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From cost tracking to cost intelligence: Making IT economics always-on

July 17, 2026

<h2>As AI pushes technology budgets to record highs, the enterprises that win will be the ones that understand their spend in real time.</h2>

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Global enterprise technology spending is rising rapidly. <a rel="noopener noreferrer" href="https://www.cognizant.com/en\_us/insights/documents/cost-intelligence-research-report.pdf" target="_blank">In recently published research</a> from Dun & Bradstreet, commissioned by Cognizant, 89% of organizations expect technology spend to rise in the next 12 months, with one-third forecasting growth above 10%. For the majority (80%) of organizations, the ambition behind this spending is to drive growth, not simply reduce cost. That reframes the cost governance question. When technology is a growth engine, businesses need to look beyond cost containment, to making every dollar visible, controllable and clearly aligned to business outcomes. Yet that is precisely where most enterprises fall short. Our research shows that only 12% of organizations have a fully consolidated, enterprise-wide view of technology spend. Visibility exists in pockets—across budget owners, teams and platforms—but for most, it stops short of reconciling into a single, complete picture. That gap is the case for IT cost intelligence: a shift from simply tracking technology costs to understanding, predicting and acting on them continuously, in alignment with the business decisions they support. Cost intelligence pairs a strategic lens with analytical processes to understand and optimize IT investment in harmony with business objectives. In practice, that means real-time cost visibility, dynamic scenario modelling for shared decision-making, portfolio prioritization based on strategic fit and return on investment, and forward-looking projections built on predictive analytics and benchmarks. <h3>Why cost intelligence is an urgent need</h3> The distinction between cost management and cost intelligence matters because the cost of getting it wrong has risen sharply. Two forces have made cost intelligence a boardroom issue rather than a back-office one. The first is waste. According to<a> </a><a rel="noopener noreferrer" target="_blank" href="https://info.flexera.com/CM-REPORT-State-of-the-Cloud?lead\_source=Organic%20Search">Flexera's 2026 State of the Cloud Report</a>, an estimated 29% of cloud spend is now wasted—and, strikingly, that figure rose for the first time in five years, reversing a long downward trend. The new dimension is AI: Dynamic, hard-to-forecast workloads and new pricing models are making cost visibility more difficult, even as FinOps practices mature. Layer on fragmented data across cloud, on-prem and SaaS, ungoverned shadow IT and a persistent inability to tie technology spend to business outcomes, and most enterprises simply cannot see where their money actually goes. In our research, 63% of organizations cite fragmented data across systems and tools as the biggest obstacle to stronger IT cost intelligence, while 53% point to limited analytics or reporting capability. Forty-one percent highlight insufficient collaboration between IT and finance. The problem is less about a lack of data and more about too many disconnected signals that do not reconcile into a single view of value. The second force is the new economics of AI itself. Our research highlights that near-term AI spend accumulates well beyond model usage alone. It spans model access, platform tooling, integration, guardrails, change management and specialist AI engineering and product talent. At the same time, generative AI-enabled automation and agentic workflows are being positioned as cost-takeout levers, which creates a new governance requirement: tracking whether productivity gains actually translate into run-cost takeout or simply fund additional demand. <a rel="noopener noreferrer" target="_blank" href="https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-burns-its-2026-ai-budget-in-four-months-on-claude-code/">Uber's 2026 experience</a> made this concrete. The company reportedly exhausted its entire 2026 AI budget by April, after Anthropic's Claude Code spread across roughly 5,000 engineers. Adoption jumped from 32% of engineers in February to 84% classified as agentic-coding users by March. While the tool increased productivity, the consumption-based, token-by-token pricing behaves nothing like the per-seat software line items CFOs know how to plan around. <a rel="noopener