As global IT spending accelerates and enterprises move beyond AI experimentation, 2027 could redefine how technology is built, secured, delivered and monetised.
A global manufacturer discovers that a critical component will arrive late. Its procurement team checks supplier emails, inventory records and production schedules before recommending a response. In a more connected enterprise, an AI agent could gather this information, identify alternative suppliers, assess the production impact and prepare an action plan for approval.
Elsewhere, a software engineering team is using AI to modernise a legacy application, a cybersecurity team is monitoring autonomous systems, and a multinational company’s Global Capability Centre is building reusable AI platforms for business units across several markets.
These scenarios illustrate the direction in which enterprise technology is moving. But they also reveal a fundamental challenge that deploying AI is becoming easier, while making it reliable, secure, affordable and commercially valuable remains complex.
For the global IT industry, 2027 will arrive at a time of significant investment, rapid technological change and growing pressure to demonstrate returns. The next phase will not be determined by AI alone. Cloud infrastructure, data integration, cybersecurity, software engineering, digital sovereignty and the organisation of technology teams will all influence how value is created.
The global IT industry in 2026: An expanding market undergoing structural change
The IT industry enters 2027 with strong spending momentum, driven particularly by AI infrastructure, software and cloud services. However, the expansion is uneven: the economics of AI infrastructure are changing faster than those of many traditional technology segments.
According to Gartner’s April 2026 forecast, worldwide IT spending is expected to reach $6.31 trillion in 2026, an increase of 13.5% over 2025.
Gartner’s September 2026 forecast puts worldwide AI spending at approximately $2.7 trillion in 2026, representing annual growth of 49.5%. The estimate encompasses a broad range of AI-related spending, including infrastructure, software and services; it should not be interpreted as spending on AI applications alone.
The scale of these investments is changing technology priorities across the industry.
Infrastructure is absorbing a growing share of investment
The demand for AI computing is driving investment in specialised processors, servers, networking, storage and data centres. Gartner forecasts that spending on data-centre systems will grow by 55.8% in 2026, reaching approximately $788 billion.
Cloud providers and technology companies are investing to support both the training of AI models and the inference workloads generated when AI systems are used in production.
This distinction matters. Training models requires substantial computing resources, but deploying AI across thousands of business workflows creates a continuing demand for computing capacity. As AI adoption expands, infrastructure costs, power availability and workload efficiency will become increasingly important considerations.
Software is evolving from a collection of tools into a network of intelligent capabilities
Enterprise software is also changing. Applications that once primarily stored information or responded to user requests are increasingly incorporating AI capabilities that can interpret context, recommend decisions and execute tasks.
IDC’s analysis of agent adoption predicts that agent use among Global 2000 companies will increase tenfold by 2027. IDC also forecasts that agentic automation will enhance capabilities in more than 40% of enterprise applications by that year.
If these forecasts materialise, the distinction between software, infrastructure and services could become less pronounced. Applications will increasingly need to interact with one another, share trusted information and coordinate workflows.
Technology buyers are asking harder questions about value
The scale of AI investment has also raised expectations. Enterprises need to know whether AI reduces operating costs, improves productivity, strengthens customer experience or creates new revenue opportunities.
A successful demonstration is no longer sufficient evidence that a technology investment should be expanded across the organisation. Businesses must account for integration, ongoing computing costs, security, employee oversight and the operational changes required to realise the benefits.
The result is a technology market with two simultaneous characteristics: substantial investment in emerging capabilities and increasing scrutiny of the economics of deploying them.
Against this backdrop, ten trends stand out as particularly important for 2027.
1. Agentic AI will move from assistance to execution
AI assistants have already demonstrated their ability to summarise documents, generate content and answer questions. The next development is systems that can plan and execute multistep workflows using enterprise applications and data.
Agentic AI introduces a different operating model. Rather than waiting for a person to initiate every step, an agent can retrieve information, perform permitted actions, evaluate results and escalate exceptions.
