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Demand for IT services companies is being pulled by five related needs: building AI infrastructure and applications, moving and optimizing workloads in the cloud, modernizing software and operations, improving security and resilience, and accessing specialist skills through outsourcing. The opportunity is real but uneven. Spending is accelerating fastest in AI infrastructure, data-center systems and cloud consumption, while some traditional managed and labor-intensive services face slower growth, pricing pressure and substitution by automation.
What IT services demand actually includes
IT services demand is not one market. It includes project work such as cloud assessments, application development, systems integration and security implementation; recurring work such as managed operations, monitoring and support; and newer consumption-based arrangements in which customers buy infrastructure or software capabilities as a service. A company can benefit from one of these currents while losing ground in another.
It is also important to separate overall technology spending from outsourcing activity. A larger technology budget may be spent internally, on packaged software or on hardware rather than on an external services provider. Conversely, an outsourcing contract can move work from one supplier to another without creating entirely new industry demand. The strongest analysis therefore asks what customers are buying, why they are buying it, and whether the spend is recurring, project-based or merely being reallocated.
AI adoption is creating a new layer of infrastructure and implementation work
Artificial-intelligence initiatives require much more than a model license. Organizations need compute capacity, storage, networking, data engineering, governance, security controls, application integration and operational support. Existing systems often need to be cleaned up or connected before an AI feature can deliver a reliable business result. That chain creates work for infrastructure specialists, cloud architects, data teams, application integrators and managed operations providers.
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Gartner’s July 27, 2026 worldwide forecast illustrates where the investment is concentrated. It projects total IT spending of $6.37 trillion in 2026, 14.2% higher than in 2025. The category figures below are forecasts, not reported revenue for IT services companies.
| Gartner worldwide category | 2026 forecast | Forecast change | What it signals for services demand |
|---|---|---|---|
| Data-center systems | $822 billion | Up 62.5% | Capacity planning, deployment, networking, integration, resilience and operations around AI-heavy workloads |
| Infrastructure as a service (IaaS) | $287 billion | Up 29.3% | Cloud architecture, migration, platform engineering, cost control and ongoing operations |
| Software | $1.468 trillion | Up 15.5% | Implementation, customization, integration, modernization, data work and support |
| Services | $1.570 trillion | Up 5.3% | Professional, managed and other services across the technology market; not a direct measure of provider revenue growth |
Gartner analyst John-David Lovelock described data-center systems and IaaS as the top growth segments, reflecting investment in AI infrastructure, cloud platforms and intelligent applications. That does not mean every AI experiment becomes a profitable consulting engagement. Projects can be cancelled, consolidated or kept in-house, and customers may negotiate lower prices as suppliers compete for a limited pool of high-value work.
AI also changes the economics of delivery. Large language models can automate portions of coding, testing, documentation, service-desk work and other labor-intensive tasks. A provider may therefore win new AI-infrastructure or transformation work while seeing fewer billable hours in a conventional support contract. The relevant question is not simply whether a company mentions AI, but whether it can turn AI demand into repeatable, valuable services.
Where AI-related services appear in a customer project
- Readiness and strategy: identifying viable use cases, estimating data and compute requirements, and setting governance rules.
- Data foundations: consolidating sources, improving quality, defining access controls and building pipelines.
- Infrastructure: selecting capacity, configuring environments, connecting networks and designing for availability.
- Application integration: embedding model capabilities in workflows, customer interfaces and internal tools.
- Operations: monitoring performance, managing costs, updating models, handling incidents and measuring outcomes.
These stages can be sold separately or as a managed lifecycle. The durable demand tends to come from the operational work that remains after an initial proof of concept.
Cloud migration and consumption turn one-time projects into continuing work
Moving workloads to a cloud platform creates an initial wave of assessment, architecture, migration, integration and security work. After the move, customers still need platform engineering, reliability management, backup, observability, governance and cost optimization. Cloud-native services can therefore produce both project revenue and recurring operational demand.
The ISG Index’s second-quarter 2026 data measures a different part of the market from Gartner’s spending forecast. ISG tracks commercial outsourcing contracts with annual contract value of at least $5 million, rather than all cloud consumption or every IT purchase.
| ISG 2Q26 measure | Annual contract value | Year-over-year change | Interpretation |
|---|---|---|---|
| Managed services plus XaaS | $42.4 billion | Up 43% | Large commercial outsourcing and as-a-service contracts captured by the index |
| XaaS total | $31.5 billion | Up 65% | Strong demand for consumption-based infrastructure and software arrangements |
| IaaS | $25.8 billion | Up 78% | Particularly rapid growth in measured infrastructure-as-a-service contracts |
| SaaS | $5.7 billion | Up 25% | Growth in measured software-as-a-service contracts |
ISG’s figures should not be added to Gartner’s IaaS forecast. They have different definitions, thresholds and measurement methods. ISG’s July 9, 2026 release also forecast full-year 2026 growth of 30% for XaaS and 2.1% for managed-services revenue. Those are forecasts, not final results. The gap between them shows why a provider focused on cloud consumption can have a different outlook from one dependent on traditional labor-based managed services.
