2026 IT Spending and Budget Forecasts: Where Organizations Are Investing

Global IT spending is expected to top $6 trillion for the first time, nearly ten percent higher year-over-year, according to Gartner. This spending will be driven by continued investment in software, infrastructure, and AI-related technologies. But this growth looks different from previous years.

Organizations remain confident in technology’s role in driving business growth and operations. We may, however, see some budgetary pushback, as executives expect investment in emerging technologies — such as generative AI and advanced automation — to be justified with real, tangible value.

The following overview explores how global IT spending is evolving in 2026, what’s driving growth, and where organizations are focusing their budgets to balance innovation with efficiency. The analysis is structured around the three key spending categories shaping today’s IT landscape, including:

• Security and compliance
• AI and automation
• Cloud infrastructure and hybrid work solutions

Let’s dive in.

Security and compliance

Security and compliance remain among the fastest-growing areas of IT investment in 2026. One forecast expects global cybersecurity and risk management spending to grow 12.5% to $240 billion.

This growing investment reflects the changing threat landscape and the need to mitigate risk. Generative AI is making attacks such as phishing, credential theft, and ransomware easier to scale and harder to detect. AI-enabled attack activity increased by 72% in 2025, placing additional strain on security teams and increasing the likelihood and potential impact of successful security incidents.

As a result, security spending is increasingly viewed as essential to maintaining operational continuity, rather than as a discretionary response to individual incidents.

In 2026, security budgets are focused on a small number of priority areas where organizations expect to see the greatest operational and risk-reduction impact:

• AI-augmented security operations. Automation and investigation workflows across SIEM, SOAR, and detection platforms improve response speed without expanding security headcount.
• Identity-focused security controls. Identity threat detection and response (ITDR) and related tooling, reflecting the rise of credential abuse across cloud, SaaS, and third-party environments.
• Cloud and application security. Protection of cloud workloads, APIs, and non-human identities as cloud-native architectures become central to day-to-day operations.
• Data protection and visibility. Technologies that help organizations identify sensitive data, enforce access controls, and monitor data usage across hybrid, cloud, and AI-enabled environments.
• Operational resilience, risk, and compliance. Backup, disaster recovery, service continuity, monitoring, and reporting capabilities that support recovery from cyber incidents and meet regulatory expectations.

New and tightened regulations, such as the EU’s Digital Operational Resilience Act (DORA), which is now in force, also force organizations’ hands, requiring them to invest to comply with the rules.

AI and automation

While the broader bluster around AI continues, organizations are taking a more mature approach, moving beyond experimentation and expecting AI investments to deliver measurable productivity gains and return on investment.

This doesn’t mean enthusiasm is waning. AI remains one of the fastest-growing areas of IT investment, with global spending expected to exceed $2 trillion in 2026. What has changed is the intent behind that investment: organizations are now prioritizing scalability, reliability, and operational impact over novelty.

As a result, AI budgets are increasingly focused on production-scale deployment rather than exploratory pilots. Infrastructure and compute capacity have become key constraints on AI deployment at scale, driving disproportionate investment. Spending on AI infrastructure — particularly servers with embedded accelerators — now accounts for 84.1% of total AI spend.

Beyond infrastructure, spending is shifting toward embedding AI into day-to-day operations. Organizations are investing in automation built directly into workflows to improve productivity, alongside AI-enabled analytics that apply machine learning to operational data to support faster and more informed decision-making.

Increased investment in integration, governance, and operational tooling reflects the need to manage AI with the same discipline as other core enterprise systems.

Cloud infrastructure and platforms

Cloud infrastructure and platforms remain a central area of IT investment in 2026, with Gartner forecasting global cloud services spending to reach $877 billion. While adoption is now widespread, the drivers behind that spending have evolved.

With most organizations running a majority of their workflows in the cloud, the question is no longer whether to adopt cloud, but how to operate it efficiently while supporting larger, more compute-intensive workloads.

A growing share of cloud investment — expected to double from 2025 levels —  is being directed toward AI-optimized cloud infrastructure, which offers higher-performance compute and accelerated processing to support AI models and data-intensive applications in production.

As more operational workloads move to the cloud, cloud-native analytics becomes increasingly necessary rather than an add-on. The growing volumes of data generated by cloud, AI, and digital services mean analytics must operate in the same environment to process, correlate, and act on that data effectively. This shift is reflected in market growth: the global cloud analytics market is projected to grow at a 17.7% CAGR, reaching $147 billion by 2032, reinforcing a cycle of cloud adoption that increases pressure on infrastructure capacity.

These AI-driven, analytics-intensive workloads place significantly greater demands on infrastructure, reinforcing the role of cloud platforms for resource-intensive use cases where capacity can be scaled to meet peak demand.

However, this pay-as-you-go model also makes costs more variable, increasing the need for tighter control over usage as infrastructure requirements grow. As a result, cost management has become a defining feature of cloud strategy. Gartner research shows that 84% of CIOs now identify cost optimization as a top IT priority, ranking it ahead of security for the first time.

In 2026, cloud investment is expected to continue growing, but it is increasingly shaped by broader questions around cost variability, governance, and the ability to justify spend as AI-driven workloads scale.

(Related reading: a how-to guide for managing cloud costs.)

The trade-offs shaping IT investment in 2026

Despite global IT spending continuing to grow, a defining tension is emerging in 2026 budgets. The same AI-driven workloads that justify increased cloud investment are also making infrastructure costs more variable and harder to predict.

As a result, organizations face a clear trade-off: limit the pace of AI deployment in favor of controlling costs, or continue scaling in the expectation that long-term competitive advantage will outweigh near-term cost pressure.

In practice, many organizations are pursuing a third path. Rather than slowing AI investment, they are spending more on visibility and optimization to scale AI workloads in a controlled and sustainable way. In 2026, continued investment in AI will increasingly depend on initiatives demonstrating clear value today, not just the promise of value down the line.

FAQs about Global IT Spending in 2026