IDC’s Agent Adoption: The IT Industry’s Next Great Inflection Point identifies this transition as a structural change in technology design, delivery and value creation.
Use case: A supply chain exception is handled before it becomes a production crisis
A manufacturer learns that a critical component will not arrive on schedule. An AI agent retrieves the relevant purchase order, checks inventory, compares approved suppliers and estimates the production impact of alternative delivery dates.
Instead of manually assembling information from several systems, the procurement manager receives a consolidated recommendation with supporting evidence. The manager approves the next step, while the agent updates the appropriate records and communicates the decision through authorised workflows.
This scenario depends on more than an advanced AI model. It requires integration, reliable data, permission controls and the ability to record what the agent has done.
What to watch in 2027: Enterprise agent platforms, workflow orchestration, agent identity, monitoring and the emergence of applications that can execute business tasks rather than merely present information.
2. AI-native software engineering will change application development
AI-assisted development is moving beyond code completion towards broader participation in software engineering. AI tools can support requirements analysis, code generation, testing, debugging, documentation and maintenance.
Gartner’s research on AI coding agent harnesses examines the environments and controls needed to scale agentic software engineering.
The opportunity is significant, particularly for organisations managing large application portfolios. However, faster code generation does not automatically produce secure, maintainable software. Engineering teams still need to validate outputs, manage dependencies and ensure that applications meet business requirements.
Use case: Modernising a legacy application without replacing everything at once
A financial services company relies on an application developed over several decades. Its documentation is incomplete, specialist knowledge is concentrated among a few employees, and replacing the system would create considerable cost and operational risk.
AI-assisted engineering tools help analyse the codebase, map dependencies, generate documentation and identify components that can be modernised. Engineers use automated tests to validate each change before migrating selected services.
The organisation can then modernise incrementally rather than attempting a disruptive replacement.
What to watch in 2027: AI-native development environments, automated testing, legacy modernisation and changes in how software engineering productivity is measured.
For IT services companies, the commercial challenge will be to demonstrate measurable improvements in delivery speed, quality and total cost of ownership—not simply the number of AI tools used.
3. AI-ready data and enterprise context will become strategic assets
An AI system can only be as reliable as the information it can access and interpret. Fragmented databases, inconsistent records, outdated documentation and unclear access permissions can undermine even sophisticated AI applications.
Gartner identifies AI context platforms as a strategic technology trend for 2027. These platforms are intended to organise and govern enterprise knowledge so that autonomous agents can use it more reliably.
IDC’s agent adoption research similarly highlights AI-ready data as a competitive advantage.
Use case: Three departments, three versions of the same customer
A global enterprise discovers that its sales, finance and customer support systems contain conflicting information about the same customer. Contract values differ, account details are outdated, and support teams cannot easily establish which record is authoritative.
Introducing an AI assistant without resolving these inconsistencies may simply accelerate the circulation of inaccurate information.
The enterprise instead establishes data ownership, reconciles critical records and creates governed connections between its systems. AI applications can then retrieve information with clearer context and appropriate access controls.
What to watch in 2027: Enterprise integration, data quality, master data management, knowledge graphs, data observability and AI context platforms.
For technology providers, the opportunity lies in helping businesses connect existing systems and make trusted information available to AI applications. Data integration is becoming a prerequisite for scaling enterprise AI, rather than a back-office technology concern.
4. AI security will become a distinct enterprise investment priority
The introduction of AI creates security risks that extend beyond traditional application vulnerabilities. These include prompt injection, exposure of confidential information, insecure model integrations and unauthorised actions by AI agents.
Gartner’s August 2026 forecast estimates that the market for securing AI will reach approximately $4.8 billion in 2027, representing 68.7% growth over 2026.
As AI agents gain access to enterprise systems, security teams must determine not only who can access a platform but also what each agent is allowed to read, change, approve or initiate.
