While it may sound like just another buzzword, hyperautomation is one of the most important and powerful strategic technology trends in existence.

What is hyperautomation?

At its core, hyperautomation is the synergy produced by combining robotic process automation (RPA) and artificial intelligence / machine learning (AI/ML). More broadly, hyperautomation refers to an effective combination of complementary sets of tools that can integrate functional and process silos to automate and accelerate business processes. [1]

This involves the orchestrated use of multiple technologies, tools or platforms, such as  machine learning & AI, event driven software architecture, RPA, iPaaS, packaged software, and other types of decision, process, and/or task automation tools.

Why cookie cutter automation fails to deliver

Most modern companies already have some form of automation, from packaged software to SaaS solutions to more advanced RPA. However, the failure to effectively combine solutions leaves a significant margin of improvement on the table.

Single tool, siloed, or poorly implemented automation strategies fall short due to:

• Automating inherently inefficient processes results in unrealized benefits.
• Processes automated in parallel (or in a vacuum) cannot synergize with each other to improve outcomes.
• Lack of monitoring and analysis prevents complex, continuously evolving process optimization.
• The failure to automate more complex processes, such as social media sentiment analysis, inhibits reaction time.

How hyperautomation solves business challenges

The goal of hyperautomation is to improve business performance in a truly holistic manner. It also enables automation to penetrate into processes that businesses could not automate in the past. Here are some ways hyperautomation works.

Improves Accuracy

To ensure accuracy, data can be scraped from web applications, such as HR, Legal, Purchasing, or Contracting. The scraped data is then processed through natural language processing (NLP) methods, such as text cleaning & transformations, named entity recognition, and word frequency within NuMantra’sML/AI application to predict the “accuracy or fit” of the data. The results can be analyzed and used to also make changes to the original data. Using a pre-built API, the data is then re-loaded to the third-party application or used for further analysis.

The end result is application-based data gets automatically double-checked for accuracy.

Accelerates Reaction Times

We all realize by now the importance of social media customer feedback. How your client base reacts to reviews and opinions can directly affect brand image and even revenue streams.

By using NLP, datasets can be readied for ML models to analyze sentiment impact on historical transactional, planning, or budgeting data. Developing and implementing these types of models enables organizations to react faster to current and future events.

Reduces Cost

ML models can be used to analyze transactional (PO processes in SAP) or support processes (ServiceNow) and ensure optimal process performance. Cost gets driven down in the following manner:

1. Discover and analyze process bottlenecks with process mining.
2. Combine multiple process inputs using NLP & ML/AI to derive optimal performance measures in multiple areas (PO Creation, Order Processing, Credit & Risk Management, Contracting, Manufacturing, Logistics, Accounts Receivable & Accounts Payable, Security, and Infrastructure Management).
3. Generate results that predict process performance for different parameters.
4. Implement changes and automate with RPA to improve business outcomes and lower costs.

Automates More Complex Processes

Cognitive Automation refers to RPA tools and solutions that leverage AI technologies, such as optical character recognition (OCR), text analytics, and machine learning to improve workforce and customer experience.

For example, from any PDF or scanned document (legal, HR, purchasing, contracting, or customer facing functions) data can be extracted, analyzed, and validated using an ML/AI engine and prediction models. This can be combined with NLP for data cleansing and transformation. The data then gets automatically loaded to an ERP system using RPA.

This is how hyperautomation penetrates and automates more complex, nuanced business processes.

Deepen Process Insight

Automated process improvement is enabled by ML/AI analysis and computer vision. This begins with process discovery using system logs to determine which events conform (or fail to conform) with standard operating procedure (SOP).

After simply uploading a CSV, FTP or database extract, process mining automatically analyzes and visualizes the data. Any deviation identified is then analyzed and improved upon based on ML/AI inputs. Computer vision technology is used to generate automated scripts of the improved processes.

These steps can be repeated periodically as processes evolve over time for continuous process monitoring and improvement.

Accelerating Automation with Hyperautomation

Hyperautomation augments and accelerates the automation of business processes by implementing Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA). Nearly any repetitive task, even more complex processes, can be automated.

Hyperautomation even enables the discovery of new processes that can be automated as well as providing the tools for constant process monitoring and improvement.

Want to learn more? Set up a demo with NuMantra and find out how hyperautomation can propel your business performance to the next level.