RPA is considered a cutting-edge technology for handling repetitive and regular business. At present, RPA is widely used in the digital upgrading of the business of government enterprises, helping government and enterprises to improve quality, reduce costs and increase efficiency, but in different industry scenarios, the application form of RPA tends to vary, to really intelligent RPA digital employees, for example, has been for a number of financial, carriers, energy, e-commerce, and other areas of enterprises and government to provide digital transformation (intelligent + automation) solutions. You can also through the following articles to better understand the real usefulness of RPA
Want to know what RPA is doing, the first need to have a clear understanding of the development history of RPA, the next, we sorted out the state of the different phases of RPA applications for you.
I. When RPA first appeared:
The term RPA appeared in 2000. At this time, RPA has been different than the previous "RPA-like", "take the essence, remove the trough meal, push out the new, the old and the new" can well summarize the development of this stage.
It has been able to effectively combine AI and automation technologies, with the most popular application being OCR, which allows RPA software to no longer rely on code for screen capture, but allows users to use drag-and-drop functionality in a visual way to create process management workflows and automate repetitive tasks. This approach lowers the barrier to entry for users, allowing them to quickly access data and build processes without the need for specialized coding knowledge, and is where RPA's value lies.
But this stage of the RPA in the landing process is difficult to be accepted by the market, well such as digital accumulation, the choice of business productivity, the main contradiction in the growth of the enterprise shackles, etc. These factors, as well as technological means can not be solved by the manufacturers of automation, part of the automation seems to be like a chicken ribs, because of the lower labor costs, often considered to be an increase in the number of people.
Two, after the emergence of RPA:
With the RPA began to solve more complex tasks through a simple operating system, and easy to operate, more and more industries put into use on a large scale. For example: BPO (Business process outsourcing , business process outsourcing).
BPO sees RPA as a key driver of efficiency and productivity. The two go hand in hand. With RPA, BPO can rapidly automate offices with lower cost-effectiveness and faster responsiveness. At the same time, RPA was able to land in the outsourcing space.
Then, after 2010, with "Internet+" and "Smart+" on the agenda, RPA was a technology that saw rapid growth in a wide range of industries, especially in insurance, healthcare, banking, and new retail.
The implementation of RPA has dramatically reduced labor costs and increased productivity while reducing errors.
Three, 2020 Avery report a leaf to know the autumn - "2020 China RPA Report"
In the past two years, RPA vendors have mushroomed in large numbers, how to form their own advantage in the competition? Positive Intelligence's approach is: by virtue of the innate AI advantage, constantly improve AI competitiveness at the same time, in-depth more business scenarios, empowering users to realize the automation of different unstructured data business scenarios (e.g., invoice extraction, speech-to-text conversion, etc.), to create the most accurate, efficient, and user-aware intelligent automation products, i.e., the octopus digital employee.
In addition to the traditional "three-piece" architecture, Z-Brain, a self-developed AI capability platform, integrates a variety of AI capabilities, including Chatbot, data platforms, and algorithmic platforms. Z-Brain integrates a variety of AI capabilities including Chatbot, data platform, algorithm platform, etc. Among them, in the field of natural language processing, Z-Brain covers the latest algorithms including BERT, ALBERT, RoBERTa, etc. In the field of computer vision, Z-Brain covers the latest algorithms such as DB, PMTD, RARE, etc. It has the capability of self-learning and efficient iteration. Equipped with self-learning, efficient iteration, automatic parameter tuning, and multi-scene fusion technologies, it can output AI components to accomplish intelligent decision-making in large-scale complex scenes.
An excerpt from the article: "At the level of AI technology, the core technology of Positive Intelligence, Cloud Brain, adopts the best algorithms in the industry, which can be personalized according to the business scenarios of different users, so as to achieve the data model that best meets users' business needs; the data training cycle usually takes about 1 to 30 days. Usually, the data training cycle is about 1-30 days, and the length of training time depends on the complexity of data and business. To put it simply, "Real Intelligence" is to empower users with AI capabilities to automate different unstructured data business scenarios (e.g. invoice extraction, speech-to-text conversion, etc.), and RPA is just a carrier. Therefore, the future development of RPA must be AI-based, through AI to create product differentiation and competitiveness ......"
"The top-down application strategy shows that the organization's managers see the key role played by RPA in the development of that digital transformation can be effectively achieved through RPA; bottom-up indicates that the actual need exists, and that both needs generally **** together. Therefore, only the existence of just automation will further stimulate the development of the RPA market, and the market will awaken faster and faster. Initially, it is just the first application in the field of finance, banking and other large amounts of data, but as AI technology continues to iterate to become more and more intelligent, it will see RPA in more business scenarios."