Use case: A customer service agent encounters a malicious instruction
An AI customer service agent can retrieve account information and initiate refunds. A malicious message attempts to manipulate it into ignoring its instructions and disclosing restricted information.
A robust security architecture cannot rely solely on the model recognising the attack. It must enforce permissions independently, restrict access to sensitive information, validate actions and require human approval for high-risk transactions.
The system should also record agent activity so security teams can investigate incidents and strengthen controls.
What to watch in 2027: AI application security, agent identity and access management, runtime protection, AI gateways, security testing and monitoring of AI-specific risks.
The central challenge is to ensure that increased autonomy does not create uncontrolled access to enterprise systems.
5. Sovereign cloud and digital resilience will influence infrastructure strategy
Cloud strategy is becoming more complex as organisations balance cost, scalability and performance against regulatory requirements, geopolitical uncertainty and operational resilience.
Digital sovereignty concerns the control an organisation or government has over relevant aspects of its data, infrastructure, technology dependencies and legal exposure. The importance of these considerations varies by industry and jurisdiction.
Forrester’s Predictions 2027: Cloud Computing examines sovereignty, security and resilience as important considerations in the changing cloud landscape.
Use case: A multinational redesigns its cloud architecture
A healthcare organisation wants to deploy an AI application across several markets. Certain data must be stored or processed within specific jurisdictions, while other workloads can operate globally.
Instead of moving everything into one environment, the company evaluates which workloads require regional infrastructure, which can use global cloud services and how critical applications should recover during a disruption.
The resulting architecture may combine regional cloud environments, global services and controlled data exchanges.
What to watch in 2027: Sovereign cloud, hybrid-cloud architecture, workload portability, data residency, disaster recovery and resilient infrastructure.
The opportunity for IT providers is to help enterprises balance regulatory requirements, performance, cost and flexibility without assuming that a single cloud model will suit every workload.
6. AI infrastructure growth will increase pressure on cost and energy efficiency
AI adoption depends on more than software. It requires specialised computing, networking, storage, power and data-centre capacity. As AI systems move into production, organisations will need to manage the ongoing cost of running them.
Gartner forecasts that worldwide spending on AI-optimised infrastructure as a service will reach approximately $66.1 billion in 2027, up from $42.3 billion in 2026. Source: Gartner’s AI-optimised IaaS forecast.
The expansion of AI infrastructure creates opportunities for cloud providers and hardware vendors, but it also increases pressure on customers to manage computing expenditure efficiently.
Use case: An AI assistant becomes too expensive to scale
An enterprise introduces an AI assistant for employee queries. Adoption grows quickly, but so do inference costs. The technology team discovers that simple requests are being routed to expensive models and that some workflows repeatedly retrieve the same information.
The company introduces workload classification, routes simpler tasks to smaller models, caches suitable results and monitors the cost of completing each task. Advanced models remain available for requests that require them.
The objective is not simply to minimise computing costs. It is to maintain acceptable quality while improving the economics of the service.
What to watch in 2027: AI infrastructure optimisation, inference efficiency, model routing, FinOps for AI, data-centre capacity and energy management.
Technology providers will increasingly need to explain both the performance of their solutions and the cost of operating them at scale.
7. Low-code development and disposable applications will challenge IT governance
AI-assisted development and low-code platforms are making it easier for business users to create applications for specific operational needs. This can reduce development backlogs and accelerate experimentation, but it also creates new governance challenges.
In its September 2026 strategic predictions for 2027 and beyond, Gartner predicts that 80% of new applications will be intentionally disposable by 2029, designed for use lasting less than a year.
This forecast extends beyond 2027, but the underlying shift has immediate implications for application management.
Use case: A temporary application becomes a critical business dependency
An operations team builds a lightweight application for a product launch. Other departments find it useful and begin relying on it. Months later, its original creator leaves, documentation is missing and no one knows who maintains its integrations.