noreferrer" target="_blank" href="https://spacedaily.com/n-microsoft-is-canceling-claude-code-licenses-across-its-experiences-devices-division-by-june-30-steering-thousands-of-engineers-toward-github-copilot-while-uber-burned-through-its-entire-2026-ai-bu/">Even Microsoft moved to scale back Claude Code licenses</a> as token costs climbed. The FinOps Foundation <a rel="noopener noreferrer" target="_blank" href="https://data.finops.org/">now ranks managing AI cost</a> as the top forward-looking priority for practitioners. When a single capability can blow through an annual budget in a quarter, "we'll reconcile it next cycle" stops being a viable operating model. <h3>The maturity journey: Most organizations are still at stage one</h3> Across client engagements, IT cost maturity tends to fall into four stages: <ol> <li>Cost tracking: "We know what we're spending." Invoices, cloud billing, spreadsheets. The data is days or weeks old and lacks context. Most organizations are here. </li> <li>Cost visibility: "We can see it." Dashboards exist, costs are tagged by team or project, showback reports reach business-unit heads. While this is superior to cost tracking, it is still backward-looking. </li> <li>Cost optimization: "We're acting on it." Rightsizing and reserved-instance management are happening, but reactively, driven by budget cycles rather than continuous signal. </li> <li>Cost intelligence — "We're predicting and automating it." Anomaly detection, natural-language query, auto-remediation, and unit economics tied to business outcomes. Only a small fraction of enterprises have arrived.</li> </ol> Research from <a href="https://www.cognizant.com/assets/apac/en/field-marketing/documents/cmp-007179/Cognizant-Technology-Cost-Intelligence-in-Asia-Enterprises-Dec-2024.pdf" target="_blank">IDC</a>, commissioned by Cognizant, frames this through organizational archetypes—a way for CIOs to pinpoint the cost-intelligence gaps that slow their speed-to-decision, and to speak more credibly with their C-suite peers about business impact. <h3>What it takes to achieve cost intelligence maturity</h3> Maturity is built across five capability domains, spanning people, process, data and technology: <ul> <li>Data foundation and visibility. A unified cost layer across cloud, on-prem and SaaS—and also AI tokens in the new era—is a critical step towards achieving this maturity. </li> <li>AI-driven analytics. Anomaly detection, forecasting, machine language-powered rightsizing and natural-language interfaces that let business users ask questions directly. </li> <li>A FinOps operating model. Cross-functional ownership where engineering owns, finance governs and leadership decides, moving deliberately from inform to optimize to operate. </li> <li>Business-value alignment. IT cost mapped to capabilities and products, with unit economics such as cost-per-transaction or cost-per-customer. </li> <li>Automation and continuous optimization. Policy-as-code remediation and a feedback loop that keeps the cost posture self-correcting.</li> </ul> Underneath sits a three-layer reference architecture—business, data and technology—held together by a FinOps governance model rather than by any single tool. <h3>From saving money to funding the future</h3> The point of cost intelligence is not minimizing spend. It is freeing "run-the-business" dollars so they can be redirected into innovation and growth, which is precisely why most organizations are investing in technology in the first place. The patterns are already visible through Cognizant's own engagements. In one engagement, we helped an enterprise analyze maintenance and development spend across more than 10 vendors. We consolidated these to three—selected on their AI capabilities—and repurposed significant cost in the process. In another, we examined end-of-life and end-of-service applications across 300+ systems, identifying where AI agents could replace them outright and retire the associated license and maintenance costs. That is the destination: every technology dollar traceable to a business outcome, the wasteful spend continuously surfaced and remediated, and the savings reinvested where they compound. Cost intelligence, done well, is not a dashboard you check at quarter-end. It is an always-on capability. And in an era where AI can rewrite a budget in a single sprint, it may be the difference between the CIOs who are trusted to invest in the next wave and the ones still explaining last quarter's overrun. Please download the full research report on cost intelligence, <a rel="noopener noreferrer" href="/content/dam/connectedassets/cognizant-global-marketing/marketing-channels/cognizant-dotcom/en_us/insights/documents/cost-intelligence-research-report.pdf" target="_blank">Smarter IT spend: From cost control to cost intelligence</a>.