What is hyperautomation?

Hyperautomation is an organizational approach aimed at systematically identifying and automating as many business processes as possible, instead of automating one process at a time on request. The term was popularized by Gartner, which included it among its top strategic technology trends for 2020: it does not describe a new technology, but a discipline that combines several existing tools, from discovering processes through process mining to orchestrating them with RPA, iPaaS, AI and low-code platforms, while keeping an inventory of what has been automated and measuring the result. The difference from an isolated automation project lies in governance: usually a center of excellence sets priorities, standards and shared metrics, instead of letting each department pick its own tool with no coordination. It is a word that shows up constantly in automation-suite vendor marketing, where it ends up meaning simply "our product," losing the organizational meaning Gartner originally gave it.

See also: Process mining · A technique that rebuilds how a business process really runs, starting from the event logs of enterprise systems.

What changes compared to a one-off automation project

The useful part of the concept is the process that comes before automating. Before automating anything you have to discover what is actually worth automating, and that is where process mining comes in: it reconstructs the real flow of a process from the logs of the systems that run it, instead of trusting the procedure written on paper, which is often different from how people actually work. Once the process is discovered, execution can stay pure RPA on a mechanical step, move up to IPA where reading a document is required, or go through an iPaaS when the problem is getting two systems that share no common API to talk to each other. Hyperautomation does not replace any of these techniques: it puts them under a shared inventory, with a center of excellence that decides where to invest, keeps two departments from building the same automation twice, and measures the outcome to stop what does not pay off instead of letting it run forever.

An enterprise example

An industrial group with twelve plants runs its ERP logs through process mining and discovers that approving a purchase order takes seven days on average, and in a third of cases bounces back for a missing field. The first, isolated fix is an RPA bot that copies fields from one form to another, which shortens the mechanical step but does not touch the waiting time. Under a hyperautomation approach the problem is handled as a portfolio: process mining pinpoints where the flow actually stalls, an iPaaS connector pulls the missing field directly from the supplier's system instead of asking an operator for it, and only the remaining step, the final check, stays a human task with a simpler interface. Approval time drops because the missing field that sent orders back now comes straight from the supplier's system.

Why it matters for decision makers

Hyperautomation is worth keeping as a reminder of two things an isolated RPA project easily forgets: nothing gets automated before someone has measured what is actually happening, and an automation with no owner and no ongoing metrics turns into invisible technical debt the moment whoever built it changes role. It is worth much less as a product name: when a vendor uses it to describe a single suite, they are selling a tool under the name of a discipline, and it is the discipline that decides whether the investment pays off. The useful question to ask a vendor is not whether the platform is "hyperautomation," but whether it helps discover processes before automating them and measure the outcome afterward.