Hyperautomation vs RPA technology, any difference?
Robotic process automation brought tangible changes, but leaders are now going towards greater innovation. Hyperautomation, a new approach, is filling the gaps by integrating multiple technologies – RPA, LCNC (Low-Code/No-Code platforms), AI, ML – to empower large-scale transformation.
Hyperautomation emphasizes two things:
- Automation expansion, where everything that can be optimized, is optimized
- Cognitive capabilities – discovery, analysis, design, execution, monitoring, reassessment
What is hyperautomation about?
By targeting enterprise-wide operations rather than single processes, hyperautomation represents a strategy to transform disconnected workflows into optimized, holistic systems.
Hyperautomation describes a strategy of using multiple technologies, (hyper)automating everything at speed. Comprehensively enacting the concept of mimicking human action, hyperautomation reshapes our viewpoint on technology revolutionizing workspaces.
But how does this look like in real-world business scenarios?
Let’s see:
- A patient intake system is handling form collection and routing by analyzing the insurance and urgency
- The patient’s medical history, lab results, and other key details are retrieved by the RPA bot
- The patient’s health metrics are analyzed by the AI model to suggest possible conditions and programs
- After review and approval, the processes coming afterwards – data updates, appointment scheduling – are handled by another RPA bot
Hyperautomation vs RPA technology
Where basic automation tackles simple tasks by using rule-based scripts, hyperautomation goes way beyond.
Robotic process automation (RPA) is designed to handle repetitive tasks, for example data entry and invoicing. Hyperautomation, on the contrary, is leveraging multiple technologies to cover as much as possible at speed – robotic process automation and artificial intelligence (machine learning) in particular.
| From automation | To hyperautomation | |
| Definition | Simple automation of processes typically performed by humans by using rule-based decision-making | A strategy that combines simple automation with more advanced techniques to tackle as many routine processes as possible |
| Decision-making | If-then logic, rule-based basis | AI techniques to learn and adapt |
| Platforms | Traditional environments and vendor-specific RPA studios | Low-code/no-code platforms and unified automation workbenches |
| Technologies | RPA bots, simple scripting and macros | RPA bots, AI & ML models, process mining, digital twins |
| AI involvement | Either minimal or none | Core element, in particular predictive analytics, computer vision, and natural language processing |
| Cloud integration | Often on-premises or standalone | Either native or hybrid with scaling |
| Flexibility | Any changes through scripting and updates are manual | The workflows evolve dynamically through retraining AI models |
| Scalability | Quite limited to only individual tasks and workflows | Enterprise-wide orchestration across systems and departments |
But there’s more than one technology worth mentioning.
The main differences between automation technologies and approaches:
- Robotic process automation (RPA): rule-based actions without intelligence
- Business process automation (BPA): mainly focused on efficient process design and automation
- Intelligent process automation (IPA) additionally appends cognitive abilities
- Hyperautomation approach: all about advanced technology to deliver enterprise-wide automation
Hyperautomation technologies
Robotic process automation (RPA)
Robotic process automation solutions – the reliable digital workforce within every hyperautomation strategy. They replicate simple actions – copying/pasting information, clicking buttons, navigating interfaces, and more – without changing underlying systems.
Key functions:
- Automate repeated, rule-based tasks
- Integrate systems
- Reduce errors in everyday, high-volume operations (claims processing, payroll management)
- Increase speed of execution across operations
For example, in the healthcare industry, that would involve logging into systems to gather patient information.
Low-code/no-code (LCNC)
Low-code/no-code platforms are irreplaceable automation accelerators in larger hyperautomation strategies. They democratize the creation of workflows and applications by empowering technical and non-technical users to design and deploy automation scripts by using visual tools.
Key functions:
- Allow building automation flows without coding
- Facilitate prototyping & iteration (logic, forms, dashboards, integrations)
- Reduce dependency by providing pre-built templates, visual builders, and connectors
- Increase compliance by enforcing version control, best practices, and governance
For example, in the healthcare setting, any employee can build automation flows to guide software developers.
Artificial intelligence
Artificial intelligence is the “brain” element that turns rule-based scripts into adaptive, orchestrated platforms. It allows to simulate human reasoning and make context-aware decisions.
Key functions:
- Interpret information – free-text input, scanned documents, images, videos, and emails
- Understand speech to streamline customer support and even doctor-patient interaction
- Extract insights from imaging, medical forms, and others
- Allow decision-making through analysis
For example, the algorithm can analyze radiology reports to identify health trends and detect at-risk patients.
Machine learning
Machine learning is the adaptive engine that allows ongoing improvement without needing additional training. It learns to uncover important patterns and make accurate predictions.
Key functions:
- Detect patterns, including anomalies and trends
- Predict outcomes – patient deterioration, fraud schemes, and others
- Optimize workflows by learning from interactions
- Allow personalization by analyzing user behavior and preferences
For example, in a telehealth system, the model can learn from consultations to recommend patient redirection.
When should you use hyperautomation solutions?
When robots make sense
Automation scripts are perfect for high-volume, rule-based tasks & processes being performed by employees. This approach is best-applied for simplistic, well-defined tasks and processes that don’t need decision-making.
- The goal is efficiency and minimal human involvement in performing repetitive activities
- The processes follow structures, rule-driven workflows and require no judgement or adaptation
- There is no need for advanced data manipulation and analytics – the processes are mechanical
- The processes involve interactions with interfaces – entering values, clicking buttons, and more
Beyond robots: hyperautomation connecting the dots
Hyperautomation integrates various techniques, thus allowing to handle business operations from start to end. This approach is best-suited for sophisticated, enterprise-wide workflows.
- The goal is innovation and end-to-end business optimization
- The processes include multiple interconnected steps, often spanning across departments and systems
- There is a need for decision-making
- The processes must adapt to exceptions, real-time inputs, and changes in rules
- The integration with systems, data sources, and services is required to achieve flow continuity
- You’re aiming to scale automation across the enterprise
How we can help
As more business leaders move towards hyperautomation toolchains, right partners can make the difference.
We at Abto Software, have both the knowledge and experience to deliver automation tools and toolchains. Business discovery, proof-of-concept, prototyping, software development & integration, even modernization – we have the skills.
Our services:
- AI development
- RPA services
- .NET development
- ASP.NET development
- Web development
- Mobile development
- Cloud services
- Custom product software development
Our expertise:
FAQ
Hyperautomation, if put simply, is a modern approach of minimizing manual work across divisions and systems. Hyperautomation is carried out by integrating multiple technologies – automation, low-code-no-code, AI, ML – to discover, automate, execute, and orchestrate complex operations.
- Robotic process automation handles rule-based tasks by mimicking user behavior
- Business process automation focuses on streamlining entire processes for efficiency
- Intelligent process automation applies AI capabilities to support informed decision-making
- Hyperautomation combines multiple technologies to handle enterprise-wide workflows
Yes, RPA takes over in handling simple activities, which don’t necessarily require human oversight and partake. But RPA is only one part of the bigger picture.
- In the healthcare industry, for optimized patient intake – by collecting and analyzing patient records, and prioritizing before consultation
- In the retail domain, for smart inventory management – by tracking and forecasting product demand, and trigger supplier orders
- In the logistics sector, for end-to-end shipment tracking – by overseeing and bridging involved systems to update the statuses, reroute deliveries, and trigger customer alerts
- In the manufacturing sector, for predictive equipment maintenance – by observing machinery statuses and schedule timely inspections when predicting potential failures