In the process of digital transformation, enterprises have been exploring new ways to optimize business processes to enhance their market competitiveness. The application of technologies such as machine process automation (RPA), low process automation and artificial intelligence (AI) provides enterprises with more diversified automation solutions. However, with the continuous emergence of technology, while enterprises have more choices, it is inevitable that the phenomenon of "automation chimneys" will appear: it is difficult to interact with heterogeneous applications, data isolation, etc., which makes it difficult for enterprises to realize the leap from local optimization to global optimization when facing complex business processes across departments and systems.
Under the higher transformation requirements, super automation came into being. The concept of hyperautomation was first coined by Gartner and is defined as a systematic set of methods used by organizations to quickly identify, review, and automate as many business and IT processes as possible. Hyperautomation involves the coordinated use of multiple technologies, tools, or platforms to achieve business outcomes.
Minsheng **shares***
Wu Zherui, President of Information Technology Center.
Super automation dimensionality reduction solves the problem of digitalization of the whole business process
Hyperautomation is not a "smarter" RPA, but an integration of a series of technologies to optimize and reshape the entire business process. A typical financial business process usually needs to span multiple systems and involve operations outside the system. However, the existing automation solutions are often limited to process fragments in a single system, and cannot effectively solve the automation of the whole business process and the series and scheduling across systems. The advent of hyperautomation solves this pain point, emphasizing longer and more comprehensive process management and automation, cross-system tandem and scheduling, and the integrated use of multiple technologies.
At present, a series of vendors have invested in the hyper-automation track, including companies that originally focused on RPA, process engine, process mining, process management systems (such as OA, BPM), data and API integration, and automated operation and maintenance. Hyper-automation platforms from different vendors are developed from their original core products, such as RPA, BPM, or IPAAS platforms. Although the definition of the capability list of these platforms is not yet uniform, it can be seen that the core capabilities and components of the leading hyperautomation platforms are converging. This convergence is not accidental, but the pull of business needs pushes the functional characteristics of major platforms in the same direction: unified orchestration, scheduling, and control of heterogeneous applications. This also proves that the popularity of hyper-automation** is not a conceptual hype, but is driven by actual and sufficient business needs.
Capability analysis: Promote the efficient connection of the whole process of cross-system and cross-department business, and eliminate duplicate construction
Hyperautomation is more concerned with the optimization of the entire process of a certain business, and even the digitization of the entire business area, so it requires more capabilities than process automation (RPA). Depending on the business needs, the required capabilities will be emphasized. We look at the capabilities required for a typical hyperautomation solution into three categories: core capabilities, integration capabilities, and non-system capabilities.
1.Core capabilities for business orchestration, control, and analysis
At a minimum, the core capabilities include process orchestration capabilities, operation scheduling, interaction control, rule engines, data and process management and control interfaces, and process execution analysis and evaluation. On the platform, users can drag and drop out complex business processes, monitor the execution of processes in real time, and continuously analyze and evaluate the operation effect, so as to achieve efficient management and continuous optimization of business processes.
2.All connectable and configurable integration capabilities
As an important feature of hyperautomation, cross-system calls are indispensable for integration capabilities. The hyper-automation platform should have a wide range of integration capabilities, and be able to integrate different RPA platforms and AI services as needed. Integrate low-quality applications and self-service reporting platforms to build data and process processing functions for complex processes; Integrate APIs and databases to call external services and data, etc. As an important foundation for enterprise digitalization, the platform should also be connected to the enterprise's work platform, such as mobile tools such as OA, DingTalk, or Feishu, to achieve seamless process connection. In addition, it is also necessary to connect with various business systems of the enterprise and carry out unified control for different docking methods.
Some hyperautomation platforms have RPA and AI capabilities embedded in them, which can help improve delivery efficiency and reduce overall costs, but should not limit their ability to connect with other RPA and AI platforms. One of the advantages of hyperautomation is that it can quickly connect and invoke the automation capabilities you need, rather than focusing on endogenous ownership.
It's worth emphasizing that hyperautomation should be lightweight, configurable, and non-intrusive. Different from the traditional software development model, the core capabilities and integration capabilities are basically used in the design and operation of the process, basically in a no-or low-quality way. In this way, users can quickly and conveniently visually model business processes through the platform, which greatly improves production efficiency and reduces R&D thresholds and costs.
3.Non-systematic capabilities to break down departmental walls, process pull-throughs, and optimized design
Hyperautomation has the technical capability to integrate heterogeneous applications, but only by modeling and optimizing real businesses can the potential and value of hyperautomation be realized. Therefore, in order for a hyperautomation platform to succeed, in addition to the system itself, it also needs fintech integration talents who are proficient in process design and business models, and master the application of technology, as well as an organizational culture and atmosphere that supports breaking down departmental walls and pulling processes together.
Implementers need to have the ability to analyze and optimize processes, the ability to design and coordinate cross-system and cross-department docking with the help of the platform, and the ability to quickly transform business processes according to business changes. In the past, the need for RPA came from tapping into business operations that could be automated; The demand for low-quality applications comes from offline processes that have not yet been brought online. Nowadays, with a hyper-automation platform, the entire business process can be orchestrated onto the platform first, regardless of whether there are optimization points for upscaling or automation. By solidifying standard operating procedures (SOPs) on the platform, business processes are continuously optimized and even reshaped according to the continuous enrichment of technical means.