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Why cloud demand persists after migration
Cloud environments change the operating model rather than eliminating operations. Usage fluctuates, architectures evolve and security responsibilities remain shared between the customer and the platform operator. Customers often need help with:
- rightsizing compute and storage to prevent waste;
- setting budgets, usage policies and chargeback models;
- designing reliable backup, disaster recovery and multi-region arrangements;
- integrating cloud services with on-premises systems and packaged applications;
- monitoring performance, identity, configuration and compliance; and
- reworking applications so they use cloud capabilities without creating unnecessary complexity.
These needs support recurring contracts, but they also expose providers to margin pressure. Tools automate more routine administration, customers benchmark prices more aggressively, and a migration project may end sooner than expected if the client builds internal capability.
Digital transformation and application modernization broaden the market
Digital transformation is broader than an AI program or a cloud move. It covers changes to workflows, customer experiences, operating models and business processes, supported by hardware, software and services. Many organizations still run critical applications on architectures that are difficult to change. Modernization work can involve rewriting code, replacing a component, exposing application programming interfaces, moving data, improving user journeys or integrating systems after a merger.
IDC’s 2026 analysis of its Worldwide Digital Transformation Spending Guide, V1 2026, projects global digital-transformation software spending to reach $640 billion by 2029. It expects software’s share of total digital-transformation spending to rise from 32% in 2026 to 36% in 2029. IDC projects a 21.7% compound annual growth rate for digital-transformation software, compared with 10.6% for services and 19.8% for hardware over the period it describes. These are projections, not observed outcomes.
IDC also expects AI to represent roughly 40% of worldwide digital-transformation software investment by 2029. That still leaves most investment outside AI. As IDC analyst Mariya Yahnyuk put it, the growth in digital-transformation software is broad-based: organizations are investing across the software stack, with AI accelerating that investment rather than replacing it.
Modernization work that generates services demand
- Application assessment: mapping dependencies, technical debt, licensing and business criticality.
- Replatforming: moving an application to a newer runtime or managed service with limited code changes.
- Refactoring or rewriting: changing code and architecture to improve scalability, maintainability or release speed.
- Integration: connecting applications, data stores, identity systems and external partners through APIs or event-driven designs.
- Data modernization: consolidating information, improving lineage and making data usable for analytics and AI.
- Change enablement: redesigning processes, training users and measuring adoption after deployment.
Providers that can connect technical changes to measurable outcomes, such as faster order processing or fewer manual reconciliations, are better positioned than those selling modernization as an abstract technology refresh.
Security, resilience and sector-specific operations create non-discretionary work
Security demand comes from the need to prevent incidents, satisfy regulators, protect sensitive data and keep services available. Typical engagements include risk assessments, identity and access projects, security architecture, monitoring, incident response, recovery planning and compliance support. Resilience work overlaps with security but also covers backup, disaster recovery, critical-supplier planning and the ability to continue operating during equipment or network failures.
Demand varies by industry because the operational problem varies. IDC identifies security as a leading digital-investment use case in financial services, including detecting cyber threats and preventing fraud. In manufacturing and resources, IDC highlights autonomic operations, aging equipment, maintenance and complex supply chains. These examples show why industry expertise can matter as much as generic technical capacity; they are not evidence that cybersecurity alone determines the entire IT services market.
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A customer may postpone a new customer-facing application when budgets tighten, yet still fund a required identity-control upgrade, regulatory remediation or recovery test. That relative resilience does not make security immune to cuts. Buyers still consolidate tools, bring monitoring in-house or demand proof that a service reduces measurable risk. Providers must therefore demonstrate coverage, response quality, compliance evidence and a credible operating model.
Outsourcing and operating-model changes reshape who performs the work
Organizations outsource to obtain scarce skills, run systems continuously, accelerate a transformation or change the economics of an operation. A contract can cover a help desk, infrastructure operations, application maintenance, security monitoring or a complete business process. The customer may choose a fixed-fee arrangement, consumption pricing, outcome-based fees or a hybrid of these.
