Hyperautomation Strategy: Scaling Automation Across the Enterprise
Hyperautomation — the disciplined approach to identifying, vetting, and automating as many business processes as possible at enterprise scale — has evolved from a Gartner-coined buzzword to a core enterprise capability. According to Gartner's June 2026 Hyperautomation Market Analysis, organizations with mature hyperautomation programs are automating 40-55% of eligible business processes, up from 15-20% in 2023, and achieving compounding returns as each automated process frees resources for the next wave of automation.
Hyperautomation is not a technology — it is a strategic discipline for scaling automation across the organization. It combines multiple technologies (workflow automation, AI, RPA, low-code development, process mining) within a governance framework that ensures automation investments are directed toward the highest-value opportunities and executed with appropriate quality and control.
Building a Hyperautomation Program
Discovery: Finding Automation Opportunities
The first challenge is identifying which processes to automate. Process mining analyzes system logs to discover actual process flows, identify bottlenecks, and quantify automation potential. Task mining captures user interactions to identify repetitive tasks suitable for automation. Process assessment workshops engage business stakeholders to surface pain points and automation candidates. The output is a prioritized automation pipeline based on feasibility, impact, and strategic alignment.
Delivery: Building Automations at Scale
Hyperautomation delivery requires a factory model — standardized methods, reusable components, and clear quality gates that enable parallel automation development by multiple teams. Low-code platforms are essential to this model because they enable business analysts and citizen developers to build automations alongside professional automation engineers, dramatically increasing delivery capacity.
Governance: Ensuring Quality and Control
As automation scales, governance becomes critical: automated testing validates automations before production deployment, monitoring dashboards provide real-time visibility into automation health, exception handling processes ensure that automation failures are detected and resolved quickly, and change management procedures ensure that changes to underlying systems don't silently break dependent automations.
Optimization: Continuous Improvement
Hyperautomation creates a continuous improvement flywheel: process analytics identify further optimization opportunities, AI analyzes automation execution data to suggest improvements, and automated processes generate data that feeds the next round of discovery.
Why Informat Enables Hyperautomation
Informat's unified platform is purpose-built for hyperautomation: process mining and task capture for discovery, low-code workflow automation for delivery, enterprise governance for control, AI-powered analytics for optimization, and a platform foundation that standardizes the entire automation lifecycle.
Conclusion
Hyperautomation represents the industrialisation of business process automation — moving from isolated automation projects to a systematic, scalable capability for transforming how work gets done. Organizations that build this capability will automate faster, at higher quality, and with greater business impact than those still pursuing automation project by project.