The problem is not that the application was created quickly. It is that the organisation failed to establish ownership, security requirements and retirement criteria.
A better approach allows employees to build applications in approved environments, registers them in an application inventory and establishes appropriate access controls, review dates and retirement processes.
What to watch in 2027: Governed low-code platforms, AI-generated applications, automated application discovery, security validation and lifecycle management.
The challenge for enterprise IT teams will be to encourage experimentation without losing visibility or control.
8. Physical AI will extend intelligence into the real world
Physical AI connects artificial intelligence with robots, industrial equipment, vehicles, sensors and other systems that interact with their surroundings.
Advances in computer vision, robotics, connected devices and edge computing are creating opportunities to use AI in industrial inspection, warehouse automation, equipment monitoring and hazardous tasks.
Gartner’s 2027 strategic predictions identify physical AI as an important development, with longer-term implications for frontline work.
Use case: An intelligent factory detects a production problem
A manufacturer uses connected sensors and computer vision to monitor an assembly line. An AI system identifies an unusual vibration pattern, correlates it with maintenance records and recommends inspecting a machine before it fails.
A maintenance engineer reviews the evidence and decides whether to intervene.
The value comes from connecting sensor data, AI inference, operational software and maintenance workflows. A model operating in isolation would be far less useful.
Physical AI also introduces risks that conventional business applications may not face, including physical safety, latency and the consequences of incorrect actions.
What to watch in 2027: Industrial AI, robotics integration, digital twins, edge computing, computer vision and operational technology cybersecurity.
For technology companies, the opportunity is to connect intelligence with physical operations while ensuring safety and reliability.
9. AI governance and accountability will become part of enterprise architecture
As AI systems gain greater autonomy, organisations will need clear rules governing the decisions they can make, the actions they can perform and the information they can access.
AI governance includes risk assessment, model evaluation, audit trails, human oversight, access controls and incident response. It also requires organisations to understand which legal and regulatory obligations apply to their systems.
Gartner’s Top Strategic Technology Trends for 2027 emphasises the need to govern autonomous systems and establish the conditions required for trusted outcomes.
Use case: A finance agent recommends a decision it cannot execute
An AI agent identifies a discrepancy between an invoice and a purchase order. It retrieves the relevant records, compares amounts and prepares a recommendation.
The enterprise allows the agent to investigate discrepancies and prepare cases but prohibits it from approving payments above a defined threshold. A human employee must review the evidence before the transaction proceeds.
This distinction between analytical capability and decision authority is critical. A system can be technically capable of performing an action without being authorised to do so.
What to watch in 2027: AI governance platforms, model evaluation, auditability, compliance automation, identity controls and mechanisms for human oversight.
Governance should enable responsible adoption, not simply add another layer of approvals. Organisations will need controls proportionate to the risks and consequences of each AI application.
10. AI monetisation will matter more than adoption alone
The technology industry has invested heavily in AI infrastructure, applications and experimentation. The next challenge is converting these investments into sustainable financial and operational value.
Gartner’s 2027 strategic predictions include a growing emphasis on linking AI expenditure to value. The wider implication is that AI investments will increasingly be assessed according to their measurable contribution to business performance.
For software companies, this may mean rethinking product packaging and pricing. For IT services providers, it may mean demonstrating improvements in delivery efficiency and customer outcomes. For enterprise buyers, it means evaluating AI initiatives according to the problems they solve.
Use case: A successful AI pilot fails the business case
A company deploys an AI assistant that reduces the time employees spend drafting reports. User feedback is positive, and the pilot attracts executive attention.
However, the finance team discovers that the organisation has not measured the cost of running the system, the time employees spend checking outputs or whether the saved hours have improved throughput.
The next phase introduces clearer measures: time saved per report, error rates, cost per completed task, employee adoption and the extent to which saved capacity can be redirected to higher-value work.
The initiative can then be evaluated on evidence rather than enthusiasm alone.
