Presented by Apptio, an IBM company
AI spending is increasing, but the full impact often remains an open question. Bridging the gap requires clear answers to how AI is managed, measured and linked to business outcomes.
ROI uncertainty is not unique to AI Apptio 2026 Technology Investment Management Reportt, 90% of technology leaders surveyed said ROI uncertainty has a moderate or large impact on overall technology investment decisions, an increase of 5 percent year-over-year. In other words, tech leaders are increasingly relying on ROI – even if they don’t quite know how to measure it. And the AI economy involves new and unexpected costs that make ROI calculations even more difficult. Faced with increasing uncertainty and growing budgets, technology leaders need a clear, reliable framework for evaluating AI ROI.
Organizations increasingly expect AI at scale to at least partially pay its way. According to Apptio’s technology investment management report, 45% of organizations surveyed intend to fund innovation by reinvesting savings from AI-driven efficiencies. This model assumes that such savings are both achievable and quantifiable. Meanwhile, two-thirds of organizations planning to shift existing budget capital to AI need to clarify the tradeoffs involved.
As in the early days of the public cloud, AI costs and revenues are difficult to predict. While consumption is unpredictable, prices vary widely between providers and continue to evolve. As organizations face the threat of disruption by more nimble competitors, the pressure to adopt quickly is also great.
The new math of AI ROI
With so many variables to consider, tech leaders must view AI ROI as a matter of optimization. Implementation of high-level AI initiatives is inevitable. The question is how to achieve the greatest possible return, both financially and organizationally.
Start with a business problem. There are many ways that AI can have a positive impact, but organizational resources and attention may be limited. Make sure you prioritize your AI investment strategy on the right initiatives based on quantifiable goals tied to real business outcomes. Are you trying to speed up your decision-making? Increase throughput or capacity? Or pursue cool side jobs with high potential returns but minimal strategic relevance?
Define what success looks like. AI can introduce a new capability or enhance an existing one. Articulate the opportunities you want to open for new opportunities, such as new revenue opportunities, workflows, or decision-making processes. For augmentation, set the baseline performance you aim to achieve with AI and the expected boost.
Consider how finances will affect your assessment. Some use cases may show minimal results in the short term, but may provide significant value in the long term. What is your turnaround time? On the other hand, more successful launches with rapid adoption may unexpectedly produce high results. Does this mean pulling the plug or bending further? What should your cost and revenue curve look like over the years? When mapping out your timeline, set clear thresholds to determine whether you will continue, stop, stop, or accelerate your investment.
Define the right KPIs. Estimating the returns on investment in AI can be more difficult than the costs. Utilization, efficiency and financial implications are all important. But AI’s success metrics won’t always be straightforward. There may be new use cases that you don’t yet have a way to measure. Your technology environment may experience subsequent changes that require further evaluation. Will you be able to reduce your reliance on other tools such as offloading your data analytics platform? How will you factor in cross-tool price comparisons for multiple AI providers with variable rates?
To gain full context and insight, you should also consider the initiative’s alignment with your broader strategy and consider the opportunity cost of investments you might otherwise make. Remember, you don’t evaluate the value of AI work in isolation; you decide whether all your investments are the best use of finite capital.
These decisions will require a much higher level of insight than is needed to justify traditional purchases such as network infrastructure or enterprise software. Tech leaders exploring the complexities of the AI economy must consider a new framework for data-driven decision-making.
Making AI investment sustainable with TBM
Technology business management (TBM) helps make ROI more concrete and measurable, so it can be relevant to the business. Bringing together IT Financial Management (ITFM), AI FinOps (cloud financial management for AI workloads) and Strategic Portfolio Management (SPM), the TBM framework integrates financial, operational and business information across the enterprise. It allows you to calculate the cost and value of AI across multiple dimensions – and it allows you to turn budgeted assumptions into just a hypothetical board. inspection.
TBM can help leaders build a reliable cost base that captures AI costs across labor, infrastructure, inference, storage and applications. As AI workloads change dynamically, TPM provides visibility into how those costs are distributed between on-premise systems and cloud environments – both of which require different capacity planning for specific skill sets. The framework also links investments to business outcomes, aligning AI initiatives to strategic priorities and measurable outcomes. With increased visibility, you can identify problems and make quick decisions, such as catching cost increases early. Early detection can help determine whether a change of use is eligible for funding. This unified view of financial and operational data helps leaders measure business and reassess what’s failing as adoption grows. TBM provides essential visibility and context across the entire AI cost management conversation. As pricing evolves, tools change, and workflows change, you can apply the same analytical approach to understand what actually works and demonstrate ROI. Leaders using AI within TBM:
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Evaluate ROI at both project and portfolio levels
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Note unexpected cost increases
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Compare multiple AI tools
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Understand ripple effects in business systems
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Defend your investment decisions with confidence
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Understand and manage overall costs and usage throughout the AI investment lifecycle
From theory to practice
Organizations are moving beyond AI experiments, and we’re past the point where these investments can only be funded on optimism. Against a backdrop of increased uncertainty and cost sensitivity, boards are asking more strategic questions and demanding financial certainty.
Enterprise leaders who view artificial innovation as a managed investment rather than a bet on innovation will scale it successfully. To fund AI responsibly, leaders must create clarity around scope, outcomes, cost drivers, and readiness. A TBM-based approach provides the data base, visibility and accountability for those decisions.
Learn more about how Apptio TPM is transforming IT cost management in the era of AI.
Ajay Patel is the General Manager of Apptio, an IBM company.
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