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Chapter 6

What is Enterprise Grade Intelligent Automation and How Does it Differ from Traditional RPA?

The market for automation has evolved quickly over the past few years. In 2016, robotic process automation (RPA) was still a relatively new technology that few companies had running in production, let alone at any scale, within their organizations.

Over the years, however, RPA has evolved to solve more challenging enterprise demands, making way for intelligent automation, which combines artificial intelligence (AI), machine learning (ML), and natural language processing and other advanced technologies to drive productivity at scale. In 2021, however, with thousands of organizations adopting some form of RPA, there’s even more confusion about the difference between enterprise intelligent automation and traditional RPA.

Rpa vs hyperautomation

Traditional RPA: A powerful platform

RPA redefined the way we viewed work. By integrating software robots into the business workflow, RPA enables people to streamline their efforts, managing data across various systems using rules and strict governance to ensure compliance.

This traditional RPA platform can automate processes based on structured data and well-defined rules. Companies can realize significant gains in efficiency, data quality, employee satisfaction, and customer experience using traditional RPA. In fact, many organizations that have implemented traditional RPA have experienced costs savings and ROI of 30%-200%.

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Even with these amazing efficiencies, however, there are limitations to traditional RPA. For example, the world we live in is not always made up of structured data. Most organizations receive invoices from vendors on paper in many, non-standardized formats.

Converting data from these unstructured documents traditionally means that people must read the information and re-type the data into a structured format that can be used with data processing systems. In addition to the unstructured data, often the rules to judge specific actions are dependent on many factors which may not be aligned with easily definable rules. With high volumes, this process is anything but efficient.

This is where traditional RPA falls short of enterprise grade intelligent automation.

Intelligent automation: Making RPA smarter

Building on the strengths of RPA, intelligent automation brings together complimentary technologies to augment the capabilities that traditional RPA doesn't include. Broadly, these technologies fall into the following categories:

  • Knowledge & Insight - The ability to scan data sets and knowledge bases to extract data and compile it into customized formats

  • Visual Perception - The ability to read, understand and contextualize visual information digitally

  • Learning - The ability to adapt and evolve processing patterns and contextual meaning from datasets

  • Problem Solving - The ability to solve logic, business and system problems without human intervention

  • Collaboration - The ability to enact seamless communication and collaboration between people, process and technology

  • Planning & Sequencing - The ability to discover and leverage opportunities to optimize workflows and workloads that optimize business outcomes

Technologies like AI and ML can be applied in nearly every category where complimentary technologies like optical character recognition (OCR) and intelligent document processing (IDP) would apply to the visual perception category. This is where unstructured documents like invoices or purchase orders can be read and from which data can be extracted to be used by RPA.

Intelligent automation can engage components from one or more of the categories to create complex and interesting solutions to real business issues. This capability not only opens up new ways to interact with customers, vendors, and employees, but also can spawn new products and services for the organization. While intelligent automation can integrate with these complimentary technologies to achieve amazing outcomes, the technology is incapable of considering the need to manage the platform at an enterprise level. Let’s look at what is necessary to upgrade from intelligent automation to true enterprise grade intelligent automation.

Enterprise Intelligent Automation: Smart RPA under governance

While adding technical solutions to interpret unstructured data makes it possible to automate data through traditional RPA methods, it quickly becomes apparent that technology alone isn’t enough. Stacking one technology on top of another can create a house of cards rather than a structure that can withstand a hurricane.

In order to be enterprise grade, the whole stack of technologies must adhere to a strong governance structure to ensure that any automations built on that stack can endure the harsh extremes of corporate use, including:

  • Security

  • Data management

  • Integration

  • Access control

  • Flexibility under stress

  • Scalability

  • Support

  • Policy management

If one of the technologies used to build the intelligent automation solution doesn’t conform to the enterprise grade IT requirements, the entire solution is at risk. Each component, like a brick that makes up a wall, must hold up under the pressures of the enterprise.

Building an enterprise grade intelligent automation solution may require additional effort, but it will produce a solution that the entire organization can rely on.

Next: Chapter 7

Will RPA Technology Take Human Jobs?

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