Presented by Apptio, an IBM company
AI spending is surging, but the full impact often remains an open question. Closing the gap requires clear answers to how AI is governed, measured, and tied to business outcomes.
ROI uncertainty isn’t unique to AI: In the Apptio 2026 Technology Investment Management Report, 90% of technology leaders surveyed said that ROI uncertainty has a moderate or major impact on overall tech investment decisions, a 5-percentage point year-over-year increase. 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 unpredictable costs, further complicating ROI calculations. Faced with increasing uncertainty and increasing budgets, technology leaders need a clear, reliable framework for evaluating AI ROI.
Organizations increasingly expect scaled AI to pay its own way, at least partially. According to Apptio’s technology investment management report, 45% of organizations surveyed intend to fund innovation by reinvesting savings from AI-driven efficiencies. That model assumes that such savings are both achievable and quantifiable. Meanwhile, the two-thirds of organizations planning to reallocate existing budget capital to AI will need clarity on the trade-offs involved.
Much like the early days of public cloud, AI costs and returns are difficult to predict. Pricing varies widely across providers and continues to evolve, while consumption is unpredictable. The pressure to adopt quickly is also formidable as organizations navigate the threat of disruption by more agile competitors.
The new math of AI ROI
Considering the many variables, tech leaders should view AI ROI as a matter of optimization. At a high level, the implementation of AI initiatives is inevitable. The question is how to achieve the greatest possible returns — both financial and organizational.
Start with the business problem. There are many ways AI can deliver 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 decision-making speed? Increase throughput or capacity? Or chasing cool edge cases with high potential returns but minimal strategic relevance?
Determine what success looks like. AI can introduce a new capability or augment an existing one. For new capabilities, articulate the possibilities you’d like to unlock, such as new revenue opportunities, workflows, or decision-making processes. For augmentations, establish baseline performance and the expected lift you aim to achieve with AI.
Consider how finances will influence your evaluation. Some use cases may show minimal results in the near-term but drive significant value in the long-term. What’s your timeframe for return? On the other hand, more successful rollouts with rapid adoption can generate unexpectedly high inference bills. Would that mean pulling the plug — or leaning in further? What should your cost and return curve look like over the years? As you map your timeline, establish clear thresholds to determine whether you’ll proceed, pause, stop, or accelerate your investment.
Identify the right KPIs. The returns on an AI investment can be even more difficult to evaluate than the costs. Usage, efficiency, and financial impact all matter. But AI success metrics won’t always be straightforward. There may be new usage patterns you don’t yet have a way to measure. Your technology environment may experience follow-on shifts that call for further evaluation. Will you be able to lessen your reliance on other tools, such as reducing seats in your data analytics platform? How will you factor in cross-tool pricing comparisons for multiple AI providers with shifting rates?
To gain full context and insight, you must also take into account the alignment of the initiative with your broader strategy and consider the opportunity cost of the investments you might otherwise have made. Remember that you’re not evaluating AI business value in isolation; you’re deciding whether it’s the best use of finite capital across all your investments.
These decisions will call for a level of insight far exceeding what was needed to justify traditional purchases like network infrastructure or enterprise software. Tech leaders navigating the complexities of AI economics should 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. 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 account for AI value and cost across a wide array of dimensions — and translate hypothetical innovation into board presentations and budget justifications that hold up under scrutiny.
TBM can help leaders build a trustworthy cost foundation that captures AI spend across labor, infrastructure, inference, storage, and applications. As AI workloads shift 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 specialized skill sets. The framework also connects investments to business outcomes, aligning AI initiatives with strategic priorities and measurable results. With increased visibility, you’re able to identify issues and make decisions fast, such as catching cost spikes early. Early detection can help to determine if the usage shift merits shifting funding. This unified view of financial and operational data helps leaders scale what’s working and reassess what isn’t as adoption increases. TBM provides essential visibility and context across the entire AI spend management conversation. Even as pricing evolves, tooling changes, and workflows shift, you can apply the same analytical approach and understand what’s actually working and demonstrate ROI. Leaders who operationalize AI within a TBM framework can:
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Evaluate ROI at both project and portfolio levels
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Spot unexpected cost spikes
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Compare multiple AI tools
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Understand ripple effects across run-the-business systems
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Defend investment decisions with confidence
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Understand and manage total costs and usage across the AI investment lifecycle
From theory to practice
Organizations are moving beyond AI experiments, and we’re past the point where these investments can be funded on optimism alone. Amid heightened uncertainty and cost sensitivity, boards are asking more strategic questions and finance wants trustworthy data.
Enterprise leaders who treat AI as a managed investment, rather than a bet on innovation, are those who will scale it successfully. To fund AI responsibly, leaders must establish clarity around scope, outcomes, cost drivers, and readiness. A TBM-driven approach provides the data foundation, visibility, and accountability to make those decisions.
Learn more here about how Apptio TBM transforms IT spend management in the AI era.
Ajay Patel is General Manager at Apptio, an IBM Company.
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