A deep dive into robotic process automation (RPA) and intelligent automation (IA)

Defining robotic process automation (RPA) and intelligent automation (IA)

RPA refers to the utilization of software robots or 'bots' to automate repetitive, rule-based tasks, while IA combines this process-based automation with cognitive technologies that learns from data patterns and user actions — think artificial intelligence (AI) and machine learning (ML).

RPA tasks can range from simple data entry and extraction tasks, all the way to scheduling appointments and automating order processing.

IA takes things a step even further by automating more complex tasks, such as natural language processing (NLP) and predictive analytics. NLP technology within IA systems can understand and generate human language, while ML algorithms enable the system to learn from data, make predictions and improve over time.

> Learn more | How RPA and AI drive end-to-end intelligent process automation

The key differences between RPA and IA

While both RPA and IA leverage software to reduce manual tasks, there are key differences between their capabilities and the types of tasks they’re able to take on:

RPA and IA in action

Here are some examples that show how RPA and IA technologies work on a day-to-day basis:

Capabilities and use cases of RPA

While RPA does have limits, the adoption of the automation technology isn’t slowing down. The future of RPA is bright, and intelligent automation software can use RPA to build up automated systems even faster than without RPA already onboard. At its core, RPA “increases efficiency, effectiveness and agility in business processes” by automating high-volume, repetitive activities.

Deloitte reports the opportunities for RPA are vast, predicting that its use cases will expand to handling “more complex tasks, such as customer service and fraud detection.” Advancements in cognitive automation means less human intervention may be required in the future for RPA to perform tasks. Here’s how organizations can leverage RPA to streamline:

• Human resources (HR): RPA can automatically track employee clock-in and clock-out times, leave requests and absences. Additionally, HR professionals can use RPA-enabled platforms to build performance management systems that facilitate real-time feedback and other evaluation processes.
• Customer service: RPA can automate responses to common customer inquiries and manage customer tickets effectively — acting as a “triage” to route tickets based on urgency and direct them to the right team, improving resolution times.
• Data migration and entry: RPA can be commonly used for tasks like logging onto websites, copying data between spreadsheets and processing standardized digital information.

Capabilities and use cases of IA

It’s a common misconception to assume IA could replace a human workforce. IA is the combination of RPA capabilities with AI-powered functions — which merely simulate human-like responses.

IA deals in the facts and data, whereas employees possess human empathy, understanding and expertise. Employees can use information derived from IA to back up their decisions, improve accuracy and turn customers in the right direction. Here are examples of IA implementation:

• AI-driven chatbots: IA technology is able to interpret customer needs beyond standard queries, providing faster and more consistent responses to engage customers before connecting them with human representatives.
• Supply chain and logistics processes: IA powers predictive maintenance processes for fleets and other equipment used in logistics through sensors and monitoring devices. All of this helps to reduce unexpected breakdowns and costly downtimes for a business.
• Compliance management: An IA system can streamline invoice processing workflows to maintain a clean, controlled environment that ensures financial documentation goes to the correct person at the right time based on predefined rules.

How Siemens uses IA to accelerate key AP processes

German multinational technology conglomerate Siemens is renowned for its innovative technologies. When the company faced an influx of invoices, it turned to Hyland’s IA solution, Brainware, to manage core AP processes with its precise intelligent capture capabilities.

Just nine weeks after initiating the project, Brainware for Invoices was fully integrated into the company's SAP systems.

With this implementation, Brainware drove hands-free invoice classification, header and line-item field data extraction, line pairing against information in SAP and seamless routing for approval. By leveraging Brainware, Siemens achieved:

• A 30–80% boost in process automation across shared services
• More than 90% of its 51 data fields extracted without manual intervention
• Quick and precise ability to capture new invoices in more than 20 different languages