The automation of operational processes through the use of robotic process automation and artificial intelligence (AI) has spurred the development of intelligent automation. More and more organizations are using this technology to automate complex processes, which enables data analysts and knowledge workers to focus on other tasks that generate more value. These developments have led to the advancement of process-driven and data-driven automation.
Process-Driven Automation Is the Beginning
Process-driven automation focuses on performing tasks using predetermined pathways and processes. Automation cannot vary from its predetermined pathway because the process guides the automation. If an assigned task does not fit this predetermined pathway, it is deemed an exception to the rules, which means a human must intervene and perform or oversee the task.
Robotic desktop automation is the most basic level of intelligent automation. It allows automation of basic tasks, usually within one system and one data silo. An example is a virtual assistant, which can automatically send emails or create reports. The next step up is robotic process automation, which can automate slightly more complex processes across multiple systems. However, both types can only automate repetitive processes.
Process-driven automation is a first step in digital development and transformation because it is manageable and easy to implement. Organizations can automate some processes within weeks or months to realize gains in efficiency, cost and time savings, and improvements in accuracy. For example, process-driven automation can be used to manage financial resources, deliver physical products, and streamline customer services. However, process-driven automation has significant limits.
Data-Driven Automation Is the Future
Data-driven automation is the evolution of process-driven automation, as it is a more complex version of intelligent automation. Data and context guide the automation, which makes it more powerful and more capable of handling complex processes. Data-driven automation combines robotic process automation with AI technologies, including natural language processing (NLP), machine learning (ML), deep learning (DL), deductive and prescriptive analytics, and recommendation or decision-making engines.
High quality data is obviously central to data-driven automation, as the AI learns from the data to optimize process automation. Processes can be automated more quickly and accurately than process-driven automation, and it can occur across multiple data silos and systems. Data-driven automation can also process unstructured data and use advanced AI to engage in judgment-based interactions and perform human-like activities.
Data-driven automation is more powerful than process-driven automation because it can automate non-repetitive processes. This increases the range of processes that can be automated across an enterprise. Data-driven automation can automate tasks that humans had to perform themselves. It also makes robotic process automation smarter because it is now data driven, which enables intelligent automation to engage in processes that go off predetermined pathways.
Data-driven automation produces a number of key benefits including:
- Increasing the speed of analytics
- Improving the ability to analyze big data
- Reducing the number of manual errors
- Saving enterprise time and money spent on human resources
- Allowing data scientists and analysts to uncover new insights that can guide data-driven decision-making
While process-driven systems are effective for automating certain processes, they have significant limitations. Automation of data-driven systems is the future. Advances in robotic process automation and AI, supported by the proliferation of big data, will enable you to make greater use of intelligent automation. Data-driven automation can produce substantial time and cost savings and increase efficiencies to higher degrees than ever before.