IT Automation Trends 2026: Key Insights from the Global State of IT Automation Report

That number has been consistent enough over multiple years of this survey that it no longer surprises anyone. What has changed is why it matters.

Early hybrid strategies were largely about migration: moving workloads to the cloud while keeping some legacy systems on-prem. The focus was on integration and coexistence. Today, the priority has shifted to operational consistency. Workloads now run across:

• Public cloud platforms
• Private cloud infrastructure
• On-prem data centers
• Mainframe systems

These varying environments often appear within the same end-to-end process. Each introduces its own tooling, access patterns, and operational model.

Without centralized orchestration, automation fragments. Teams build and run workflows within individual environments but lose visibility the moment a process crosses a boundary. Alert fatigue increases. Recovery times lengthen. Compliance gaps emerge.

The organizations managing hybrid IT most effectively are those that standardize on orchestration platforms providing end-to-end workflow visibility, centralized governance, and event-driven execution, regardless of where a workload runs.  

The features driving modernization decisions are consistent with a broader shift toward enterprise-wide adoption:

• Self-service portals: 44% — the top feature for the third consecutive year
• Modern user interfaces: 38%
• SaaS deployment: 37%
• AI workflow creation: 36%

Legacy automation platforms were built for small teams of specialists. They require significant expertise to operate and are difficult to extend beyond the core IT team. Modern enterprise environments need automation to support a much broader set of users (including developers, cloud engineers, data teams, and business users), not just automation specialists. 

Self-service portals topping this list for three years running is not a coincidence. It reflects a fundamental change in what automation platforms need to deliver: not just better execution capabilities for experts, but accessible tooling that scales automation usage across the organization safely and efficiently.

The resolution is not headcount. It’s a shift from an expert-led delivery model to an automation-as-a-service model. Rather than executing workflows for every team, centralized automation groups are building governed frameworks that enable users to automate safely on their own. This typically involves:

• Reusable workflow templates that encode best practices
• Role-based access controls (RBAC) that govern what users can access and execute
• Standardized automation patterns that prevent sprawl
• Centralized monitoring and compliance controls

These structures allow organizations to expand automation usage while maintaining the operational consistency and security that centralized oversight provides. The automation-as-a-service model is how teams scale without chaos.

This is a healthy architectural signal. Automation is becoming invisible infrastructure. Rather than requiring teams to context-switch into a dedicated specialist console, organizations are embedding automation access directly into the tools people use every day — their ITSM system, their messaging platform, their internal web portal. 

Practically, this means automation platforms need robust API-first architectures and pre-built integrations with ITSM and collaboration tools. The interface is no longer the product. The integration surface is. 

Only 49% report fully integrated MFT workflows — meaning file transfers are embedded directly into orchestration workflows rather than managed separately. 41% have partial integration, where some MFT activity feeds into broader workflows, but monitoring, recovery, and governance remain disconnected.

That gap matters for reliability. When a file transfer fails in a partially integrated environment, recovery depends on someone noticing the failure, identifying the failure point, and manually intervening across two separate systems. In a fully integrated environment, the orchestration platform detects the failure, triggers the appropriate recovery workflow, and maintains a complete audit trail — automatically.  

Beyond failed transfer scenarios, full integration allows organizations to treat data movement as part of a broader automated process. This enables:

• Cohesive monitoring of workflows and transfers
• Automated recovery from transfer failures
• Centralized governance of data movement
• Tighter coordination with downstream processes

As hybrid data flows grow more complex — especially across cloud platforms and B2B environments — the 51% of organizations still running partial or disconnected MFT integration are carrying operational and compliance risk that fully integrated orchestration would eliminate.

The reasons are practical, not philosophical. Organizations moving AI from pilot to production consistently encounter the same set of challenges:

• Integrating AI services with existing automation workflows without rebuilding them from scratch
• Establishing governance and compliance controls for AI-generated outputs
• Developing in-house expertise around AI tooling and prompt engineering
• Managing AI-introduced latency in time-sensitive process chains

These are orchestration problems, not AI problems. Adding an AI capability to an existing workflow requires the workflow infrastructure to support it — with proper monitoring, human-in-the-loop approval chains for low-confidence outputs, exception handling, and auditability. That infrastructure is built into enterprise orchestration platforms. It doesn’t exist in most AI vendor tools or experimental frameworks. 

The 21% who’ve reached enterprise scale are not distinguished by better AI models. They’re distinguished by better operational foundations underneath those models.

WLA/SOAPs ranking second is architecturally significant, and here’s why: AI vendor-created tools, custom-built frameworks, and open-source workflow automation tools (not to be confused with workload automation tools) are generally not designed with hardened enterprise-production requirements in mind. They work well for experimentation and for smaller, team-centric workflows. They don’t natively provide the coordination capabilities that enterprise production demands.

That gap becomes clear when you look at what a real enterprise AI process actually involves.  

Consider this realistic enterprise AI workflow scenario: a business event triggers an LLM task, the output must be passed to a downstream data pipeline, a human approval step is triggered if the confidence score falls below a defined threshold, and the entire process must generate a complete, auditable trail. That scenario requires:

• Heterogeneous execution environment coordination
• Dependency management across systems
• SLA enforcement
• Human-in-the-loop approval workflows
• Exception handling and automated recovery
• End-to-end auditability and centralized governance

AI vendor tools are optimized for automation within their own ecosystems. That’s a feature, not a flaw — but it also defines their limit. That’s where orchestration platforms come in. They’re designed to connect systems, manage complexity, and ensure that workflows run reliably at scale.