Jesse Stockall is Chief Architect, Flexera.
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As organizations across industries accelerate their adoption of artificial intelligence (AI), cloud spending tied to those initiatives is rising just as quickly. While early cloud strategies prioritized cost optimization, many organizations are realizing this reductive view on spend isn’t cutting it. Today, leaders face a hard-to-master challenge: ensuring that their AI investments deliver measurable business value.
For years, leaders have evaluated technology initiatives through narrow, cost-centric metrics—budget, headcount and near-term ROI—often missing the broader indicators that signal sustained business value. The Flexera 2026 State of the Cloud report suggests that mindset is starting to shift. When asked which metrics respondents use to assess progress against cloud goals, value delivered to business units increased by 12 percentage points year over year to 64%, while cost efficiency and savings fell by six points as a primary success metric.
This shift signals a clear pivot toward prioritizing business outcomes over cost alone. AI’s greatest impact may not show up in reduced spending. In many cases, mature AI manifests in areas like improved decision-making speed, operational effectiveness and stronger customer experiences. While these gains do not always reduce overall spending, they represent a meaningful return.
In the early days of AI adoption, organizations committed significant capital to its initiatives, causing boards and executives to expect rapid, measurable proof of financial impact. Given that AI’s output isn’t instantaneous, many of them were disappointed with the results, motivating them to rethink the metrics that matter.
So much so, last year’s Flexera 2025 State of the Cloud report found that 87% of respondents used cost efficiency and savings as their top metric to track success, compared to 81% in 2026. As AI moves out of its infancy, it's evident that it enables teams to operate more efficiently—but leaders are seeing that efficiency doesn’t always translate into immediate reductions in overall spend.
With more time to wrap their heads around AI, leaders understand ROI is not always immediate and investments should always be approached with a long-term perspective. The initial decision to invest should be driven by a deep understanding of an organization's strategic, long-term business goals. These goals often take longer to appear on financial statements but can significantly transform how organizations operate.
However, that long-term mindset must coexist with a now much faster business cycle, prompting leaders to rethink how they plan and adjust their technology strategies.
AI's rapid evolution has created a reality where tools become obsolete in just a few months. Historically, executives built three-to-five-year strategies grounded in competitive analysis and cost metrics. While that discipline hasn't changed, the speed at which leaders must move has increased exponentially.
This pace requires leaders to be more agile than ever, transforming traditional long-term planning into a model of continuous evaluation, with monthly course-correction to stay aligned with market realities. This strategy is not always compatible with cost savings.
A higher price tag doesn’t automatically make an AI investment the wrong choice. In many cases, tools that deliver long-term business value drive far greater organizational transformation and competitive advantage than solutions focused on short-term cost savings. To justify these investments, leaders must move beyond intuition and establish a clear framework for quantifying success.
AI projects must have measurable metrics and clearly defined outcomes in order to deliver real value. Practical metrics can provide a more complete picture of value. Key areas to consider include:
1. Beginning With A Problem Or Pain Point: Clearly define a desired outcome rather than starting with the technology and looking for a place to use it. While there is an existing temptation to develop everything in-house, at times, leveraging existing vendors allows organizations to focus more on core competencies.
2. Outlining Specific Business KPIs: The most consistent indicators of ROI show up progressively in how time, cost or risk are being reduced. AI ROI should also be tracked through specific business KPIs like churn reduction, resolution time or conversion rates.
3. Tracking Productivity Gains: Increasing efficiency plays a key role in improving AI ROI by reducing operational costs, increasing output without expanding headcount and speeding up delivery. By automating routine tasks and minimizing errors, AI frees employees to focus on higher-value work.
In most cases, however, returns don’t show up straight away. At times, it can take longer for benefits to translate into measurable financial outcomes, especially in larger organizations.
Companies that focus exclusively on cost may risk underinvesting in the full potential of AI. We are seeing a clear shift in leaders rethinking what defines success as AI exits the hype cycle. By measuring success through long-term business value, organizations remain competitive. In this new technology era, the true return on AI is not simply about minimizing budgets—it’s sustained business value.
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