomes. But without these connections, it’s impossible to accurately and precisely track ROI.
The strategic value of cost transparency
Cost transparency enables smarter decisions, from resource allocation to staffing.
By connecting specific AI resources with the projects they support, technology decision makers can ensure that the most valuable projects have what they need to succeed. Setting the right priorities is especially important when there is a shortage of top talent. If your highly compensated engineers and data scientists are spread too thin among interesting but non-essential pilots, it will be difficult to staff the next strategic and perhaps impending pivot.
FinOps best practices apply equally to AI. Cost insights reveal opportunities to optimize your infrastructure and address waste by right-sizing performance and latency to suit your workload requirements, or choosing smaller, more cost-effective models instead of defaulting to modern large-scale language models (LLMs). As work progresses, tracking cost increases can help leaders quickly pivot in a more promising direction if needed. A project that makes sense at X cost may be worthless at twice the 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 the best ROI from their investment.
TBM: An enterprise framework for AI cost management
Transparency and control of AI costs relies on three practices:
IT Financial Management (ITFM): Manage IT costs and investments to align with business priorities
FinOps: Optimize cloud costs and ROI through financial responsibility and operational efficiency
Strategic Portfolio Management (SPM): Prioritize and manage projects to ensure they deliver the most value to your business
Together, these three disciplines make up Technology Business Management (TBM). TBM is a structured framework that helps technology, business, and finance leaders connect technology investments to business outcomes and improve financial transparency and decision-making.
Most companies are already on the TBM path, whether they realize it or not. You may have adopted some form of FinOps or cloud cost management. Or perhaps you have developed strong financial expertise in IT. Alternatively, you can leverage enterprise agile planning and strategic portfolio management project management to make your initiatives more successful. AI can leverage and impact all of these areas. TBM inherently clarifies AI costs and the business impact they enable by using a common model and vocabulary to unify them under one umbrella.
The success of AI is determined by value, not just speed. The cost transparency provided by TBM provides a roadmap to help business and IT leaders make the right investments, deliver cost-effectively, scale 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 end 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-end-of-2027
GARTNER® is a registered trademark and service mark of Gartner®, Inc. and/or its affiliates in the United States and internationally and is used herein with permission. Unauthorized reproduction is prohibited.
Ajay Patel is General Manager of Apptio and IT Automation at IBM.
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