Hyper Automation

Hyper automation refers to the integration of various automation technologies to enhance and optimize business operations. This approach aims to automate as many tasks as possible, including those traditionally carried out by people. While it is commonly linked to the automation of routine tasks, it can also be utilized for more intricate business processes.

The advantages of a well-implemented hyper automation strategy include:

• Enhanced Efficiency: Hyper automation allows businesses to automate repetitive and manual tasks, leading to greater operational efficiency. By offloading these tasks, employees can dedicate their time to higher-value work that demands creativity, critical thinking, and problem-solving skills.

• Boosted Productivity: Automation streamlines the time and effort needed to complete tasks, resulting in heightened productivity. With hyper automation, organizations can optimize and refine their end-to-end processes, eliminating delays and minimizing manual errors, enabling employees to achieve more in a shorter timeframe.

• Cost Reduction: Automating tasks and processes helps organizations cut costs related to manual labor, human mistakes, and the need for rework. Hyper automation reduces the necessity for routine, labor-intensive tasks and lessens the chances of errors, ultimately leading to long-term savings.

• Increased Accuracy and Quality: Automation promotes consistency and precision in task execution. By reducing human involvement, hyper automation mitigates the risk of mistakes stemming from fatigue, distractions, or lapses in focus, resulting in higher quality, accuracy, and reliability in business operations.

• Scalability and Flexibility: Hyper automation allows organizations to expand their automation efforts across various departments and functions. It offers the agility to manage higher workloads without a corresponding rise in resources. Furthermore, as it utilizes a mix of technologies, hyper automation enables organizations to adapt their automation strategies to evolving business needs and market conditions.

• Data-Driven Insights: Automation produces vast amounts of data that can be analyzed for valuable insights. Hyper automation can utilize AI and ML technologies to uncover significant patterns, trends, and insights from the data generated during the automation process. These insights can inform decision-making, enhance process optimization, and highlight areas for improvement.

• Enhanced Customer Experience: By automating processes, organizations can significantly improve the customer experience. Automation leads to quicker response times, fewer errors, and consistent interactions with customers, resulting in greater customer satisfaction, loyalty, and retention.

Technologies that facilitate hyper automation encompass a range of solutions, including:

• Robotic Process Automation (RPA)

• Business Process Automation (BPA)

• Digital Process Automation (DPA)

• Low-Code Development Platforms

• Virtual Assistants and 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 the majority, of these technologies are enhanced by or linked to artificial intelligence (AI) and machine learning (ML) tools that can automate subsequent steps in workflows and conduct intelligent analyses.

What are some examples of hyper automation applications?

Processes that are high-volume and repetitive, yet still require manual effort from multiple individuals across various departments, are ideal for hyper automation.

It's advantageous to concentrate on processes that are time-critical (meaning quicker for machines), necessitate audit trails, or would be unfeasible without automation because of the large amounts of data involved, particularly those that gain from AI and ML analysis.

Here are a few examples of hyper automated processes:

• Data Extraction: Optical Character Recognition (OCR) technology enables the extraction and transformation of data from scanned or photographed documents and PDFs into a format that machines can read. This data can subsequently be verified and enhanced by machine learning tools using both internal and external databases before being submitted to relevant systems like Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP). Hyper automated data processing is beneficial for various sectors such as accounting, supply chain management, insurance, legal services, and others that manage significant volumes of both structured and unstructured documents.

• Customer Service: In a customer call center, the automation of service call transcriptions can be achieved, followed by the application of Natural Language Processing (NLP) to refine these transcriptions. The organization can then leverage NLP to identify patterns within these records, as well as chatbot interactions, to reveal emerging issues, assess sentiment, enhance self-service content on their website or automated phone services, assist in agent training, and optimize chatbot functionality.