MAROKO133 Update ai: AI’s financial blind spot: Why long-term success depends on cost tran

📌 MAROKO133 Breaking ai: AI’s financial blind spot: Why long-term success depends

Presented by Apptio, an IBM company

When a technology with revolutionary potential comes on the scene, it’s 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.

AI’s 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 — so you can ensure that the potential of AI translates into real, positive impact for your business.

The AI acceleration paradox

While AI is helping to transform business operations, its own financial footprint often remains obscure. If you can’t 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® Hype Cycle™ for Artificial Intelligence, GenAI has moved into the “Trough of Disillusionment” .

Effective strategic planning depends on clarity. In its absence, decision-making falls back on guesswork and gut instinct. And there’s 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’ biggest driver of future budget growth.

But bigger budgets don’t guarantee better outcomes. Gartner® also reveals that “despite 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.” If there’s no clear link between cost and outcome, organizations risk scaling investments without scaling the value they’re meant to create.

To move forward with well-founded confidence, 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 can give IT leaders flashbacks to the early days of public cloud. When it’s 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 — while incurring additional costs for data platforms and engineering resources. And that’s on top of the tokens used for each query. The decentralized nature of these costs makes them particularly difficult to attribute to business outcomes.

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’s just as likely that AI will take their department’s budget.

Meanwhile, according to Gartner®, “Over 40% of agentic AI projects will be canceled by end of 2027, due to escalating costs, unclear business value or inadequate rish controls”. 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? ?

Without transparency into AI costs, companies risk overspending, under-delivering, and missing out on better opportunities to drive value.

Why traditional financial planning can't handle AI

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 — from prompt optimization and model routing to data preparation, regulatory compliance, security, and personnel.

This complex mix of ever-shifting factors makes it understandable that finance and business teams lack granular visibility into AI-related spend — and IT teams struggle to reconcile usage with business outcomes. But it’s impossible to precisely and accurately track ROI without these connections.

The strategic value of cost transparency

Cost transparency empowers smarter decisions — from resource allocation to talent deployment.

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’ll be hard to staff the next strategic — and perhaps pressing — pivot.

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.

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.

TBM: An enterprise framework for AI cost management

Transparency and control over AI costs depend on three practices:

IT financial management (ITFM): Managing IT costs and investments in alignment with business priorities

FinOps: Optimizing cloud costs and ROI through financial accountability and operational efficiency

Strategic portfolio management (SPM): Prioritizing and managing projects to better 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 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 — and impact — 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.

AI success depends on value — 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.

Sources : Gartner® Press Release, Gartner® Predicts Over 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-percen…