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Presented by Apptio, an IBM company<\/i><\/p>\n
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When a technology with revolutionary potential comes on the scene, it\u2019s easy for companies to let enthusiasm outpace fiscal discipline. Bean counting can seem short-sighted 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 fast.<\/p>\n
AI\u2019s value is becoming evident in areas like operational efficiency, worker productivity, and customer satisfaction. However, this comes at a cost. The key to long-term success is understanding the relationship between the two \u2014 so you can ensure that the potential of AI translates into real, positive impact for your business. <\/p>\n
While AI is helping to transform business operations, its own financial footprint often remains obscure. If you can\u2019t connect costs to impact, how can you be sure your AI investments will drive meaningful ROI? This uncertainty makes it no surprise that in the 2025 Gartner\u00ae Hype Cycle\u2122 for Artificial Intelligence, GenAI has moved into the \u201cTrough of Disillusionment\u201d . <\/p>\n
Effective strategic planning depends on clarity. In its absence, decision-making falls back on guesswork and gut instinct. And there\u2019s a lot riding on these decisions. According to Apptio research, 68% of technology leaders surveyed expect to increase their AI budgets, and 39% believe AI will be their departments\u2019 biggest driver of future budget growth. <\/p>\n
But bigger budgets don\u2019t guarantee better outcomes. Gartner\u00ae also reveals that \u201cdespite an average spend of $1.9 million on GenAI initiatives in 2024, fewer than 30% of AI leaders say their CEOs are satisfied with the return on investment.\u201d If there\u2019s no clear link between cost and outcome, organizations risk scaling investments without scaling the value they\u2019re meant to create.<\/p>\n
To move forward with well-founded confidence, business leaders in finance, IT, and tech must collaborate to gain visibility into AI\u2019s financial blind spot.<\/p>\n
The runaway costs of AI can give IT leaders flashbacks to the early days of public cloud. When it\u2019s easy for DevOps teams and business units to procure their own resources on an OpEx basis, costs and inefficiencies can quickly spiral. In fact, AI projects are avid consumers of cloud infrastructure \u2014 while incurring additional costs for data platforms and engineering resources. And that\u2019s on top of the tokens used for each query. The decentralized nature of these costs makes them particularly difficult to attribute to business outcomes. <\/p>\n
As with the cloud, the ease of AI procurement quickly leads to AI sprawl. And finite budgets mean that every dollar spent represents an unconscious tradeoff with other needs. People worry that AI will take their job. But it\u2019s just as likely that AI will take their department\u2019s budget. <\/p>\n
Meanwhile, according to Gartner\u00ae, \u201cOver 40% of agentic AI projects will be canceled by end of 2027, due to escalating costs, unclear business value or inadequate rish controls\u201d. But are those the right projects to cancel? Lacking a way to connect investment to impact, how can business leaders know whether those rising costs are justified by proportionally greater ROI? ? <\/p>\n
Without transparency into AI costs, companies risk overspending, under-delivering, and missing out on better opportunities to drive value. <\/p>\n
As we learned with cloud, we see that traditional static budget models are poorly suited for dynamic workloads and rapidly scaling resources. The 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 practices. But the scope of the challenge goes much further. On top of costs for storage, compute, and data transfer, each AI project brings its own set of requirements \u2014 from prompt optimization and model routing to data preparation, regulatory compliance, security, and personnel.<\/p>\n
This complex mix of ever-shifting factors makes it understandable that finance and business teams lack granular visibility into AI-related spend \u2014 and IT teams struggle to reconcile usage with business outcomes. But it\u2019s impossible to precisely and accurately track ROI without these connections.<\/p>\n
Cost transparency empowers smarter decisions \u2014 from resource allocation to talent deployment. <\/p>\n
Connecting specific AI resources with the projects that 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 critical when top talent is in short supply. If your highly compensated engineers and data scientists are spread across too many interesting but unessential pilots, it\u2019ll be hard to staff the next strategic \u2014 and perhaps pressing \u2014 pivot.<\/p>\n
FinOps best practices apply equally to AI. Cost insights can surface opportunities to optimize infrastructure and address waste whether by right-sizing performance and latency to match workload requirements, or by selecting a smaller, more cost-effective model instead of defaulting to the latest large language model (LLM). As work proceeds, tracking can flag rising costs so leaders can pivot quickly in more-promising directions as needed. A project that makes sense at X cost might not be worthwhile at 2X cost. <\/p>\n
Companies that adopt a structured, transparent, and well-governed approach to AI costs are more likely to spend the right money in the right ways and see optimal ROI from their investment. <\/p>\n
Transparency and control over AI costs depend on three practices:<\/p>\n
IT financial management (ITFM):<\/b> Managing IT costs and investments in alignment with business priorities<\/p>\n
FinOps:<\/b> Optimizing cloud costs and ROI through financial accountability and operational efficiency <\/p>\n
Strategic portfolio management (SPM):<\/b> Prioritizing and managing projects to better ensure they deliver maximum value for the business<\/p>\n
Collectively, these three disciplines make up Technology Business Management (TBM) \u2014 a structured framework that helps technology, business, and finance leaders connect technology investments to business outcomes for better financial transparency and decision-making. <\/p>\n
Most companies are already on the road to TBM, whether they realize it or not. They may have adopted some form of FinOps or cloud cost management. Or they might 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 \u2014 and impact \u2014 all of these areas. By unifying them under one umbrella with a common model and vocabulary, TBM brings essential clarity to AI costs and the business impact they enable.<\/p>\n
AI success depends on value \u2014 not just velocity. The cost transparency that TBM provides offers a road map that can help business and IT leaders make the right investments, deliver them cost-effectively, scale them responsibly, and turn AI from a costly mistake into a measurable business asset and strategic driver. <\/p>\n
Sources : Gartner\u00ae Press Release, Gartner\u00ae Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 25, 2025 <\/i>https:\/\/www.Gartner\u00ae.com\/en\/newsroom\/press-releases\/2025-06-25-Gartner\u00ae-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027<\/i> <\/i><\/p>\n
GARTNER\u00ae is a registered trademark and service mark of Gartner\u00ae, Inc. and\/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.<\/i><\/p>\n
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Ajay Patel is General Manager, Apptio and IT Automation at IBM.<\/i><\/p>\n
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