What Is Hyperautomation?

Definition

Hyperautomation is a business strategy that combines multiple automation technologies — including artificial intelligence, machine learning, robotic process automation (RPA), integration platforms (iPaaS), and process mining — to automate as many business processes as possible end-to-end. Coined by Gartner in 2019 and named a top strategic technology trend for three consecutive years (2020-2022), hyperautomation moves beyond automating individual tasks to transforming entire operational workflows across departments.

As of 2026, hyperautomation has transitioned from a technical trend to a boardroom-level priority, with organizations treating it as a discipline rather than a project.

Component Technologies

How Hyperautomation Differs from Basic Automation

Standard automation targets a single task with a single tool: an RPA bot that copies data between systems, or a Zapier workflow that syncs CRM records. Hyperautomation differs in three dimensions:

• Scope: Aims to automate end-to-end processes spanning multiple departments, not isolated tasks
• Intelligence: Incorporates AI for decision-making and adaptive behavior 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

Enterprise Adoption Patterns

Organizations typically progress through a maturity curve:

1. Task automation (Level 1): Individual tasks automated with single tools (e.g., one RPA bot)
2. Process automation (Level 2): End-to-end processes automated with orchestrated tools
3. Cross-functional automation (Level 3): Automation spans departments with shared governance
4. Autonomous operations (Level 4): AI-driven systems discover, build, and optimize automations with minimal human intervention

Most enterprises in 2026 operate between Level 2 and Level 3. Level 4 remains largely aspirational.

Practical Examples

• Procure-to-pay: Process mining identifies bottlenecks, RPA handles invoice data entry, AI validates line items, iPaaS connects ERP to payment systems, BPM orchestrates approvals
• Employee onboarding: Low-code apps collect new hire information, iPaaS provisions accounts across 10+ systems, RPA handles benefits enrollment, AI chatbot answers policy questions
• Customer service: Process mining maps case resolution paths, AI classifies and routes tickets, RPA retrieves account data, low-code app provides agent workspace

Criticisms and Practical Considerations

Hyperautomation has faced valid criticism as a marketing term that repackages existing automation concepts under a unified label. The practical challenges are significant: implementing a full technology stack requires coordinating multiple vendors, managing complex integrations, training diverse teams, and establishing governance across technologies. Gartner projected cumulative hyperautomation software spending would reach $1.04 trillion 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. Many organizations have not yet fully utilized single-technology automation.

Editor's Note: A mid-market financial services client asked us to evaluate their "hyperautomation readiness" in late 2025. They had 14 separate automation tools with zero orchestration between them. After consolidating around three core platforms — Make for workflow, UiPath for RPA, and Fivetran for data — they reduced manual handoffs by 73% within 4 months. The lesson: hyperautomation is less about adding more tools and more about connecting the ones already in place.