13 May 2020 What is the use of robotic process automation?

Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers.
It is sometimes referred to as software robotics (not to be confused with robot software).In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application's graphical user interface (GUI) and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to the use of automation in products that might not otherwise feature APIs for this purpose.

RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI and often do so by repeating a set of demonstration actions performed by a user. RPA tools differ from such systems that allow data to be handled in and between multiple applications, for instance, receiving the email containing an invoice, extracting the data, and then typing that into a bookkeeping system.

Business process automation (BPA), also known as business automation or digital transformation, is the technology-enabled automation of complex business processes. It can streamline a business for simplicity, achieve digital transformation, increase service quality, improve service delivery, or contain costs. It consists of integrating applications, restructuring labor resources, and using software applications throughout the organization. Robotic process automation is an emerging field within BPA that uses artificial intelligence.

The practice of performing robotic process automation (RPA) results in the deployment of attended or unattended software agents to an organization's environment. These software agents, or robots, are deployed to perform pre-defined structured and repetitive sets of business tasks or processes. Artificial intelligence software robots are deployed to handle unstructured data sets and are deployed after performing and deploying robotic process automation. Robotic process automation is the leading gateway for the adoption of artificial intelligence in business environments.

BPAs can be implemented in a number of business areas including marketing, sales, and workflow. Toolsets vary in sophistication, but there is an increasing trend towards the use of artificial intelligence technologies that can understand natural language and unstructured data sets, interact with human beings, and adapt to new types of problems without human-guided training. BPA providers tend to focus on different industry sectors but their underlying approach tends to be similar in that they will attempt to provide the shortest route to automation by exploiting the user interface layer rather than going deeply into the application code or databases sitting behind them. They also simplify their own interface to the extent that these tools can be used directly by non-technically qualified staff. The main advantage of these toolsets is, therefore, their speed of deployment, the drawback is that it brings yet another IT supplier to the organization.

The market is, however, evolving in this area. In order to automate these processes, connectors are needed to fit these systems/solutions together with a data exchange layer to transfer the information. A process-driven messaging service is an option for optimizing your data exchange layer. By mapping your end-to-end process workflow, you can build an integration between individual platforms using a process-driven messaging platform. A process-driven messaging service gives you the logic to build your process by using triggers, jobs, and workflows. Some companies use an API where you build workflow/s and then connect various systems or mobile devices. You build the process, creating workflows in the API where the workflow in the API acts as a data exchange layer.

Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success, and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability, and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.