Outsourcing does not automatically mean net-new demand for the industry. Work can move from an internal team to a provider, from one provider to another, or from a broad contract to several specialist suppliers. ISG reported that much managed-services activity in the second quarter of 2026 appeared to reflect portfolio reshaping and operating-model changes. It also described greater competition and pricing deflation, while large language models affected labor-intensive work.
Signals of healthy outsourcing demand
- The customer has a recurring operational requirement rather than a one-off staffing gap.
- The provider owns a measurable service level, reliability target or business outcome.
- Specialist skills are difficult for the customer to recruit and retain internally.
- The contract includes modernization or automation that can improve the operation over time.
- The commercial model rewards efficiency instead of relying only on adding billable personnel.
Conversely, a contract built mainly on supplying labor for routine tasks is more exposed to automation, wage inflation and price competition. Providers are increasingly expected to combine people with reusable tooling, standard processes and domain knowledge.
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Why market growth is uneven
Several forces can raise total technology investment while limiting the gains available to an individual provider.
- Budget strain: Gartner noted that inflation, supply shortages, rising hardware and memory costs, AI-funding initiatives and shifting priorities are putting pressure on technology budgets.
- Different growth rates: Cloud and AI infrastructure can expand quickly while conventional managed services grow slowly.
- Internal delivery: Large customers may build engineering, data or security capabilities themselves rather than outsource them.
- Provider substitution: A new contract may replace an incumbent, creating revenue for one company but not additional market demand.
- Automation: AI tools can reduce the hours needed for coding, support and administration even as they create new architecture and governance work.
- Industry cycles: Financial services, manufacturing, healthcare, government and retail face different investment calendars, regulations and operating constraints.
For this reason, a market-wide spending increase should never be treated as a guarantee that every geography, service line or company will grow at the same rate.
How to compare IT services companies or market segments
When evaluating a provider, use its exposure to the demand drivers rather than its headline technology vocabulary. The following framework helps separate durable opportunity from short-term volume.
| Comparison axis | Questions to ask | Why it matters |
|---|---|---|
| AI infrastructure and implementation versus traditional labor | Does the provider design platforms, integrate AI into workflows and operate them, or mainly supply staff? | AI can expand high-value infrastructure work while reducing hours in routine services. |
| Cloud/XaaS versus managed-services mix | How much revenue comes from consumption-based cloud work compared with conventional outsourcing? | The segments have different growth rates, contract structures and pricing dynamics. |
| Recurring operations versus one-time projects | After migration or implementation, does the provider retain monitoring, optimization or support work? | Recurring services can improve visibility, but they also face automation and renewal pressure. |
| Industry exposure | Which sectors and regulated environments does the provider understand? | Security, resilience and operational requirements differ materially by industry. |
| Outcome delivery and economics | Can it show reliability, cost, revenue, risk or productivity outcomes while controlling delivery costs? | Customers are less willing to pay indefinitely for activity that is not tied to a result. |
No single mix is universally best. A specialist with deep expertise in a regulated industry may be attractive despite a smaller cloud footprint, while a cloud-heavy provider may grow quickly but face intense price competition. The correct comparison depends on the customer problem and the contract model.
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- Define the business outcome. State whether the goal is lower operating cost, faster releases, better resilience, regulatory compliance, a new digital product or a workable AI use case.
- Map the current estate. Document applications, data dependencies, contracts, identity controls, recovery objectives and the skills already available internally.
- Separate project and run work. Price migration, modernization and implementation independently from monitoring, support, optimization and incident response.
- Set measurable baselines. Record availability, deployment frequency, incident volume, cloud cost, response times or fraud losses before the engagement begins.
- Test the operating model. Clarify which decisions remain with the customer, how automation changes staffing, where data may be processed and how a provider can be replaced.
- Plan for the post-project phase. Require documentation, skills transfer, governance and a sustainable support model rather than treating go-live as the finish line.
This process helps buyers avoid paying for a fashionable technology label when the real requirement is data cleanup, integration, process redesign or disciplined operations.
What the 2026 outlook means for IT services companies
The evidence points to a market being reallocated rather than uniformly expanding. Gartner’s forecast places the fastest growth in data-center systems and IaaS. ISG’s contract data shows particularly strong momentum in XaaS, with IaaS growing faster than SaaS in its measured contracts, while its managed-services forecast is much more restrained. IDC expects digital-transformation software to expand rapidly and says AI will account for a large, but not complete, share of that investment.
Providers are therefore likely to compete on three fronts at once: access to scarce technical and industry expertise, the ability to operate complex cloud and AI environments reliably, and the ability to improve delivery economics through automation. Winning demand is not enough if each engagement requires more labor than the customer will pay for. The companies best aligned with the market will connect infrastructure, software, data, security and ongoing operations to outcomes customers can measure.
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