The last few years have seen a major surge in Cognitive Process Automation with the rising of technologies Robotic Process Automation (RPA) and Artificial Intelligence (AI). Traditionally, business process improvements were multi-year efforts and required an overhaul of enterprise business applications and workflow-based process orchestration. The surge is due to RPA’s ability to rapidly drive the automation of business processes without disrupting existing enterprise applications. Today, it’s Artificial Intelligence’s (AI) turn to prove itself.
Typical use cases on AI in the enterprise range from front office to back office analytics applications. A study by McKinsey noted that customer service, sales & marketing, supply chain, and manufacturing are among the functions where AI can create the most incremental value. Despite the tremendous potential of AI, the study also notes that only a few pioneering firms have adopted AI at scale. Key among the adoption limitations are the availability of massive data sets, generalized learning, regulation, and social acceptance due to potential bias in algorithms.
How are they different?
While RPA is used for attended automation where it requires some assistance from people by automating repetitive processes, AI is viewed more as a form of unattended automation not requiring human labor at all by automating end-to-end processes. RPA uses structured inputs and logic, while AI uses unstructured inputs and develops its own logic.
How they can be embedded?
Today, organizations need more cognitive capabilities which calls for integration of RPA with AI to automate more complex business processes with data from unstructured sources (like scanned documents, emails, letters, and natural speech). This is called Cognitive Process Automation.
Let’s look at how we can embed AI into RPA for Cognitive Process Automation:
- Any enterprise process can be defined as consisting of the following sequence: Data -> Judgment -> Action. Leading companies are leveraging AI to make the most of all the data that is available to them by adding prediction as a step into the sequence, leading to: Data -> Prediction -> Judgment -> Action.
- Understanding the complexities and challenges of these steps will be critical to solving the AI/cognitive puzzle when it comes to enterprise automation.
- Each of these four steps consists of challenges that typically lead to increased manual activities.
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