At the organizational level, the use of hyper-automation platforms can also effectively promote efficient cross-departmental collaboration. While connecting the upstream and downstream of business processes, standardize business operations and clarify rights and responsibilities to promote information exchange; On the basis of improving operational efficiency and risk management and control, it stimulates business innovation and inspiration. If each department completes its own work in isolation, it is difficult to achieve the overall optimal state of the entire enterprise. Not only does this lead to inefficiency, but it also increases the risk of errors and delays due to poor communication. Therefore, by promoting the construction of longer and more comprehensive business processes, breaking down departmental walls, and establishing a closer collaboration and communication mechanism within the organization, the work of various departments can be better coordinated and the operational efficiency of the entire enterprise can be improved (Figure 1).
Figure 1 Hyperautomation solution.
Practice case: Reshape the whole process of parameter management and establish a platform-driven cross-departmental agile operating model
The importance of effective collaboration and communication within the enterprise is constantly emphasized. How to establish a closer collaboration and communication mechanism within the organization and improve the overall operational efficiency, the Minsheng Business Parameter Management Center provides a new management idea (as shown in Figure 2).
Figure 2 Architecture of the Service Parameter Management Center.
In the traditional model, each business parameter is usually stored in different systems and managed and set by each department itself. This management method leads to a series of problems: unclear parameter usage chain, high maintenance cost, untimely adjustment and synchronization, and difficult to cover manual inspection. Relying on super automation capabilities, the parameter center comprehensively uses OCR, RPA and other intelligent tools to connect multiple business systems to realize parameter standardization, intelligent identification, collaborative mobility, dependency visualization, setting automation, and audit normalization. While improving the efficiency of cross-departmental and cross-system parameter operation, it also solidifies the operation experience, supplemented by daily parameter full audit, to fully ensure the smooth operation of the business.
In the hyper-automation mode, the collaboration model is transformed into a parameter center that supports and drives the entire business process, undertaking tedious but established rules. For example, parameter table identification, cross-system parameter setting, auditing, etc., and automatically send to-do and process information to business personnel; Business personnel are responsible for key parameter review, abnormal problem troubleshooting, etc., saving energy and focusing more on business development and innovation. The parameter management mode has also changed from vertical management of various departments to unified management on the platform. In the face of business parameter control across multiple departments and systems, which change from time to time and have extremely high requirements for timeliness and accuracy, this platform-driven agile horizontal management model will be particularly applicable. Management and business personnel can understand business progress anytime and anywhere, and at the same time, it also provides the underlying automation capabilities for the full life cycle management of financial products.
Business modeling: Hyper-automation enables digital operations excellence and adapts to more agile and flexible business upgrades
As can be seen from the above examples, hyperautomation opens up cross-departmental and cross-system processes in a low-cost manner, models business, and completes local data governance in the process of whole-process governance, analyzes unstructured data and implements it, providing necessary conditions for automation execution. This reflects the digitalization of processes and business elements, and is also a key area for the digital transformation of enterprises. With the advantages of super automation and lightweight, enterprises can keep up with the pace of business development and quickly increase the proportion of digitalization in the digital process in the field of management and business operations without waiting for the upgrading of business systems.
In this process, the super automation platform can also act as a "clutch" between systems, without worrying about the over-coupling between systems due to the whole process series, and minimize the impact of business system upgrades on upstream and downstream systems, and only need to update the corresponding nodes on the super automation platform. This digital operating model, which visually models and flexibly adjusts services, helps to improve management resolution, speed up business processes, and enhance decision-making agility, further promoting the digital transformation of the domain (Figure 3).
Figure 3 Hyperautomation facilitates digital operations.
Development prospects: Super automation superimposed on large language models, a subversive revolution in enterprise digital operations
AI plays a key role in automation. At present, the application of large language models (LLMs) in hyperautomation is mainly reflected in the processing and integration of unstructured data with the help of intelligent natural language processing technology, as well as the use of natural language to build processes or applications. In the future, large models will play a greater role in the design, execution, analysis and optimization of processes.
In the design stage, the rules and regulations and standard operation manuals (SOPs) are intelligently analyzed and analyzed, supplemented by manual inspection and adjustment, and executable process documents are quickly generated. This will simplify the process analysis and more accurately identify logical errors in the operation manual and the necessary compliance review and risk control points.
In the execution stage, the execution mode has changed from running the solidification process (such as using RPA to automate execution) to analyzing and dismantling the task by itself according to the execution goal, and independently adjusting and optimizing it in the process, which greatly increases the flexibility of the operation. For example, it automatically finds an element on a page without specifying it in advance, and upgrades from a rule engine to an intelligent engine for more intelligent process scheduling, gradually evolving into an AI agent.
In the analysis and optimization stage, according to the operation records and results, and with the original process design, the optimization points are automatically found. Because the super automation platform can match the process system, standard operating procedures and process design and store them in the platform, and monitor the operation status and analyze the operation data at all times, with the development of AI capabilities, it can comprehensively manage the whole life cycle of enterprise processes.
In general, the application of large language models will change the current situation of separating process design, process mining and process execution, and will no longer be limited to a single process and a single system, which will be a subversive revolution for process-related management technology and will also rapidly improve the level of digital operation of enterprises.