Presented by Apptio, an IBM company
AI spending is growing, but the full impact often remains an open question. Bridging the gap requires clear answers about how AI is regulated, measured, and linked to business outcomes.
ROI uncertainty is not unique to AI: In the Apptio 2026 Technology Investment Management Report, 90% of technology leaders surveyed said that ROI uncertainty has a medium or large impact on overall technology investment decisions, an increase of 5 percent year-over-year. In other words, tech leaders are increasing their reliance on ROI – even if they don’t fully know how to measure it. And AI economics involves new and unexpected costs, further complicating ROI calculations. Faced with increasing uncertainty and expanding budgets, technology leaders need a clear, reliable framework for evaluating AI ROI.
Organizations are increasingly expecting that scaled AI will pay its own way, at least partially. According to Apptio’s Technology Investment Management Report, 45% of organizations surveyed intend to finance innovation by reinvesting savings from AI-powered efficiencies. That model assumes that such savings are both achievable and quantifiable. Meanwhile, two-thirds of organizations planning to reallocate existing budget capital to AI will need clarity on the trade-offs involved.
Like the early days of the public cloud, AI costs and returns are difficult to predict. Pricing varies widely between providers and continues to evolve, while consumption is unpredictable. The pressure to adapt quickly is also tremendous as organizations face the threat of disruption by more agile competitors.
The New Mathematics of AI ROI
Tech leaders must view AI ROI as a matter of optimization, taking into account many variables. At a high level, the implementation of AI initiatives is inevitable. The question is how to achieve the maximum potential returns – both financial and organizational.
Start with the business problem. There are many ways in which AI can have a positive impact, but organizational resources and focus may be limited. Make sure you’re prioritizing the right initiatives by basing your AI investment strategy on quantifiable goals tied to real business outcomes. Are you trying to improve your decision making speed? Increase throughput or capacity? Or pursuing spectacular cases with high potential returns but minimal strategic relevance?
Determine what success looks like. AI can introduce a new capability or enhance an existing capability. For new capabilities, clarify the possibilities you want to unlock, such as new revenue opportunities, workflows, or decision-making processes. For enhancements, establish baseline performance and the expected lift you want to achieve with AI.
Consider how finances will affect your appraisal. Some use cases may show minimal results in the near term but provide significant value in the long term. What is your time frame for return? On the other hand, more successful rollouts with faster adoption may generate unexpectedly high estimate bills. Will this mean pulling the plug – or leaning further? What should your cost and return curve look like over the last few years? As you map out your timeline, establish clear boundaries to determine when you will advance, pause, pause, or accelerate your investments.
Identify the right KPIs. Evaluating the return on AI investment may be even more difficult than the cost. Usage, efficiency and financial impact all matter. But AI success metrics won’t always be straightforward. There may be new usage patterns that you don’t have a way to measure yet. Your technology environment may experience subsequent changes that will require further evaluation. Will you be able to reduce your reliance on other tools, such as reducing seats in your data analytics platform? How do you account for cross-tool pricing comparisons across multiple AI providers with varying rates?
To gain the full context and insight, you also need to take into account the alignment of the initiative with your broader strategy and consider the opportunity cost of investments you might otherwise have made. Remember that you are not evaluating AI business value alone; You are deciding whether this is the best use of your limited capital across all your investments.
These decisions will require a level of insight far beyond that required to justify traditional purchases such as network infrastructure or enterprise software. Tech leaders who understand the complexities of AI economics should consider a new framework for data-driven decision making.
Making AI investments sustainable with TBM
Technology business management (TBM) helps make ROI more concrete and measurable, so it can be relevant to the business. By bringing together IT financial management (ITFM), AI FinOps (cloud financial management for AI workloads), and strategic portfolio management (SPM), a TBM framework connects financial, operational, and business data across the enterprise. This makes it possible to take into account AI value and cost across a wide range of dimensions – and translate hypothetical innovation into board presentations and budget justifications that remain under scrutiny.
TBM can help leaders build a reliable cost base that incorporates AI spend across labor, infrastructure, estimation, storage, and applications. As AI workloads change dynamically, TBM provides visibility into how that spend is distributed across on-premises systems and cloud environments – both of which require different capacity planning for particular skill sets. The framework also links investments to business outcomes, aligning AI initiatives with strategic priorities and measurable outcomes. With increased visibility, you are able to identify issues and make decisions faster, such as catching cost overruns early. Early detection can help determine whether a change in use is worth a change in funding. This integrated view of financial and operational data helps leaders measure what is working and reevaluate what is not working as adoption rates increase. TBM provides essential visibility and context throughout the AI spend management conversation. Even as pricing evolves, tooling changes, and workflows change, you can apply the same analytical approach and understand what’s really working and demonstrate ROI. Leaders who operationalize AI within a TBM framework can:
- Evaluate ROI at both the project and portfolio level
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Detect unexpected cost increases
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Compare multiple AI tools
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Understand ripple effects in the systems that drive business
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Defend investment decisions with confidence
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Understand and manage total cost and usage across the AI investment lifecycle
From theory to practice
Organizations are moving ahead with AI experiments, and we are past the point where these investments can be funded on optimism alone. Amid growing uncertainty and cost sensitivity, boards are asking more strategic questions and finance wants reliable data.
Enterprise leaders who treat AI as a managed investment rather than a bet on innovation will be the ones to scale it successfully. To fund AI responsibly, leaders must establish clarity about scope, outcomes, cost drivers, and readiness. The TBM-driven approach provides the data foundation, visibility, and accountability to make those decisions.
Learn more about how Apptio TBM transforms IT spend management in the AI age here.
Ajay Patel is General Manager at Apptio, an IBM company.
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