Forrester forecast global technology spending to reach $4.9 trillion in calendar year 2025, up 5.6% from $4.7 trillion in 2024. But that figure is not a definitive measure of enterprise-only spending, and it is a forecast rather than a verified final result.
The durable trend behind the headline is a shift toward AI infrastructure, software, cloud operating models, managed IT services, modernization, and cybersecurity. For CIOs, CFOs, and investors, the important question is not simply how much technology spending grows, but whether the additional dollars represent new capacity, higher prices, recurring consumption, or budgets being moved from legacy systems.
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What the $4.9 trillion forecast actually measures
Forrester’s February 12, 2025 forecast described global technology spending, not “enterprise tech spending” as a formally defined category. It projected spending to rise from $4.7 trillion in 2024 to $4.9 trillion in 2025, a 5.6% increase. Forrester said software and IT services would account for 66% of the 2025 total and forecast software growth of 10.5%.
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That distinction matters because several commonly quoted figures measure narrower markets:
- Global technology spending: a broad market estimate such as Forrester’s $4.9 trillion forecast.
- Worldwide IT spending: Gartner’s separate market view, covering categories such as data-center systems, devices, software, IT services, and communications.
- Public-cloud spending: end-user expenditure on public-cloud services, not all cloud-related investment.
- AI spending: a category that can overlap with servers, cloud capacity, software, services, and consulting.
- Enterprise IT budgets: the internal spending plans of particular companies, which vary by industry, size, geography, regulation, and existing contracts.
Forrester identified generative AI, cybersecurity, cloud technologies, and legacy modernization as important growth drivers. It did not establish that AI alone caused a specific dollar amount of the $200 billion increase.
Read Forrester’s forecast and methodology description.
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Why major technology forecasts do not match
The $4.9 trillion figure is credible as a dated Forrester forecast, but it is not an industry-wide consensus number. Gartner’s January 2025 forecast put worldwide IT spending at $5.61 trillion, growing 9.8%. In July, Gartner revised that estimate to approximately $5.44 trillion, or $5.435555 trillion, with 7.9% growth.
| Forecaster or category | 2025 figure | Growth | How to interpret it |
|---|---|---|---|
| Forrester | $4.9 trillion | 5.6% | Global technology spending; software and IT services emphasized |
| Gartner, January 2025 | $5.61 trillion | 9.8% | Worldwide IT spending across data centers, devices, software, services, and communications |
| Gartner, July 2025 | About $5.44 trillion | 7.9% | Revised worldwide IT-spending forecast reflecting uncertainty and slower net-new spending |
| Gartner public cloud | $723.4 billion | Not stated in the cited release | Public-cloud end-user spending only |
| Gartner information security | $212 billion | 15.1% | Information-security end-user spending |
These figures should not be added together or treated as measurements of precisely the same market. Differences can result from category boundaries, communications inclusion, consumer coverage, currency assumptions, vendor revenue versus end-user spending, the treatment of managed services and internal labor, and the date of the forecast.
Gartner’s revision is also a reminder that forecasts move. Its July update described a pause in some net-new technology spending while recurring cloud and managed-services spending remained more stable. Higher nominal budgets may partly reflect price increases and renewals rather than equivalent growth in computing capacity or business capability.
See Gartner’s January forecast and its July revision.
AI spending is a stack, not a single budget line
AI is influencing technology budgets at several layers. Treating it as a single software category hides where the money is actually going.
AI infrastructure
The infrastructure layer includes AI-optimized servers, GPUs and other accelerators, high-bandwidth memory, networking, storage, data-center power and cooling, colocation, and cloud capacity. Gartner’s January forecast put 2025 spending on AI-optimized servers at $202 billion—more than twice traditional-server spending. Its July forecast put data-center systems spending at approximately $474.883 billion, growing 42.4%.
This spending is concentrated. Hyperscalers, IT-services companies, chip suppliers, and AI vendors are building capacity to sell cloud and AI services. Their capital expenditure should not be confused with broad-based adoption by ordinary enterprises.
AI platforms and applications
Enterprise buyers may pay for foundation-model access, model APIs, retrieval-augmented generation, enterprise search, vector databases, AI application-development tools, observability, evaluation, model security, governance, and AI features embedded in existing applications.
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Read Gartner’s AI-spending forecast.
Implementation and operating costs
For many companies, the model license is only one part of the bill. AI projects can require data cleaning, modernization, integration with ERP, CRM, HR, and developer systems, consulting, systems integration, employee training, governance, compliance, human review, and process redesign.
A company can increase AI licenses without increasing productivity if it has poor data, unclear permissions, weak adoption, or no redesigned workflow. Seats assigned, prompts submitted, and demonstrations completed are not financial-return measures.
