According to Abhishek Kishore Gupta (pictured), the CEO for APAC & ME at Turbotic, deployment of an RPA bot without AI is like a hammer driving a nail. When confronted with an obstacle, the hammer cannot perform, as it is supposed to. An RPA bot does its job regardless of the input, it can cause damage and possibly shut down workflow.
A higher level of intelligence to assess, learn, and optimize the outcomes is required to make better decisions and make automation resilient. AI systems with RPA bots enable decision-making based on a deep understanding of the process from all the data available using NLP. Deep learning AI is a more adaptive, algorithm-based technology that can mimic human thinking and intelligence and extract and analyze vast amounts of data. It can be trained to handle exceptions and repetitive tasks without human intervention. These models are upgraded periodically for more accuracy and efficiency.
Every automation should have Data at the core, and the system should add intelligence to understand the data dimensions. After Data, next processes and then user. Sourcing Data from input sources, processing it with intelligence and adding dimensions to make it rich are three activities in data-driven automation. Embedding AI in RPA addresses four needs that cannot be fulfilled just by RPA in the long term. These needs are the following:
- Adaptability: RPA bots have a limited understanding of everything. For example, an RPA robot can retrieve data from a known location. But if the layout changes, the robot will fail, as the robot is not skilled enough to find the location of the data. AI-based recognition tools can continuously learn through machine learning, and the efficiency of the process improves. They can capture necessary information from any location, and the workflow continues without interruption.
- Scalability: RPA robots are inflexible in the face of change, which prevents them from scaling when the organization requires upgrading. For smooth operations, the organization needs to constantly update its existing bots to handle whenever there is a variation. AI can learn from user corrections made in the past and take appropriate actions as and when required in the future, which means far less number of errors. It also means lesser involvement of human labor to build and update bots to handle new situations constantly. It gives human workers more time to focus on other strategic business activities like developing new technologies and new skills.
- Extended scope: RPA bots can perform narrowly defined tasks with well-defined data, whereas artificial intelligence automation can be applied to numerous business cases. It can analyze data, perform data mining operations, and also identify process improvements all by itself. AI-led automation can also detect anomalies in data and processes without defining code for every possible action.
- Resilient Automation Operations: AI can not only optimize Automation Operations by automating scheduling, optimizing utilization, and auto ticketing, but it can also learn from error logs, take corrective action, and auto-heal bots. AI can fix the biggest reason for RPA project failure, i.e. very high Operational cost.
