The key driver to realize intelligent automation丨Understand the application principles and case scenarios of RPA+AI in one article
At present, using AI (artificial intelligence) and RPA (robot process automation) to improve efficiency and productivity and realize intelligent automation has become the consensus and vision of many companies.
Business processes infused with RPA and AI not only expand the opportunities for automation, but also enable companies to use all the data at hand to make relevant business decisions.
How AI can help RPA robots move toward smarter
The use of AI combined with RPA can make process robots more "smart" and automate more business processes.
AI can learn from human behavior, and over time, use this learning to make itself better. For example, the Go master "Alphago" (Alphago) is a good example. The robot looks at tens of thousands of chess games by itself, learns experience and optimizes itself, and finally wins the championship in the chess field.
AI robots are also entering the field of imitating human decision-making and predicting results, with the potential to predict fraud, detect anomalies and analyze risks. As the number of AI technologies that power robots continues to increase, the value they bring to users and businesses will also increase.
In the field of reading and processing data, AI is widely used. Such data usually exists in business documents, e-mails, images, voices and texts, and it mainly relies on manual screening and processing by humans. AI can handle intelligent document processing without manual intervention.
Another area where AI plays an active role is to help people analyze data and provide valuable insights. AI real-time analysis can provide predictive and normative data points to find faults in the process and "self-repair" before it is too late.
Advanced connector to connect AI capabilities
RPA robots were originally process-based automation tools that followed fixed rules to operate and could only handle standardized formats. RPA itself has limited capabilities and needs to be enhanced with AI.
For RPA to become an advanced connector, a challenge must be overcome, that is, the control of unstructured data. Generally, only 20% of the data between different business systems is structured data that can be used directly (such as table databases, etc.), and the remaining 80% is unstructured data. In this part, there is still a lack of better solutions.
AI has this ability. AI is like having "mind ability", it can convert unstructured data, such as pictures, sounds, text, etc. into structured data, and then hand it over to RPA with "hands and feet ability" to operate. In this way, RPA can process the remaining 80% of the data.
Sub-assembly of some intelligent functions of AI into RPA connectors can solve many problems in many actual business scenarios. For example, scanning invoices, ID cards and other very standard documents can be realized by directly calling AI related plug-ins.
The RPA robot driven by AI can read and process structured and unstructured data from complex documents or images. With the help of AI technologies such as natural language processing (NLP), robots can understand the emotions and situations of human language. By learning from human corrections, the robot can improve over time, thereby increasing the productivity of the entire process.
AI has made traditional automation more flexible, allowing robots to adapt to subtle changes in processes or components (such as UI and document formats) and continue to improve over time. In addition, AI can lead to a better return on investment-redefining what can be done through automated planning.
RPA connector application scenarios combined with AI capabilities
Application scenario 1: Interface recognition
needs to identify how many interface elements there are in the software, and general RPA software can do it.
But some software does not run in the local operating system, but runs on a remote computer. In this case, you can use the AI recognition interface.
Through deep learning model training, in the model, as long as you press a key, the robot can automatically recognize, and the basic elements in the virtual machine, such as input boxes, buttons, labels, etc., are marked with green recognition boxes. It can not only identify elements with text, but also accurately identify blank input boxes without content.
Application scenario 2: Order management A
shipping company has a huge number of traditional customer inquiries. Its existing staff can only handle approximately 30% of queries in a timely manner.
By using AI-based RPA implementation solutions, companies can automate their query management processes end-to-end. After extracting the incoming query from the inbox, the custom NLP model will analyze the context of the customer query and extract data such as source, destination, weight, and material. With these basic parameters, the robot will automatically generate a response and send an e-mail such as a shipping label or cost estimate to the customer.
Application scenario 3: Contract identification The
financial field often needs to identify and process contracts. It is troublesome to extract key information from the body of the contract because there is no standard format for the contract, such as the location of Party A and Party B, the contract amount, and the expiration time.
Use the AI capabilities generated by RPA for training, extract the required total amount, billing period, supplier and other key information from the scanned copy of the contract, and then use RPA to send this information to the finance, providing a cost management basis for the finance.
Application scenario 4:
The property department of an online customer service real estate company usually sets up a centralized call center. In order to improve the user's service quality, when the owner makes a property call, he will directly access the call center.
The call center has more than 200 human customer services who can directly answer simple questions. For some similar maintenance problems, you need to make a record before repairing. AI can understand people's intentions and make simple responses.
In addition, the company mailbox can receive some emails every day, and someone needs to receive emails every day and classify them as customers or potential customers. These customers need to conduct a preliminary screening to determine the company's size and related information.
The business opportunity mail processing robot can automatically receive mail, query customers, and classify. If it is classified as a potential customer, it can also automatically check the general situation of the company, automatically reply to the email, and save the processed information in an Excel table.
The general trend of RPA+AI
AI's empowerment of RPA not only breaks through the barriers of automation scenarios, but also effectively promotes the extension of RPA scenarios and achieves a wider range of support for business scenarios. In addition, AI's ability to improve RPA products itself is also very significant and promising in the future (such as intelligent decision-making and self-learning), which is conducive to opening up all links of the industrial value chain, forming a closed-loop optimization of industrial processes, and bringing more far-reaching changes to the overall industry. significance.
With the continuous access of AI, RPA can unlock more application scenarios, and the complementary effects of RPA+AI will be infinitely amplified. Studies have shown that by 2022, the overall market for business processes will be restructured, mainly covering the service transformation around RPA and AI technology, with a total value of more than 8 billion US dollars.
The impact of the development of RPA and AI on the industry is undoubtedly huge. In recent years, more and more companies are increasing investment in RPA and AI technology research and development, hoping to achieve intelligent personalized services to improve operational efficiency and user experience.