Where Enterprise Automation Is Heading: Insights from the 2026 Gartner® Magic Quadrant™ for SOAP

Gartner defines Service Orchestration and Automation Platforms as solutions that integrate, coordinate, and manage complex workflows and processes across IT.

In practical terms, SOAPs expand traditional workload automation into a broader orchestration model. Instead of managing isolated jobs or schedules, they coordinate end-to-end processes across technologies including mainframes, distributed environments, cloud infrastructure, applications, data platforms, and DevOps toolchains.

This distinction matters because modern business services rarely live inside a single application or infrastructure environment. A critical process might start on the mainframe, depend on a cloud data pipeline, trigger an ERP transaction, and finish by calling a SaaS service. Orchestration needs to understand and coordinate this entire chain.

The Gartner Magic Quadrant evaluates providers according to two dimensions: Ability to Execute and Completeness of Vision. Gartner describes the research as a starting point for understanding providers in a market and recommends that buyers evaluate vendors against their own requirements rather than selecting solely on quadrant position.

For the 2026 SOAP Magic Quadrant, Gartner also set substantial requirements for inclusion. Among other criteria, providers needed to have generated at least $25 million in 2025 SOAP revenue or have at least 200 paying SOAP customers as of March 1, 2026. Providers also needed to operate across at least two defined global regions and offer software that could be deployed without mandatory professional services.

These requirements illustrate an increasingly mature market. SOAP is moving beyond an emerging automation category toward an enterprise platform decision.

The report’s mandatory capabilities underline another important point: hybrid complexity is no longer considered an exception that automation platforms occasionally need to accommodate.

It is the environment they are expected to manage.

Gartner identifies capabilities including managing workloads across complex technology and deployment topologies, end-to-end operational continuity, broad integration and extensibility, flexible workflow design, intelligent remediation and resilience, and governance.

The scope covers environments ranging from on-premises infrastructure and enterprise applications to cloud, SaaS, colocation, and edge locations.

This has important architectural consequences, as enterprises need orchestration that can operate above individual platforms and technologies. Cross-system dependencies need to remain visible regardless of where a particular workload executes.

Thus, integration is not simply about how many connectors a platform offers. The broader question is whether those integrations allow organizations to model, execute, and observe a complete service across heterogeneous technologies.

This is also why modernization does not necessarily require a disruptive rip-and-replace strategy. A SOAP can provide a common orchestration layer across established and modern systems, allowing organizations to modernize progressively while maintaining control over mission-critical processes.

That principle is central to our approach with ANOW!® Automate, which provides a single point of control across heterogeneous environments and supports orchestration across mainframe, on-premises, and cloud ecosystems.

One of the most interesting developments in the SOAP market is the growing connection between orchestration and observability.

• Automation traditionally answers: What should run, when, and under which conditions?

• Observability answers another: What is actually happening, and why?

Autonomous operations require both.

The 2026 report emphasizes intelligent remediation and resilience, using real-time operational insight to identify anomalies and initiate actions such as retries, rollbacks, or automated scaling.

This moves the operating model beyond basic error handling. Consider a traditional automated workflow. When a task fails, the automation platform reports the failure and a human investigates the cause.

In a more intelligent model, telemetry provides context around the failure. The system can determine whether the issue is isolated, identify the affected dependencies, select an appropriate remediation path, execute it, and verify the result.

This is one reason Beta Systems is bringing automation and observability together within ANOW!® Suite.

ANOW!® Automate provides the orchestration layer, while ANOW!® Observe adds real-time visibility into workloads, infrastructure, and applications. ANOW! Observe uses OpenTelemetry-based data to provide richer operational context across automation environments.

We aim to turn operational insight into better automated decisions and build the foundation for operations that can become more proactive, resilient, and autonomous.

AI is another area where the 2026 report signals substantial change. Gartner predicts:

The first wave of GenAI in enterprise IT has largely focused on assisting people, such as summarizing information, generating scripts, explaining errors, or helping users create workflows.

Those use cases will continue, but the next phase is deeper integration into operational processes.

In troubleshooting, for example, GenAI can help correlate information from logs, workflow histories, infrastructure telemetry, and application data. Instead of operators manually moving between tools to understand why a workflow failed, AI can help interpret the available context and recommend the next action.

Workflow creation is also changing. GenAI co-authoring can lower the barrier to creating and modifying automation by translating human intent into executable workflow logic. These developments can significantly improve productivity, but their effectiveness depends on context.

A language model that sees an error message but does not understand the surrounding workflow, dependencies, SLAs, or infrastructure state has only part of the picture.

That is why we believe the convergence of automation, orchestration, data, and observability matters so much. AI needs operational context to move from generating suggestions to supporting reliable operational decisions.

The shift becomes even more significant when we look beyond GenAI assistance toward AI agents.

Traditionally, automation starts in predictable ways: a schedule is reached, an event occurs, an API is called, or a user initiates a process.

