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RPA is not enough

How effective enterprises go beyond RPA to comprehensive intelligent automation solutions

Resilient organizations know they need to be agile in order to survive and succeed. They must be able to rapidly identify and automate as many business processes as possible—and this calls for end-to-end automation and true digital workflow transformation.

Is RPA an important piece in this process? Absolutely! But is it the only piece? Definitely not.

Why You Cannot Survive on RPA Alone

RPA adds a lot of value to the business. In fact, when properly implemented, enterprise-wide RPA deployment can serve as the cornerstone of a broader digital workflow transformation. The technology is very good at automating repetitive manual tasks that occur in a relatively stable environment using structured data. This frees up the people who were doing those tasks to do more productive things. However, many workflows are complex. What happens when you have processes that aren’t consistent or require human intervention? What about the unstructured data coming in via emails, financial documents, forms, contracts, images and digital assets? RPA bots don’t make decisions; they follow business rules. When the rules don’t sufficiently anticipate possible conditions that can arise within a workflow, bots can’t complete the process. They don’t have the appropriate business logic to handle the situation.

The challenge then is figuring out how to optimize the entirety of the process to deliver better results to customers, partners and suppliers, while creating a better work experience for the people executing the processes. Optimization will include RPA, but it will do so as part of a larger intelligent automation approach.

Intelligent automation refers to the holistic and comprehensive use of artificial intelligence (AI) and other automation technologies in addition to RPA to streamline and scale automation and drive greater value. When RPA is implemented as part of a well-planned, thoughtful intelligent automation deployment, business rules and logic can be integrated into the workflow to handle more complicated eventualities.

3 Steps to Intelligent Automation and Digital Workflow Transformation:

1. It must be championed at the highest level of management.

  • The transformation process begins with upper management. There are three elements that C-levels need to envision and communicate: The strategic objectives of the intelligent automation transformation; a business case, including a high-level cost–benefit analysis; and an overview of the roadmap and key milestones for the next one to three years. The project should be designed from the top down, but implemented from the bottom up (for example, beginning by deploying a pilot).
  • Comprehensive digital transformation requires doing the necessary homework at the beginning of the transformation journey—process mining and mapping, determination of relevant business rules and data quality control.

2. Put the right technologies to work.

  • The key to moving beyond RPA is to infuse it with artificial intelligence (AI). Why? Because almost all business processes rely on data, and as the old adage goes: “Garbage in, garbage out.”
  • In other words, inaccurate data can cause problems in processes and workflows that hurt your ability to deliver a high-quality customer experience, your bottom line and even your ability to comply with regulations.
  • Data collection usually begins early in a process and continues throughout the entire workflow.
  • Getting the data collection right is key to providing the quality raw materials necessary for quality results. When automated, data capture processes can positively impact both robotic and human workflows. The right data, accurately captured, equals smarter robots and more productive humans.
  • Document intelligence: AI needs to step in when it comes to unstructured data. Technologies like cognitive capture, machine learning (ML) and natural language processing (NLP) can identify, extract and analyze unstructured information, so organizations can unlock the value of all of the data available.
  • Process orchestration: There are many workflows operating at once in every organization. Process orchestration makes it possible to coordinate all of the automated workflows so companies can scale and manage their digital workforce on-demand.
  • Connected systems: Data silos and disparate systems will hold back your digital transformation efforts. An open architecture and pre-built connectors enable organizations to integrate systems into a network of intelligent technologies and services and unite critical business systems such as enterprise applications.

3. Leverage low-code for agility and scalability.

  • Today’s ever-changing business landscape means the rules guiding automated processes may need to be modified—quickly. Businesses need to be able to continuously orchestrate automated processes to stay on top of shifting regulations. You can’t purely rely on programmers if you want to be agile.
  • An intuitive interface that features drag-and-drop features and reusable components empowers citizen developers to put their knowledge of business processes to work. They can speed up end-to-end automation for real scalability, and they can also directly modify and update automated workflows for maximum agility. They can even build and deploy RPA bots in just a few hours—all without needing to rely on programmers.
  • At this point, the direction is clear and so is the technology required to accomplish it.
This article is based on The Institute for Robotic Process Automation and Artificial Intelligence (IRPA AI) WhitePaper Titled RPA is Not Enough

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