What Is Hyperautomation?

• Increased efficiency: Hyperautomation enables organizations to automate repetitive and manual tasks, increasing operational efficiency. By automating these tasks, employees can focus on higher-value activities that require creativity, critical thinking, and problem-solving.
• Improved productivity: Automation reduces the time and effort required to complete tasks, resulting in improved productivity. With hyperautomation, organizations can streamline and optimize end-to-end business processes, eliminating bottlenecks and reducing manual errors. This allows employees to accomplish more in less time.
• Cost savings: By automating tasks and processes, organizations can reduce costs associated with manual labor, human errors, and rework. Hyperautomation eliminates the need for repetitive, time-consuming activities and minimizes the likelihood of errors, leading to cost savings in the long run.
• Enhanced accuracy and quality: Automation ensures consistency and accuracy in performing tasks. By minimizing human intervention, hyperautomation reduces the potential for errors caused by fatigue, distractions, or lack of attention. This leads to improved quality, precision, and reliability in business processes.
• Scalability and agility: Hyperautomation enables organizations to scale their automation initiatives across departments and functions. It provides the flexibility to handle increased volumes of work without a proportional increase in resources. Additionally, as hyperautomation leverages a combination of technologies, organizations can adapt and evolve their automation capabilities to meet changing business needs and market demands.
• Data-driven insights: Automation generates large amounts of data that can be analyzed to gain valuable insights. Hyperautomation can leverage AI and ML technologies to extract meaningful patterns, trends, and insights from the data collected during the automation process. These insights can be used to drive informed decision-making, optimize processes further, and identify opportunities for improvement.
• Improved customer experience: Organizations can enhance the overall customer experience by automating processes. Automation enables faster response times, reduces errors, and ensures consistency in customer interactions. This leads to improved customer satisfaction, loyalty, and retention.

Examples of technology that supports hyperautomation

Common hyperautomation technology includes:

• Robotic process automation
• Business process automation
• Digital process automation
• Low-code platforms
• Virtual assistants/conversational AI
• Natural language processing (NLP)
• Integration platform as a service (iPaaS)
• Optical character recognition (OCR)
• Digital twins
• Process mining
• Task mining.

Many, if not most, of these technologies, are fortified with or connected to tools with artificial intelligence (AI) and machine learning (ML) algorithms that can automate next steps in processes and perform intelligent analysis.

What are some hyperautomation use cases?

High-volume, repetitive processes that still involve manual work by several people across departments are good candidates for hyperautomation.

It is also beneficial to focus on processes that are time-sensitive (i.e., faster for machines), require audit trails, or would not even be possible without automation due to data volume, especially those that benefit from AI and ML analysis.

Here are a few examples of hyperautomated processes:

• Data extraction: OCR can extract and convert data into a machine-readable format from scanned or photographed documents and PDFs. That data can then be validated and enriched against internal and third-party databases by machine learning tools and submitted to the proper systems, such as CRM and ERP. Hyperautomated data processing can be used by accounting departments, supply chain, insurance companies, legal firms, and any others that handle large amounts of documents with both structured and unstructured data.
• Customer service: A customer call center may automate the transcription of service calls and then use NLP to correct the transcriptions. The company can then use NLP to look for patterns in these transcriptions – along with chatbot records — to uncover trending issues, measure sentiment, inform better self-service topics for the website or automated telephony options, help with agent training, and improve chatbot performance.