Traditionally, business process improvements were multi-year efforts and required an overhaul of enterprise business applications and workflow-based process orchestration. However, the last few years have seen a surge in Robotic Process Automation (RPA). 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 recent 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.

Let’s look at how we can embed AI into RPA. 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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