High productivity, better efficiency, and low associated costs are a few striking advantages associated with smart Robotic Process Automation (RPA) implementation. To put it simply, RPA is the automation of tasks that are high-volume, repetitive, rule-based, use structural inputs, and don’t involve human judgment.

On the other hand, Artificial Intelligence (AI) is a much broader concept that is being leveraged by major corporations to attain higher business growth. Although AI is equally good for rule-based tasks, the primary attraction is that it brings decision-making abilities to the table. Its accuracy of predictions and decisions based on rules or numeric patterns or just raw data, and its ability to self-evolve with time, make it a subject of prime interest to businesses.

RPA vs AI

Because tasks such as emails, data processing, sorting ambiguous/non-standard inputs, and processing invoices with different formats involve some sort of intelligence, they are not suitable for RPA per se. However, these are some of the most easily doable jobs for Artificial Intelligence. AI provides judgment-based results and can understand and manage variability. Above all, its capability of analyzing patterns over time that makes AI the game-changer.

RPA is more process-centric, AI is largely data-driven. While the former is just mimicking the actions of the users/businesses, the latter is more about thinking - brains and brawn.

Blending the two

Today, substantial efforts are being made to fuse the capabilities of RPA and AI to deliver unprecedented gains through ‘Intelligent Automation’. When RPA functionalities are optimally backed by the wonders of AI, businesses assume a commanding position to rule the market.

Intelligent Automation, a combination of RPA and AI, is the mimicking of business actions with accurate decisions for constantly changing inputs. AI can be integrated with RPA for scenarios like image recognition and text analysis.

The applications

2 relevant and trending examples that throw light on how far this new-age tech combo can stretch.

1. Citrix Automation

   Recently, one of the prominent RPA tool makers, UiPath, introduced a new technology (an option) called AI Computer Vision for Citrix/VDI automation.

   itrix automation is used for automating virtual machines through remote access. In this process, there’s no access to selectors or UI properties and thus traditional automation is not an option. Since we cannot rely on images, the only ways left are through shortcut keys and OCR automation, and neither gives 100% accurate results. That’s where UiPath came up with this solution called AI Computer Vision Activities.

   AI Computer Vision Activities Pack is a mixture of AI, OCR, text fuzzy-matching, and an anchoring system. Using this combination, we get access to the selectors/UI properties and the success rate of citrix automation increases to over 95%.

   Citrix Automation or Virtual Desktop Interface (VDI) streams an image of the remote desktop, similar to how video-streaming services like Netflix do. There are simply no selectors to be identified in the video. Computer Vision allows bots to ‘see’ the screen and visually identify all the elements, rather than relying on their hidden properties, IDs, and other metadata. It is not only limited to VDI environments but can also recognize elements across a wide range of cases where traditional user interface (UI) automation methods struggle, including SAP, Flash, Silverlight, PDFs, and even images.

2. Invoice processing

Even in this era of digitization and automation, most banks and insurance companies rely on the use of hard copies of documents and invoices. To digitize all unstructured invoices, there are multiple tools available in the market and most of them use AI for that.

AI-based software supports:

• Invoice Separation
• Data Extraction
• Invoice processing

Invoice Separation: Segregating invoices based on their file type, be a scanned copy or PDF, is a time-taking manual process. Deploying AI for invoice segregation based on historical data, invoice date, or any other parameter, and creating a file for every invoice is a feat of the AI-RPA winner pair. Based on the inputs and the templates created, the process can be made to segregate invoices even if they are spread across multiple sheets.

Data Extraction: Without AI, RPA can be used to extract data or information based on the templates created, but the task is largely inefficient since, for every new invoice format, a new template has to be created. This is because the template tells the bots what to extract and from where. Doing this for every supplier and invoice layout makes it a tedious practice. Also, if anything changes in the layout, the template has to be redone.

But when powered by AI, information can be extracted without the creation of templates as it involves a self-learning software that does not depend on any previously created template. Typical examples of information that can be extracted from invoices are invoice number, gross amount, net amount, tax percentage, order number, etc.

The processing: The primary goal of the whole process is the end to end digitization and automation of the invoice process flow. Data might be in any format such as database, Excel, XML structure, etc. and invoices need to be processed with zero errors. Most businesses have been investing a lot in manpower to perform these tasks manually. But today, the combination of AI and RPA is a profitable replacement of the above scenario and it saves an impressive amount of money and time.

The above examples are just a glimpse of how AI brings more value to automation and we have many more coming our way. We will keep you posted on the latest updates.