Understanding Intelligent Process Automation Rpa, Machine Learning, Nlg And Cognitive Ai! V1

Understanding Intelligent Process Automation: RPA, Machine Learning, NLG And Cognitive AI!

Automation is undeniably one of the fastest-growing technologies out there today. Several organizations are already implementing AI and automation technologies like RPA, machine learning and cognitive AI to improve efficiency and simplify business workflows – which is a good thing. But the efficiency and ROI of these technologies will be much more if they are coupled together and are made to work with each other.

As Ai Becomes More Common, Applications That Employ It Must Work Effectively With Others Employing Similar Technologies, Which Will Result In Chains And Meshes Of Ai Systems That Work Simultaneously Toward Their Individual Goals In A Cooperative But Decoupled Fashion

– GARTNER

This brings us to Intelligence Process Automation (IPA).

IPA refers to the confluence of 4 emerging technologies for improved productivity, better customer experience, smarter workflows, increased efficiency and reduced costs. : Robotic Process Automation (RPA), Machine Learning/Advanced Analytics, Natural Language Generation (NLG), Cognitive Artificial Intelligence (Cognitive AI)

  • RPA is a business process automation technology that automates routine and mundane processes and is capable of performing rule-based tasks such as fetching information from configured locations, using data to create reports, populate dashboards and perform calculations.
  • Machine Learning/Advanced Analytics is a software that enables systems to learn from data, identify data patterns and take decisions with minimal human intervention.
    Advanced analytics is the analysis of data using sophisticated techniques and technologies to discover deeper insights, make predictions, or generate recommendations from it.
  • Natural-language generation and processing is the use of artificial intelligence to produce and process written or spoken narrative in natural language from a dataset by following rules to translate observations.
  • Cognitive AI technologies combine machine learning and natural-language processing & generation to build virtual agents/chatbots which can converse with users to perform tasks, send information, and push alerts.

Example Of IPA: RPA And Chatbots In IPA

The integration of back office bots (RPA bots) and front office bots (chatbots) for end-to-end automation is an excellent example of IPA.

For chatbots to hold conversations, share information, complete tasks and capture requests, they to be integrated with different enterprise systems. Chatbots can access the required information independently only if these systems have modern APIs . And in the absence of an API, the chatbot fails to integrate and retrieve information from these systems. However, with the integration of RPA, chatbots can effectively navigate through legacy enterprise systems that do not have modern APIs.

Rpa Enables Chatbots To Integrate With Legacy Systems

An RPA powered chatbot has the capability to integrate disparate and multiple back-end enterprise systems. RPA enables chatbots to retrieve information from these systems and handle more complex and real-time customer/employee requests and queries at a large scale. In the same way, Chatbots upon a user’s request, can trigger RPA to perform specific mundane tasks without routing them to a human agent.

The combination of chatbots and RPA provides several business benefits. It helps organizations to enhance customer experience at lower costs by leveraging the automation capabilities of RPA and self-service features of chatbots.

Learn More: RPA Bots: Understanding The Chatbot And RPA Integration

Take RPA To The Next Level With IPA

While RPA bots have been successful in handling structured data stored in various databases and application silos, they are now challenged to handle business scenarios involving a multitude of unstructured data such as images, text, and speech. RPA bots can be infused with AI technologies like machine learning, natural language processing and deep learning to handle these specialized cognitive tasks.

IPA can now perform tasks that were once done by humans and in addition they can perform these tasks with increased accuracy and efficiency. Along with traditional rule-based automation that RPA is popular for, IPA incorporated with deep learning and cognitive technology, is now capable of decision making as well.

Benefits of IPA

The convergence of various domains of technologies is needed to produce automation capabilities that dramatically elevate business value and competitive advantage. IPA is one such synergistic technology.

According to McKinsey, several companies have managed to automate 50-70 percent of tasks with the help of IPA, in turn reducing the through-put time by 50 – 60 perfect and yielding an ROI in triple digit percentages.

Following are some of the important benefits of IPA:

  • Maximize ROI from your legacy and complex systems.
  • Improve employee productivity and enable them to focus on high value tasks and improving customer experience
  • End-to-end automation of processes
  • With IPA a well coordinated workflow can be set up between robots, people and systems. Automating siloed taks would only provide partial solutions while integrating it with IPA would yield a seamless enterprise-wide solution.
  • Increased competitive advantage

Key Considerations When Getting Started With IPA

If you are planning to potentially invest in IPA implementation, here are a few points to keep in mind:

  • Align the role of IPA initiative with the existing framework and strategy of your organisation and clearly lay out targets
  • Start off small and deploy technology based on the results obtained from the pilot project. Launch an MVP to test impact before employing it on a large scale and automating end-to-end processes.
  • Perform a risk-benefit analysis of the project as shown below and quantify the disruption factors, time required, risks and the business benefits expected.
Mission Disruption Time & Risks
  • Set up a CoE to monitor the transformation and ensure rapid deployment of IPA solutions.
  • Coordinate changes and manage communications – In any large scale transformation it is essential to have effective communication to manage resources and align the change without hampering the corporate strategy and vision. The success of establishing a new working model depends upon the organisation’s culture and how people adapt to agile practices.

Conclusion

Organizations across the globe are looking actively for intelligent technologies like RPA and machine learning. But why deploy something good if there is something that’s even better and more comprehensive? Through end-to-end process automation, IPA helps organizations increase competitive advantages, drastically reduce costs, improve productivity, reduce risks and more!

In order to implement IPA technologies efficiently with  accelerated time to value and low cost to complete, you need an agile partner with a strong IP portfolio. Acuvate’s IP portfolio consisting of BotCore, Optimum, and Azure Cost Optimization (ACO) leveraging AI technologies for intelligent automation are helping CIOs automate and streamline IT operations, improve service desk efficiency, optimize cloud spending and reduce costs.

If you’d like to learn more about this topic, please feel free to get in touch with one of our IPA consultants for a personalized consultation. You might also be interested in exploring our process automation solutions and services for further  !

If you’d like to learn more about this topic, please feel free to get in touch with one of our IPA consultants for a personalized consultation. You might also be interested in exploring our process automation solutions and services for further insights!


    Related Post

    Subscribe to our monthly newsletter to get the latest updates directly to your mailbox!

    2021 Acuvate. All rights reserved