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Cognitive Process Automation: solving the limitations of RPA

February, 2022

Introduction:

Recent years have seen a dramatic increase in the use of robotic process automation (RPA), owing to the multiple benefits it brings in terms of automating activities, streamlining processes, and cost savings. RPA is a tool that automates repetitive, manual processes that are traditionally undertaken by skilled workers. It is designed using basic technologies such as screen scraping, macro scripting, and workflow automation.

RPA can be quite challenging when working with complex and unstructured data. It may be necessary to enlist the assistance of a person to ensure the operations go smoothly. Since these challenges first surfaced, there has been a hunt for a solution capable of expertly addressing all data types while preserving the fundamentals of efficient process optimization.

This is where Cognitive Process Automation (CPA) comes into play.

Understanding Cognitive Process Automation

It is a cost-effective, efficient, and profitable automation methodology for automating any business process end-to-end using a full stack of AI technologies and microservices such as machine learning, reasoning, natural language processing, speech, computer vision, and human-computer interactions that mimic the human brain's functioning and can improve human decision-making.

Source :cognilytics

Evolution of RPA to CPA

From time to time, cognitive process automation (CPA) is confused with robotic process automation (RPA). While this is understandable, CPA is a far more advanced and innovative platform.

The workplace and the way we do business transform along with technology. Cognitive automation is not a replacement for RPA but rather an evolution of it.

Source:EverestGroup

• Assisted RPA or RPA 1.0 is where the RPA software automates various activities and applications running on the user’s desktop. It is proved to be effective in cutting down average handling times, improving customer experience, resulting in cost savings, and cutting down the time taken to train an agent on it. Provided assisted RPA should only be applied when real-time human-system interaction is needed.
  
  Objective: Improving worker productivity
  
  Limitations:
  • Partial automation
  • Difficult to scale
• Unassisted RPA or RPA 2.0 is where the RPA software is deployed on several machines to run without the requirement for the automation to be attended. These steps can be automated and simplified through dashboards and intervene with a specific robot’s performance as and when needed. This unlocks great possibilities as the robots can work 24x7 and replaces human interactions with business processes.
  
  Objective:
  • End-to-end automation
  • Scalable virtual workers
  Deployment:
  • Server
  Features:
  • Work orchestration ( Scheduling/ queuing) centralized robot management
  • Robot performance analytics
  Limitations:
  • Manual control and management of robots
  • Management screen and system changes
• Autonomous RPA can be defined as the best of two worlds where it is built leveraging AI and related new technologies like cognitive automation, machine learning, computer vision, and more to the RPA. This splendid convergence of various technologies generates capabilities that radically elevate business values and gain a cutting advantage for users. This is the current “latest” version of RPA.
  
  Objectives:
  • End-to-end automation
  • Scalable and flexible virtual workshop
  Deployment:
  • Cloud/ saas(VMS)
  Features:
  • Auto-scaling
  • Dynamic load balancing
  • Context-awareness
  • Advanced analytics and workflows
  Limitations:
  • Processing unstructured data.
• Cognitive automation uses several algorithms like natural language processing, data mining, semantic technology, text analytics, machine learning processing both structured and unstructured data.

Use of artificial intelligence(AI)technologies including machine learning and natural language processing(NPL) to enable

• Processing of unstructured data
• Predictive and prescriptive analytics
• Dynamics load balancing
• Automation of tasks that involve judgment

Cognitive Process Automation (CPA) overcomes the constraints of robotic process automation (RPA). It is a methodology that infuses intelligence into data-driven processes, which is why it is referred to as intelligent process automation.

In terms of operational efficiency, besides cost-effectiveness cognitive automation can help enhance productivity by minimizing human-based errors and reducing process time by approximately 40%.

Benefits:

• Reduced cost
• Enhances efficiency
• Reduces error rates
• Expert decision making

ACE - The all-in-one process cognitive automation platform

Like a supercomputer in your pocket, our low-code Enterprise AI Platform, Algonox Cognitive Engine (ACE), has a powerful suite of Pick and Choose microservices to build intelligence into any app or process. ACE's robust modules of data intake, data extraction, data processing, reporting, data export, and AI conversational engine offer end-to-end automation for any process.

Conclusion:

In contrast to RPA, which automates certain operations based on static business rules that are implemented mechanically as long as the underlying system remains constant, cognitive automation automates tasks by learning the purpose of a scenario, much like humans do. Thus, cognitive automation applies artificial intelligence approaches to areas that need document processing, vision, natural language processing, and sound, elevating automation to a new level and increasing business outcomes.

ACE is the all-in-one cognitive automation platform that empowers your enterprise software. It’s designed to fuel intelligent processes on any enterprise system. Contact us today to see how we can help optimize your business

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