Presented by Apptio, an IBM company
When a technology with disruptive potential arrives on the scene, it’s simple for companies to let enthusiasm overwhelm fiscal discipline. Bean counting may seem shortsighted when faced with exhilarating opportunities for business transformation and competitive dominance. But money is always an object. And when the technology is artificial intelligence, these elements can add up quickly.
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 them – so you can be sure that the potential of artificial intelligence will translate into a real, positive impact on your business.
The AI acceleration paradox
While AI is helping to transform business operations, its own financial footprint often remains unclear. If you can’t tie costs to impact, how can you be sure that your AI investments will deliver meaningful ROI? This uncertainty is not surprising in the Gartner® 2025 study Hype Cycle™ for artificial intelligenceGenAI found itself in the “trough of disillusionment.”
Effective strategic planning depends on transparency. In its absence, decision-making is based on guesswork and hunches. Much depends on these decisions. According to Apptio research, 68% of surveyed technology leaders expect to escalate their AI budgets, and 39% believe that AI will be the largest driver of future budget growth for their departments.
However, larger budgets do not guarantee better results. Gartner® also reveals that “despite average spending on GenAI initiatives in 2024 of $1.9 million, less than 30% of AI leaders say their CEOs are satisfied with the return on investment.” If there is no clear link between costs and outcomes, organizations risk scaling investments without scaling the value they are expected to create.
To operate with legitimate confidence, business leaders in finance, IT and technology must collaborate to gain insight into AI’s financial blind spot.
The hidden financial risks of AI
The runaway costs of artificial intelligence may have IT leaders reminiscing about the early days of the public cloud. When DevOps teams and business units can easily source their own resources based on OpEx, costs and inefficiencies can quickly escalate. In fact, AI projects are voracious consumers of cloud infrastructure, while incurring additional costs for data platforms and engineering resources. And that’s in addition to the tokens used for each query. The decentralized nature of these costs makes them particularly complex to attribute to business outcomes.
As with the cloud, the ease of purchasing AI quickly leads to its proliferation. And tight budgets mean that every dollar spent is an unconscious trade-off with other needs. People fear that artificial intelligence will take their jobs. But it’s equally likely that AI will take over their department’s budget.
Meanwhile, according to Gartner®, “More than 40% of agent-based AI projects will be canceled by the end of 2027 due to rising costs, unclear business value, or inadequate financial controls.” But are these the right projects to cancel? Without a way to tie investment to impact, how can business leaders know whether these rising costs are justified by a proportionately greater return on investment? ?
Without AI cost transparency, companies risk overspending, underperformance, and missing out on better opportunities to drive value.
Why conventional financial planning can’t cope with artificial intelligence
As we’ve learned with cloud, we see that conventional stationary budget models are poorly suited to active workloads and rapid resource scaling. The key to managing cloud costs is tagging and telemetry, which assist companies attribute every dollar spent on cloud to specific business outcomes. Managing AI costs will require similar practices. But the scope of the challenge goes much further. In addition to the costs of storing, computing, and transmitting data, each AI project comes with its own set of requirements, from rapid model optimization and routing to data preparation, regulatory compliance, security, and staffing.
This elaborate mix of constantly changing factors makes it understandable that finance and business teams lack detailed visibility into AI spend, and IT teams struggle to reconcile usage with business outcomes. However, without these connections, it is impossible to precisely and accurately track the return on investment.
The strategic value of cost transparency
Cost transparency enables you to make smarter decisions, from allocating resources to deploying talent.
Connecting specific AI assets to the projects they support helps technology decision-makers ensure that the highest-value projects get what they need to succeed. Setting the right priorities is especially significant when there is a shortage of top talent. If your highly compensated engineers and data scientists are spread across too many engaging but irrelevant pilots, it will be complex to staff the next strategic – and perhaps urgent – inflection point.
FinOps best practices apply equally to AI. Cost analysis can reveal opportunities to optimize infrastructure and address waste, whether by properly matching performance and latency to workload requirements, or by selecting a smaller, more cost-effective model instead of the default state-of-the-art LLM model. As work progresses, tracking can flag rising costs so leaders can quickly pivot in more promising directions when necessary. A project that makes sense at X cost may not be viable at 2X cost.
Companies that take an organized, lucid and well-managed approach to AI costs are more likely to spend the right money in the right way and achieve optimal return on investment.
TBM: An enterprise structure for managing AI costs
AI transparency and cost control depend on three practices:
IT Financial Management (ITFM): Manage IT costs and investments in line with business priorities
FinOps: Optimize cloud costs and ROI through financial responsibility and operational efficiency
Strategic Portfolio Management (SPM): Prioritize and manage projects to better ensure they deliver maximum value to the company
Together, these three disciplines constitute Technology Business Management (TBM), a structured framework that helps technology, business and finance leaders connect technology investments with business outcomes to achieve improved financial visibility and decision-making.
Most companies are already on the TBM journey, whether they realize it or not. They may have adopted some form of FinOps or cloud cost management. They can also develop solid financial knowledge for IT. They may also rely on agile enterprise planning or project management as part of strategic portfolio management to more effectively execute initiatives. Artificial intelligence can draw from and influence all of these areas. By bringing them together under one model and a common vocabulary, TBM provides the necessary transparency into the costs of AI and its possible business impact.
AI success depends on value – not just speed. The cost transparency provided by TBM provides a roadmap that can assist business and IT leaders make the right investments, execute them cost-effectively, scale responsibly, and transform AI from a costly error to a real business asset and strategic enabler.
Sources: Gartner® press release, Gartner® predicts over 40% of agent AI projects will be canceled by the 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-cancelled-by-end-2027
GARTNER® is a registered trademark and service mark of Gartner®, Inc. and/or its affiliates in the U.S. 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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