Understanding Technologies for Smarter Automation Implementation

Artificial intelligence is being increasingly overused, even with low-level automation. This leads to its capabilities being confused between technologies and their subsets like- AI, ML, and robotic process automation (RPA).

The difference between RPA and AI is defined as the difference between a doer and a thinker. RPA follows strict rules executing repetitive operations flawlessly. On the other hand, AI can deliver much more as it deals with vast volumes of information, converting them into actionable insights by detecting underlying connections and patterns. RPA mimics human thinking by performing strings of tasks fed in the system, while AI strives to replicate the human decision-making processes.

It is easy to be confused between the two as some companies deliberately brand their RPA products as AI to justify the price and value. However, the actual AI products are almost as valuable as a well-trained and experienced employee is. AI empowers employees to make decisions and complete non-trivial business goals with increased effectiveness. It is not that either of the two technologies is better than the other. Each has its specific use cases. Marketers need to know the differences between both and select accordingly.

As automation, opportunities multiply in every business vertical; software companies strive to develop solutions accordingly. However, it helps with customer relationship management by handling customer data effectively. The process of onboarding new customers has been successfully automated in most of the firms. Since the matter is about following rules and does not require training with vast sets of data, AI is not involved in this.

For the existing customers, RPA can take help the CRM by keeping records accurate and up-to-date. That has traditionally been the biggest challenges for them. Firms also rely on RPA to take care of the due diligence workflows, including sorting, collecting, and organizing relevant data. RPA helps deliver time-consuming and error-prone financial workflows accurately and efficiently. As it heavily relies on data entry and calculations, RPA tools ensure the automatic transfer of the information in a more accurate and cost-efficient manner, accommodating real-time changes. Automated systems are best for ensuring compliance as it boils down to a stream of repetitive, standardized tasks.

There is an intermediary step between RPA and AI, which is machine learning (ML). The difference between ML and RPA is that the former is data-driven, while the latter is rule-driven. Machine learning is both a precursor and a part of AI, as it incorporates prescriptive analytics and can be used to run decision-making engines successfully. However, ML is not yet AI since it relies on sets of mathematical rules that are iteratively more accurate in recognizing patterns they are trained on.

The goal of all these intermediary developments is to create software, which mimics the human ability to make decisions, logical deductions, and act accordingly. IBM Watson and Google’s Deep Mind are brilliant AI engines that can be adopted across any industry. They can understand what they are seeing, classify information, create plans, solve problems, and interact with systems, objects, and humans. Compared to RPA, which requires a numerical input from the user, AI learns as it goes by observing deriving answers based on the context and the previous conversations.

AI can handle heaps of data, which is a significant step forward. Applying RPA tools for optical character recognition, without any information about it is a wasted effort. A useful AI tool can even be trained to spot potential errors revolutionizing the financial industry by simplifying the insurance claiming, accounting, and record-keeping processes.  All these technologies sum up as different stages of automation, and they can be optimized only after understanding the difference between them. It is essential not only to classify them correctly but also to understand which is the best for each particular project.

As the complexity of enterprise challenges increases,  firms will require a combination of all of them. For instance, an AI chatbot interacts with a customer with a machine learning-powered recommendation engine. It can find the best matches for the customer’s needs, and an RPA tool making the necessary updates in the customer’s profile for further reference in the backend. Will all these technological forces coming in, operations across departments and industries will be simplified, and the result will be a multiple times increase in productivity.