What to watch in 2027: AI return-on-investment measurement, outcome-based services, productivity-linked offerings, industry-specific AI products and new approaches to software pricing.
The competitive advantage may belong to companies that can connect technological performance to measurable business outcomes.
The GCC angle: Global Capability Centres could become strategic innovation engines
The technological trends described above are also changing how multinational enterprises organise their technology operations.
Global Capability Centres (GCCs) have traditionally supported multinational organisations through technology delivery, shared services and specialist operations. Their mandates increasingly extend into product engineering, research and development, artificial intelligence, data, cybersecurity and enterprise transformation.
Avasant’s State of the Global GCC Landscape 2026: Demographics, Staffing, Spending, and Innovation examines GCC developments across multiple geographies and industries. Its research provides a basis for examining how centres are evolving beyond conventional delivery responsibilities.
The significance of this shift lies not simply in where employees are located, but in what the centre is authorised to own, build and decide.
GCCs as enterprise AI engineering hubs
As enterprises move from AI pilots to production systems, GCCs can provide the engineering, data, integration and governance expertise needed to scale solutions across business functions.
Illustrative scenario: A multinational retailer has separate AI experiments in merchandising, customer support and supply chain planning. Its GCC develops shared data connectors, reusable AI components and common governance controls, enabling successful capabilities to be deployed across business units rather than rebuilt independently.
The benefit is the creation of reusable intellectual property and the ability to scale innovation across the enterprise.
From supporting products to owning them
There is a meaningful difference between a centre that maintains an application and one that owns its product roadmap, architecture, release decisions and performance outcomes.
Illustrative scenario: A global software company assigns a distributed product team, anchored in its GCC, responsibility for a core enterprise platform. The team participates in customer research, prioritises features, manages releases and measures adoption.
The centre is now accountable not only for delivery efficiency but also for product reliability, innovation and customer value.
Avasant’s research on the evolving GCC operating model is relevant to this shift towards broader responsibility and ownership.
AI will reshape GCC workforce planning
AI-assisted engineering could reduce effort in some routine activities while increasing demand for skills in architecture, AI evaluation, data engineering, cybersecurity and domain-specific problem-solving.
Illustrative scenario: A GCC discovers that AI tools have reduced the time required for routine testing and documentation. Leadership redirects some of the team’s capacity towards test architecture, security validation and more complex automation.
The outcome will depend on training, business demand and deliberate workflow redesign. AI does not automatically create higher-value work; organisations must plan for that transition.
Governance and accountability will become part of the GCC mandate
GCCs that develop or operate enterprise AI systems may need to implement testing, monitoring, access controls and audit trails within the parent organisation’s governance framework.
Illustrative scenario: A GCC develops an AI agent for procurement. The central security team defines the policies, while the GCC implements permission controls, logs agent activity, tests failure scenarios and monitors performance after deployment.
This model can combine global accountability with specialist execution, provided responsibilities and escalation paths are clearly defined.
GCC performance may increasingly be measured through business value
Traditional measures such as cost per employee, service levels and delivery efficiency will remain relevant. However, they may not fully capture the contribution of a centre that develops products, creates reusable technology or drives enterprise automation.
Potential measures include product adoption, platform reliability, intellectual property creation, validated productivity gains and contributions to customer or revenue outcomes.
The strategic question for 2027 is whether GCCs can move from executing work efficiently to owning capabilities that make the wider enterprise more competitive.
The pace of change will vary. Some centres will remain focused on operational excellence, while others will take on greater strategic responsibilities. The appropriate model will depend on business objectives, talent, governance and the level of ownership granted by the parent enterprise.
What should technology leaders prioritise before 2027?
The ten trends point towards five practical priorities.
1. Select AI use cases based on business value. Start with clearly defined problems, measurable outcomes and appropriate risk controls rather than deploying AI simply because the technology is available.
2. Strengthen data and integration foundations. Reliable information, clear ownership and controlled access are essential for AI applications operating across multiple enterprise systems.
