## What Hyperautomation Actually Is
Gartner coined the term, but the concept is simple: hyperautomation is the disciplined, business-driven approach of rapidly identifying, vetting, and automating as many processes as possible. It's not a single technology. It's a combination of RPA, AI, machine learning, process mining, and business process management working together under a coherent strategy.
The "hyper" prefix matters because this isn't about automating one or two processes in isolation. It's about building an organisational capability to continuously discover automatable processes, prioritise them, automate them, measure the results, and then identify the next batch. It's automation as an ongoing practice, not a one-time project.
## The Discovery Phase: Finding What to Automate
The biggest mistake organisations make with hyperautomation is starting with the automation rather than starting with the discovery. Before you write a single line of RPA code or deploy a single AI model, you need to understand your processes deeply enough to know which ones are worth automating and what the variation in those processes actually looks like.
Process mining is the technology that makes this systematic. Tools like Celonis, UiPath Process Mining, and Signavio analyse your event logs from ERP systems, CRM systems, and other sources to produce actual maps of how your processes run in practice — not how the process documentation says they should run, but how they actually run, with all the variations and exceptions and workarounds that have evolved over the years.
What you typically find is illuminating and often uncomfortable. The process you thought was standardised turns out to have 47 variants. The task you thought took 10 minutes actually averages 23 minutes and spikes to 2 hours for certain cases. The handoffs between teams that looked clean on paper actually involve a lot of informal email chasing that nobody documented.
This discovery work is what separates hyperautomation programmes that deliver transformative results from those that automate a few processes and then stall.
## Prioritisation: Not Everything Should Be Automated
One of the most important skills in hyperautomation is knowing what not to automate — at least not yet. The prioritisation matrix most successful teams use looks at two dimensions: automation potential (how structured, rule-based, and stable is the process?) and business impact (how much time/money/quality improvement would automation deliver?).
High potential, high impact processes go into your immediate pipeline. High impact, lower potential processes go into your AI-assisted pipeline where you're combining intelligent automation with human judgement. Low impact processes, even if very automatable, often aren't worth the effort unless they're building blocks for something larger.
Stability matters enormously. A process that changes frequently due to regulatory requirements or business changes is a poor automation candidate — you'll spend more maintaining the automation than you save. Look for processes that are structurally stable and unlikely to change significantly in the next three years.
## The Technology Stack for Hyperautomation
A mature hyperautomation stack in 2026 typically includes: a process intelligence layer (process mining and task mining tools), an automation execution layer (RPA platform plus API integration tools), an AI and ML layer (document AI, NLP, predictive models), an orchestration layer (process orchestration tools that coordinate across automation technologies), and a management and monitoring layer (analytics, anomaly detection, governance tools).
The vendors in this space have been consolidating rapidly. UiPath, Automation Anywhere, and Microsoft Power Automate all now include process mining, AI capabilities, and orchestration in their platforms. For organisations starting fresh, choosing one of these platforms and going deep is usually better than assembling best-of-breed components from multiple vendors.
## Measuring Hyperautomation Value
Hyperautomation programmes that maintain executive support and sustained investment are the ones with rigorous measurement frameworks. Track your automation rate (percentage of process volume handled without human intervention), straight-through processing rate, error rate before and after automation, average handling time, and total cost per transaction.
The total cost per transaction metric is particularly powerful because it captures the full picture — not just the labour cost of the task itself, but the error remediation, rework, and management overhead that comes with manual processes. When you automate well, all of those drop significantly.
*Lara IT Solutions builds hyperautomation programmes for UK enterprises. Contact us on 0330 043 1930.*