Digital matrix

The Future of Work: Climbing towards Hyperautomation

Automation is one of the most catalytic technologies in today's enterprise business. As a result, our workflows, analysis, and decision processes have accelerated to new heights over the past decade.

  • Nexus Cognitive Chief Technology Officer, Andy Nardo
    Andy Nardo

Let's look towards the future of business automation and how building a strong foundation of technologies can allow businesses to climb from traditional RPA to Intelligent Automation and ultimately HyperAutomation. 


To better understand this evolution, It's best to think of automation in terms of a maturity continuum progressing from "doing" to "thinking" and from process-driven to data-driven. 

The ultimate goal of automation is to unlock efficiencies and enable employees to move beyond the mundane and focus on more profound business outcomes.

A Strong Foundation: Robotic Process Automation (RPA)

RPA uses robots or bots that interact with an application similar to a human user. 

RPA is a noninvasive integration technology used to automate routine, repetitive, and predictable tasks through orchestrated UI interactions that emulate human actions.

Without writing code or modifying existing applications in any way, a process analyst can identify elements of an application that can become automated. Using these elements, they can apply data-driven rules using an easy-to-use low-code interface to train the robot. 

Use Case: Automating Accounts Receivable

Automating past due communication for an accounts receivable department represents a great use case of RPA. These automation steps would include evaluating specific fields in the application and then following those rules such as checking the days an invoice is outstanding. For instance, if an office has an invoice that is 30 days outstanding, a reminder email can be automatically drafted and sent. If the invoice is 45 days outstanding, a warning email could be drafted and sent. If the invoice is 60 days outstanding, the customer’s account could be marked past due in the system with follow-up ticket generated for the account team to take action.

Where is RPA Used?

RPA is compatible with any application that presents an interface for user interaction. 

This includes: 

  • Windows applications
  • Web pages
  • Legacy mainframe applications
  • Java apps
  • ERP platforms

There are two main types of RPA – assisted and unassisted:


  • Bots deploy to an individual desktop. In Assisted RPA, a human worker carries out some tasks while relying on the bot to tackle the process's repetitive or technically complex parts. Assisted RPA is also known as attended execution.


  • Bots are deployed on a centralized server, allowing unattended execution. This type of RPA solution can automate end-to-end tasks and workflow scheduling from a central point of control. 

RPA provides a low-code / no-code mechanism to apply rules, extract data, and execute actions across many existing applications and corporate systems. These capabilities provide the foundational layer for Intelligent Automation and Hyperautomation. 

These concepts require an RPA platform to permit interaction with the applications without programming the interactions. Without RPA, automating existing applications and corporate systems would require multiple point-to-point connections to be developed and integrated into artificial intelligence (AI) solution.

The Capgemini Research Institute estimates that large organizations will achieve cost savings of around USD 500 billion through IPA in 2022.

The Next Step: Intelligent Automation (IA)

Intelligent automation (also referred to as Cognitive Automation) links Artificial Intelligence (AI) with the interactive capabilities of Robotic Process Automation (RPA). 

While RPA focuses on automating repetitive and, often, rules-based processes, intelligent automation incorporates (AI) technologies like machine learning, natural language processing, and intelligent document processing. Because AI simulates types of human intelligence, IA can process higher-function tasks that require some level of reasoning, judgment, decision, and analysis.

Gartner predicts that, by 2022, 80 percent of RPA-centric automation implementations will derive their value from complementary technologies

Use Case: Invoice Processing

Anyone with experience processing invoices will explain that nearly every company has unique formats for sending invoices to their customers. These non-standardized invoices come in different file formats. In addition, they have a dynamic data structure that requires staff to individually review each invoice before inputting it into their accounting and ERP systems.

Depending on invoice volume, this activity may require dedicated resources and distract staff from higher-value activities. 

Implementing Intelligent Automation will significantly reduce staff time while concurrently improving data quality and reducing latency. 

See how we helped a large Tax Firm streamline their invoice accounting process using our Intelligent Automation tools. 

Use Case: Real-time Customer Sentiment

Invoice processing only scratches the surface of what is possible with intelligent automation. Let's look at an example of real-time natural language processing (NLP) and sentiment analysis of customers.

The NLP platform can understand both the area of concern raised by a customer (or the intent of the feedback) and the emotional energy (or sentiment) embedded in the words and phrases used. 

This NLP workflow enables additional RPA to process the surveys as they are received and decide where immediate action is necessary based on the sentiment. 

Further, this data could be aggregated over time, enabling a range of advanced analytics to drive continuous improvement of the customer experience. The automated data gathering potential introduced by RPA and enriched by AI create yet another mechanism to optimize business operations. 

Reaching New Heights: HyperAutomation

Gartner has touted hyper-automation as a top 10 technology to watch. "Hyperautomation" takes the concept of intelligent automation and extends it to include additional applications while creating a model of the automated business. 

Hyperautomation combines intelligent automation (IA) with intelligent business process management software(iBPMS) to create an intellectual workflow framework. This framework enables complex business operation decisions by utilizing AI algorithms. 

Hyper-Automation enables organizations to reach new levels of accurate, automated knowledge work.

Building Your Organizations digital twin

The recommended approach to pursue hyper-automation involves systematically creating a digital (virtual) twin of an organization (DTO). 

This digital twin makes it possible to visualize the interaction of procedures, processes, and key performance indicators. This comprehensive view continuously identifies new business opportunities in real-time. 

For example, a DTO might capture pricing and sales data to model the impact of managing profitability in specific markets. Then, the Digital Twin can use this historical data to analyze volume changes on particular activities. 

Use Cases:

  • Logistics
  • Warehousing costs
  • Personnel costs in the shipping departments
  • Supply-chain change requirements
  • Overall profitability

These changes could then be analyzed in real-time across the entire company, even with limited or selective market pricing changes.

The automated feedback from the systems could then make the adjustments necessary on personnel staffing for various shifts and apply those changes to multiple systems while tracking everything in real-time. 

Creating Continuous Competitive Advantage

  • A higher scope of automation: The range of process automation will grow and shift from simple tasks to deeper knowledge and decision work.
  • An Automation Technology Stack: RPA alone is not enough. It requires a specific stack of technologies focused on and supporting machine learning. The art will lie in selecting, orchestrating, and controlling the "right" technologies.
  • High agility and agile process management: Organizations must be able to reconfigure and optimize processes quickly. 
  • Employee commitment. Employees must question, optimize, and reinvent their work processes. To do so, they must understand and actively use automation in an organization-wide mindset. 

The future of work

The path to Hyper-automation isn’t either-or, but the cumulative adoption of RPA and Intelligent Automation.

Even if you aren't ready to invest in full-blown hyper-automation, your organization's RPA and IA efforts must build on a solid foundation for the future of HyperAutomation. To wait and develop for hyper-automation from scratch is to lose competitive advantage. It's critical to begin growing your hyper-automation moat now, reshaping culture, and reskilling employees to adopt an automation-first mindset.


Jun 14, 2021