Cloud remains a growth engine—but not automatically a saving
Gartner forecast public-cloud end-user spending of $723.4 billion in 2025, up from $595.7 billion in 2024, with all major cloud segments expected to grow at double-digit rates.
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The total includes spending across infrastructure as a service, platform as a service, software as a service, managed operations, data warehouses and lakehouses, cloud networking, security, backup, disaster recovery, and AI training and inference.
Cloud can improve flexibility, deployment speed, resilience, and access to specialized infrastructure. It can also replace predictable capital expenditure with recurring operating expenditure. Costs may rise because of idle resources, data-egress charges, premium managed services, multi-cloud duplication, changed software licensing, and the variable usage of AI workloads.
Before approving a cloud expansion, finance and technology teams should examine utilization, committed-use discounts, workload portability, network costs, data location, disaster recovery, security staffing, and exit costs. FinOps discipline is particularly important when usage-based AI services are layered onto existing cloud bills.
See Gartner’s public-cloud forecast.
Cybersecurity is both a defensive requirement and an AI multiplier
Gartner projected worldwide information-security end-user spending of $212 billion in 2025, up 15.1% from $183.9 billion in 2024. It linked the increase to AI adoption, cloud migration, application and data security, privacy, and infrastructure protection.
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Security spending can include identity and access management, endpoint protection, cloud-security posture management, cloud-workload protection, application and API security, data-loss prevention, SIEM, extended detection and response, managed detection and response, email and browser security, AI-security controls, privacy, and compliance.
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AI increases security demand because organizations must protect models, prompts, data, agents, identities, and integrations. At the same time, security vendors use machine learning for detection, triage, investigation, and response. “AI-powered” does not automatically mean safer: buyers still need evidence on false positives, explainability, data handling, integration, human escalation, and incident-response performance.
Security budgets can also become tool-heavy. Adding another dashboard without consolidating overlapping endpoint, identity, cloud, and SIEM products may increase complexity instead of reducing risk.
See Gartner’s information-security forecast.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who is paying for the build-out?
The spending is distributed across several groups:
- Hyperscalers: building data centers, networking, storage, and accelerator capacity for cloud and AI services.
- Technology vendors: embedding AI into productivity, developer, data, and enterprise applications.
- Enterprises: buying applications, cloud capacity, consulting, integration, security, and employee enablement.
- Governments and public institutions: funding digital infrastructure, security, modernization, and regulated workloads.
- IT-services firms and systems integrators: implementing, operating, and governing complex environments.
Gartner said IT-services companies and hyperscalers accounted for more than 70% of 2025 spending in the relevant forecast context. That illustrates why early AI spending can be concentrated on the supply side, even while many companies remain cautious about large-scale deployment.
What technology buyers should test before approving new spending
For AI purchases
- Define the business baseline and the measurable outcome.
- Calculate inference, storage, integration, training, and human-review costs—not just license fees.
- Test data quality, permissions, privacy, and security.
- Compare a smaller model or conventional automation with a more expensive model.
- Require evaluation criteria for accuracy, reliability, latency, and escalation.
- Review portability, usage caps, overage terms, and vendor lock-in.
For cloud purchases
- Model utilization, egress, network, backup, and disaster-recovery costs.
- Assess latency, availability, sovereignty, and portability requirements.
- Review committed-use economics and the cost of unused capacity.
- Identify managed-service dependencies and the skills required to operate them.
- Assign clear responsibility for security and compliance.
For cybersecurity purchases
- Map coverage across identities, endpoints, SaaS, cloud workloads, applications, and data.
- Check whether the product replaces an existing tool or adds another overlapping layer.
- Compare licensing metrics, including users, endpoints, workloads, events, data volume, and consumption.
- Assess deployment effort, staffing, managed-service needs, retention, and geographic processing.
- Demand evidence about detection quality, response speed, integrations, and forensic support.
The financial meaning of the $4.9 trillion headline
For investors and business readers, the forecast points to strong demand for software, IT services, cloud capacity, data-center infrastructure, cybersecurity, and modernization. It does not mean every technology vendor, enterprise, or employee will benefit equally.
For corporate finance teams, the practical distinction is between nominal growth, real growth, reallocation, and net-new investment. A higher bill may reflect price increases or renewals. A cloud migration may improve agility while raising total recurring cost. An AI purchase may create value only after data, workflow, training, and governance investments are complete.
The most defensible conclusion is that 2025 technology spending was forecast to approach $4.9 trillion under Forrester’s broad global definition. AI, cloud, cybersecurity, software, and IT services were central areas of momentum, but the total was neither an enterprise-only number nor a clean measure of new productive capacity. Buyers should judge individual investments by measurable outcomes, total cost of ownership, resilience, security, portability, and the ability to reduce overlapping complexity.
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