3. Build security and governance into deployment. Define which actions AI systems can perform independently, which require human approval and how activity will be monitored.
4. Measure the economics of AI. Track operating costs, task completion, reliability, productivity and business impact. Scale initiatives when evidence supports the business case.
5. Align the operating model with technology strategy. Decide which capabilities should be built internally, which should be provided by external partners and where GCCs or distributed engineering teams can take greater ownership.
These priorities align with Gartner’s 2027 technology outlook and IDC’s analysis of the agent economy.
The objective is not to pursue every trend at once. It is to identify where technology can solve meaningful business problems and create the conditions required to scale it responsibly.
Frequently asked questions
What are the top IT industry trends for 2027?
The leading trends include agentic AI, AI-assisted software engineering, AI-ready data, AI security, sovereign cloud, AI infrastructure optimisation, low-code development, physical AI, AI governance and AI monetisation. Together, they reflect a shift towards autonomous workflows and measurable business outcomes.
How will agentic AI affect IT services companies?
AI agents could automate selected multistep tasks across development, testing, support and enterprise operations. IT services providers may need to redesign delivery models, develop agent orchestration expertise and demonstrate business outcomes while maintaining security and human oversight. IDC’s agent adoption research provides further context.
Why will AI-ready data matter in 2027?
AI systems require accurate, accessible and governed information to produce reliable results. Enterprises with fragmented data and disconnected applications may struggle to scale AI, making integration, data quality and context management important investment areas. See Gartner’s research on AI context platforms.
What role will Global Capability Centres play in the future of IT?
GCCs may increasingly contribute to AI engineering, product development, research and development, data platforms, cybersecurity and enterprise transformation. Their strategic importance will depend on the responsibilities they own and the business outcomes they deliver. Avasant’s 2026 GCC landscape report examines this evolution.
How will GCCs change as AI adoption increases?
AI could alter GCC workforce requirements, automate selected delivery tasks and increase demand for specialist engineering, governance and domain skills. Centres may also build reusable AI platforms and assume greater responsibility for products and intellectual property.
Will cybersecurity spending increase because of AI?
AI-specific security is expected to become a larger technology investment category. Gartner forecasts that the market for securing AI will reach approximately $4.8 billion in 2027. Actual spending will vary by enterprise adoption, threat exposure and regulatory requirements. Read Gartner’s forecast.
How can businesses measure AI return on investment?
Useful measures include cost per completed task, time saved, error rates, productivity, adoption, operational reliability and revenue or customer outcomes. Metrics should be defined before deployment and evaluated against a credible baseline.
Conclusion: 2027 will test the IT industry’s ability to turn innovation into value
The global IT industry enters 2027 with substantial investment, expanding AI infrastructure and a growing range of autonomous capabilities. Yet technology adoption alone will not determine which organisations succeed.
Agentic AI may change how enterprise processes are executed. AI-assisted engineering may reshape software delivery. Data integration, security and governance will determine whether these capabilities can be trusted at scale. Cloud strategies will need to balance performance, cost and resilience, while physical AI will extend intelligence into industrial and operational environments.
At the organisational level, GCCs may become an important part of this transition as multinational enterprises reconsider where technology is developed, who owns products and platforms, and how innovation is measured.
These changes will not unfold uniformly. Adoption will depend on business priorities, technology maturity, investment, regulation and the ability to manage operational risks.
Nevertheless, the direction is becoming clearer: the IT industry’s next competitive advantage may come from connecting intelligent technology with trusted data, effective operating models and measurable business results.
In 2027, the winners may not be the organisations that adopt the most technology, but those that turn it into lasting enterprise value.
Editorial disclaimer: Forecasts and market estimates are attributed to the respective research organisations and should not be interpreted as guaranteed outcomes. Business scenarios are illustrative, not descriptions of specific companies. Some analyst reports may require a subscription to access the full research.