Agentic systems introduce another trigger: an AI agent determines that an action should occur. That could mean identifying a deteriorating service condition and initiating remediation, provisioning resources in response to predicted demand, restarting a failed dependency, or launching a workflow based on changing business conditions.

The 2026 Magic Quadrant’s optional features already reflect this direction, including multiagent orchestration, visual agentic workflows, and Model Context Protocol (MCP) support.

But giving AI agents access to enterprise systems raises a crucial question: How do you move from AI reasoning to reliable enterprise execution?

An agent may identify what needs to happen. The orchestration platform still needs to determine how that action executes across the relevant systems, which dependencies to respect, which permissions apply, and how to monitor the outcome.

In other words, AI does not eliminate the need for orchestration; it increases it.

As AI takes a more active role, governance becomes increasingly important.

The report includes governance among the capabilities expected from modern SOAPs, including security guardrails, role-based access control, compliance enforcement, and encryption.

This is not merely a security requirement; it’s a prerequisite for increasing autonomy.

An organization is unlikely to allow AI agents to initiate actions across production infrastructure unless it can answer fundamental questions:

• Who or what initiated the action?

• Was the action authorized?

• Which systems were affected?

• What policies governed the execution?

• Can the activity be audited?

• When should human approval be required?

As a result, autonomous operations should not be confused with uncontrolled operations.

The goal is governed autonomy: progressively increasing automated decision-making while maintaining clear policies, observability, auditability, and human control where appropriate.

For highly regulated enterprises and organizations running mission-critical workloads, that distinction is essential.

Together, these trends are changing how organizations evaluate automation platforms.

Traditional workload automation comparisons often focused heavily on scheduling features, supported platforms, job volumes, and operational reliability. Those requirements remain important, but evaluations now need to go further.

IT leaders should consider whether a platform can:

• Orchestrate complete workflows across mainframe, distributed, cloud, and SaaS environments

• Connect existing enterprise applications without creating new automation silos

• Coordinate workloads, infrastructure and data pipelines

• Support event-driven as well as scheduled automation

• Provide workflow-aware observability and operational context

• Detect problems and support intelligent remediation

• Govern self-service and AI-initiated actions

• Support GenAI-assisted workflow creation and troubleshooting

• Integrate AI agents into controlled enterprise workflows

• Scale without compromising mission-critical reliability

• Support modernization while protecting existing technology investments

Gartner stresses that the Magic Quadrant should start a vendor evaluation, not be the final decision. A Leader is not automatically the best fit for every organization. Buyers should compare each provider’s strengths and challenges against their own priorities.

Beta Systems has been positioned as a Leader in the 2026 Gartner Magic Quadrant for Service Orchestration and Automation Platforms for the second consecutive year.

This recognition reflects the direction we are pursuing: helping enterprises move toward more intelligent, connected, and increasingly autonomous IT operations.

Our vision is to redefine hybrid IT operations by bringing together intelligent orchestration and automation, data management, and end-to-end observability across applications and platforms. At the center of this strategy is ANOW!® Suite.

ANOW!® Automate coordinates workloads and dependencies across heterogeneous IT environments. ANOW!® Observe adds real-time operational visibility, analytics, and context. Together, they help enterprises move from fragmented automation to an integrated operational model where insight and execution are increasingly connected.

ANOW! Suite helps organizations:

1. Unify workloads and workflows across complex hybrid environments

2. Automate dependencies between systems, teams, and technologies

3. Connect observability insights with automated responses

4. Introduce AI-assisted operations within defined governance boundaries

5. Improve resilience and reduce manual intervention

6. Protect existing investments while modernizing incrementally

7. Coordinate mission-critical processes with enterprise-grade control

This approach is particularly relevant as AI begins to participate more directly in enterprise operations.

The path to autonomous operations requires an operational foundation that can provide AI with trusted data and context, then turn intelligent decisions into governed, observable actions across the systems the business depends on. We believe that convergence will define the next generation of enterprise IT operations.

The 2026 Gartner Magic Quadrant for SOAP points toward a market undergoing a fundamental transition.

Workload automation is expanding into end-to-end orchestration; hybrid environments are becoming the default. Observability is increasingly connected to execution, and GenAI is moving inside operational workflows. AI agents are becoming automation initiators. Therefore, governance is becoming essential to scaling autonomy safely.

The direction is clear: enterprises will need a common operational layer that connects systems, workflows, data, telemetry, and AI. SOAP is increasingly positioned to become that layer.

For Beta Systems, the next step is to continue bringing these capabilities together, building on more than 40 years of experience in mission-critical IT while helping enterprises prepare for a future where operations are more automated, intelligent, adaptive, and autonomous.

_{Gartner, Magic Quadrant for Service Orchestration and Automation Platforms, Hassan Ennaciri, Daniel Betts, Chris Saunderson, 5 August 2026.}

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