Presented by Apptio, an IBM company
When a technology with revolutionary potential like AI emerges, it’s easy for companies to let enthusiasm outrun fiscal discipline. In the race to transform operations and outpace competitors, cost control can feel like a distraction. But with AI, costs can escalate quickly — so financial discipline remains essential.
Long-term success depends on one thing: understanding the link between AI’s value and its true cost, so its promise translates into measurable business impact.
The hidden financial risks of AI
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?
Gaining visibility into AI’s financial blind spot is especially urgent given the breakneck speed of AI investment. 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. The decentralized nature of spend across cloud infrastructure, data platforms, engineering resources, and query tokens makes it difficult to attribute costs to business outcomes. And because budgets are finite, every dollar spent represents an unconscious tradeoff with other strategic priorities.
Without transparency into AI costs, companies risk overspending, under-delivering, and missing out on better opportunities to drive value.
Why traditional financial planning falls short for 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 the same discipline, but on a broader scale.
On top of costs for storage, compute, and data transfer, each AI project brings its own requirements. These range from prompt optimization and model routing to data preparation, regulatory compliance, governance, security, and personnel.
This complexity leaves finance and IT teams struggling to reconcile AI-related spend with business outcomes — but without these connections, it’s impossible to measure ROI.
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. Businesses can use cost insights to optimize infrastructure and address waste — such as ensuring teams aren’t provisioning higher performance or lower latency than a given workload really needs or paying for a huge LLM when a smaller model would suffice.
As work proceeds, tracking can flag rising costs so leaders can pivot quickly in more-promising directions. 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
Technology Business Management (TBM) provides the foundation for AI cost transparency. It brings together three practices — IT Financial Management (ITFM), FinOps, and Strategic Portfolio Management (SPM) — to align technology investments with business outcomes.
IT financial management (ITFM): ITFM focuses on managing IT finances in alignment with business priorities. ITFM teams analyze comprehensive data on IT costs and investments to track spending against budgets and forecasts, trim excess spending, and ensure financial transparency.
The insights that ITFM teams gain can help businesses form more-strategic partnerships between IT and the business. Collaboration with IT leaders can help business leaders understand how to best meet their technology needs, adjust expenses and behaviors for budget, and keep a data-driven eye on business impact.
FinOps: The goal of FinOps is to help optimize cloud costs and ROI through financial accountability and operational efficiency. FinOps teams work with management, financial, and engineering stakeholders to understand the interplay between the applications being built, the cloud resources that power them, their cost, and the value they generate.
While FinOps has traditionally operated as a reactive function — identifying waste and optimization opportunities in the production environment — the practice is becoming more proactive. Providing engineers with cost insights and guardrails before deployment helps them make the best decisions about cloud resources from the start, rather than navigating a growing list of issues post-launch.
Strategic portfolio management (SPM): SPM helps leaders ensure that investments in people and technology — like AI initiatives — are aligned with the company’s changing strategic needs. Holistic visibility and insights into organization-wide portfolios, programs, and processes show leaders which initiatives deliver value, where and how to apply course corrections, and when to reallocate budget and resources.
SPM encompasses the entire project lifecycle, including strategic planning and alignment, scenario modeling, capacity and resource management, and financial analysis. Its overarching goal is to move more quickly from insights to action, helping organizations respond with agility to changing conditions or opportunities.
By uniting the three practice areas into a structured framework, TBM enables technology, business, and finance leaders to 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 SPM project management to deliver initiatives more successfully. AI draws on — and impacts — all of these areas. By unifying them under one umbrella with a common model and vocabulary, TBM brings essential clarity to the cost of AI investments 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 helps business and IT leaders make the right investments, deliver them cost-effectively, scale them responsibly, and turn AI from a risky bet into a measurable business asset and strategic driver. Whether you begin with ITFM, FinOps, or SPM, each practice can be a path toward TBM — and together they create a clear roadmap to AI value.
Ajay Patel is General Manager, Apptio and IT Automation at IBM.
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