What is hyperautomation?

Definition

Hyperautomation is a business-driven approach that combines multiple automation technologies to automate as many business processes as possible across an organization. The term was coined by Gartner in 2019, and Gartner named hyperautomation a top strategic technology trend for 2020, 2021, and 2022. Unlike traditional automation that targets individual tasks or workflows, hyperautomation treats automation as an organizational discipline, combining RPA, AI/ML, process mining, iPaaS, low-code platforms, and intelligent document processing into a coordinated technology stack.

Technology Stack

Hyperautomation is not a single product but a convergence of multiple technologies:

How Hyperautomation Differs from Plain Automation

Traditional automation typically targets a single process or task type with one technology. Hyperautomation differs in three ways:

• Breadth: Aims to automate end-to-end across departments, not just isolated tasks
• Intelligence: Incorporates AI for decision-making, natural language processing, and predictive analytics rather than relying solely on rule-based logic
• Discovery: Uses process mining and task mining to systematically identify automation opportunities rather than relying on manual process audits

Hyperautomation Maturity Model

• Level 1 — Task automation: Individual tasks automated with single tools (e.g., an RPA bot that copies data between systems)
• Level 2 — Process automation: End-to-end processes automated with workflow orchestration and multiple integrated tools
• Level 3 — Cross-functional automation: Automation spans departments with shared data models, central governance, and connected workflows
• Level 4 — Autonomous operations: AI-driven systems discover, build, and optimize automations with minimal human intervention

Most organizations in 2024 operate between Level 1 and Level 2. Level 4 remains largely aspirational.

Key Vendors in the Hyperautomation Ecosystem

• UiPath: RPA platform expanded into process mining, test automation, and AI capabilities
• Automation Anywhere: RPA with integrated AI and analytics, positioned for enterprise hyperautomation
• Microsoft Power Platform: Low-code suite (Power Automate, Power Apps, Copilot Studio) targeting broad organizational automation
• Celonis: Process mining leader, provides the discovery layer for identifying automation targets
• ABBYY: Intelligent document processing, providing the data extraction layer for document-heavy processes

Criticisms and Practical Considerations

Hyperautomation has faced criticism as a marketing term that repackages existing automation concepts under a new label. The practical challenges are significant: implementing a full hyperautomation stack requires coordinating multiple vendors, managing complex integrations, training diverse teams, and establishing governance across technologies. Gartner projected the hyperautomation software market would reach $1.04 trillion in cumulative spending by 2026, a figure that includes all automation software and is broader than the term's original scope. Organizations considering hyperautomation should evaluate their current automation maturity before investing in a multi-technology approach, as many organizations have not yet fully utilized single-technology automation.