The automation landscape has witnessed progressive growth in the past few years. Robotic process automation was incorporated by only a few companies during the early days of its inception. The technology progressed further, addressing complex enterprise demands, and emerged as cognitively intelligent automation (IA). The new automation technology combines many advanced technologies, namely artificial intelligence, machine learning, and natural language processing technology, to enhance organizational productivity. The adoption of automation technology witnessed a great surge, and the key differences between RPA and intelligent automation need a better understanding before implementation.

Differences between RPA and intelligent automation.

Robotic process automation is a foundational technology on which intelligent automation is built. To put it simply, IA is thinking, and RPA is doing. Robotic process automation interacts with the application using bots and works on a predefined set of rules. Intelligent automation, on the contrary, is embraced with cognitive ability and learns how to automate tasks effectively. However, the RPA still wings intelligent automation. The new IA technology uses both artificial intelligence and RPA to transform business operations.

Scope.

RPA operates based on a set of predefined rules and automates large volumes of repetitive data. Intelligent automation handles a broad range of complex processes, incorporating decision-making capabilities through data interpretation and predictive analysis.

Artificial intelligence in RPA and intelligent automation.

RPA is programmed with limited logic and works based on a predefined set of rules. IA uses multiple technologies like ML, NLP, and artificial intelligence to automate processes and improve and adapt over time based on business data.

Cognitive ability.

RPA is a static process with expected input to deliver the desired output. IA can handle the ambiguity in the tasks with its cognitive ability.

Implementation.

RPA is a well-defined process, and it is quite easy and quick to implement. IA, on the contrary, requires a solid automation strategy, and it takes a longer time for implementation.

Traditional RPA.

Traditional RPA has played a key role in redefining business workflows by integrating software robots into business operations. The implementation of RPA in businesses could streamline efforts to manage business data with a set of predefined rules. The automation technology has saved a lot of time for the employees by automating time-consuming manual repetitive tasks, allowing them to focus on more strategic initiatives. The wide range of business operations that employ RPA technology varies from invoice processing to appointment scheduling.

In spite of benefits like cost savings, RPA is bound by a few limitations. Not all businesses will have structured data and employees may have to indulge in converting unstructured data to structured formats. Also, the rules to judge specific actions cannot always be aligned with a defined set of rules, which makes the process less effective.

Intelligent automation.

Intelligent automation integrates multiple technologies to solve business issues, from automating repetitive business tasks to executing comprehensive processes. The IA capabilities enhance business organization to enhance customer, vendor, and employee interactions to develop new products and services. However, IA implementation in the organization needs a look at underlying platform management at the enterprise level for sustainability and effectiveness.

Intelligent automation enhances RPA.

Intelligent automation can facilitate end-to-end digital transformation in the organization by blending RPA with complementary technologies to augment its capabilities. The broad classification of these technologies includes.

• Knowledge insights: Extracts and compiles data from different business sources and compiles them into customized formats.

• Visual perception: The technology can read, understand, and digitize visual information.

• Learning: Adopt and evolve processing patterns to understand contextual meaning from datasets.

• Problem-solving: The IA technology can solve logical business and system problems with zero human intervention.

• Collaboration: Establish seamless communication and cooperation between people, processes, and technology.

• Planning and sequencing: Streamline and optimize business workflows and discover opportunities and bottlenecks to derive better business outcomes.

Technologies like OCR and IDP have aided in enhancing the capabilities of intelligent automation by validating and extracting data from unstructured documents into digital formats to enable RPA to process them effectively. Tools like BPM aid in intelligent automation to analyze and monitor complex business tasks effectively.

The Path to Enterprise-Grade Intelligent Automation.

We need a robust digital workforce.

To upgrade robotic process automation to intelligent automation, organizations need to employ a digital workforce that can facilitate collaboration for long-lasting and adaptable IA solutions. A well-deployed workforce can enhance workflow efficiency for quicker market delivery and ROI for the business.

Governing smart RPA.

As said, IA stacks more technologies to enhance capabilities for enterprise automation and needs proper governance for stability. Automation stability in the business can be achieved when all the components of the intelligent automation stack adhere to strict governance and comply with rigorous IT requirements to scale resilient automation solutions that are scalable across departments.

Unifying Your Intelligent Automation Strategy.

Intelligent automation takes the stage when RPA alone is not enough to enhance business efficiency. While RPA can replace human actions, IA integrates different technologies like ML, NLP, and AI to automate complex business processes. It is a viable solution for dynamic business environments with frequent changes in the process.

Effective use of IA solutions for your business is possible in conjunction with a digital workforce and proper governance. While RPA remains a digital transformative power for many organizations, intelligent automation is the future. Scaling IA across your organization with the help of a digital workforce can unlock new opportunities for your business.