Before we start this topic, we need to have a basic understanding of the stage in design. Readers can learn about Alibaba's thinking and tool progress on the R & D mode and design engineering in design through the article "see conf design engineering trilogy! Exploring thinking and practice in the" production research collaboration mode "in the new environment". We manage multi role collaboration and consumable materials in the product production link, so as to accelerate production efficiency and reduce costs. At the same time, the experience baseline of the product can be guaranteed.
Today, the focus of our discussion is not the production of system products, but the user experience trend of b-end system and the entry point of rookies in this field. We will discuss this topic in two phases. The first phase will discuss the trend of intelligent experience of b-end system, and the second phase will discuss the changes brought about by the application of intelligent capabilities.
Trend and experience demand of b-end system
First of all, let me take a look at the development history of b-end system at the product level:
In the 1980s, IBM and other companies have provided a computer-based management system to realize the closed-loop management system of enterprise raw material management, production and processing management, and employee work time management.
In the 1990s, such a management system became more mature, which can realize the capabilities of financial prediction, production capacity and resource scheduling, and truly become a system tool that can be used for product quality management, resource management and financial management to assist enterprise managers in making decisions. We call such a system ERP (Enterprise Resource Planning).
Since 2000, the maturity of Internet technology has enabled the enterprise system to realize the data exchange ability with the upstream and downstream systems of the supply chain and customers, strengthened the contact between enterprises in all links of the supply chain, and made it more convenient for enterprise decision makers to carry out cross enterprise collaboration.
Since 2010, ERP products based on cloud computing have gradually stepped onto the historical stage by building non localized systems with SaaS, PAAS and other technologies. The ability to customize to meet the personalized business demands of enterprises is testing the scale of the system "application market" and the secondary development ability, of which the secondary development ability has attracted the attention of the industry with low code and zero code.
We can observe that the basic trend of the industry has four characteristics:
- From solving the problem of enterprise single point management to solving the problem of Enterprise Consolidation Management
- From focusing on their own internal management and production efficiency to focusing on the data and collaboration efficiency of the supply chain
- From service matters (enterprise production, etc.) to service people (enterprise decision makers, system users)
- From focusing on the functions of system modules to focusing on the ability of rapid and low-cost configuration and landing of system modules
Based on this, we have reason to believe that the evolution of the traditional management system to the digital era is currently under way. At present, we need to pay attention to the informatization of the system by means of data processing and the intelligent experience means guided by the independent decision-making of the system to achieve the goal.
We take the role and purpose of the system in the production process on the production relationship as the starting point to sort out our ideas, and call the stage in which the system takes the production management as the purpose and revolves around human processing as the traditional era, and the stage in which the system takes the data processing ability to replace human beings to make repeated decisions as the purpose as the digital era.
It is not difficult for us to find that the system in the traditional era requires us to pay more attention to the capabilities provided by the system during production and R & D, and it is best to develop it in a low-cost and fast way for mass production. With this R & D mode, various technologies and design platforms are bred. Typical ones are salesforce open platform, ant's antd, etc. the rookie's middle stage is called cone, which means Cainiao one. Cone provides design engineering, standardization, low code R & D, capability service platform, applet and other capabilities, focusing on optimizing system production links, cost and efficiency, capability module reuse and experience standardization.
In the era of evolution, in addition to the above production factors that need to be paid attention to, the middle office should also pay attention to the experience changes brought about by the change of system purpose. In order to reduce the dependence of the system on people and let the system replace human beings to make decisions with high repeatability and low risk, the middle stage needs to provide the ability of solution precipitation and system judgment of trigger conditions, as well as the supporting interactive form of intelligent experience.
Pioneer of intelligent transformation
It sounds as if intellectualization is still far away. In fact, this era has begun. Let's take a look at several examples of intelligent transformation of enterprises:
Google adjusted its corporate strategy from mobile first to AI first in 2016, followed by a gradual move towards intelligent and humanized user experience of Google products.
Google photos provides more than 5billion photos to be viewed by Google photo albums every day. AI can help users make it easier to recognize, beautify and share photos. It uses AI to segment images, automatically repair overexposed and underexposed photos, and can also conduct color correction and other processing on photos. Similarly, Google assistant adopts the deep learning WaveNet technology, which can provide 6 kinds of natural voices that are difficult to distinguish between true and false, and provides more than 30 language services in more than 80 countries and regions (no coverage in China). With this technology, people's experience in content reading will become more relaxed and humanized. Almost all of the best used Google Chrome plug-ins in the market serve Gmail intelligent reply. These plug-ins can distinguish the object and content of emails, automatically enter work-related documents into the corresponding module of the system, and automatically reply emails according to preset conditions. This intelligent experience will be a blessing for email workers who need to deal with a large number of customers every day.
IBM divides intelligence into three stages:
In the first stage, based on cloud computing, Internet of things, data analysis and other capabilities, build process automation capability (RPA) to realize process automation, and human beings only need to make decisions;
The second stage is intelligent automation (AI, RPA), which combines artificial intelligence and automation technology to provide better customer experience for mankind. For example, AI can help create guidelines for using RPA automated processes, and AI uses data to quantify and calculate process efficiency, simplify it, and achieve higher efficiency.
The third stage is real business intelligence (AI). IMB integrates enterprise AI into the solution of open hybrid cloud to realize natural language processing, credibility, automation and the ability to run anywhere. This is not the case here.
In 2019, Philips applied artificial intelligence technology to the medical field. Through image recognition, voice recognition, language processing, data mining and cognitive reasoning, it served in the fields of prevention, diagnosis, treatment, rehabilitation, drug research and hospital management, so that doctors' work changed from "intuitive medicine" relying on experience to "precision medicine" relying on data. We found that the biggest gains from intelligent technology are actually product service quality and user experience.
Intelligent Atlas of b-end system
If we are still using traditional systems today, how can we gradually make the system intelligent? I'm afraid it's unrealistic to achieve it step by step. What goals should be achieved step by step?
Let's sort out our thinking. There are three elements of intelligent system:
- Perception of the system - the system needs to know what happened to the business
- Decision making ability of the system - the system needs to make decisions about what processes to implement (including whether human intervention is required)
- Executive ability of the system - intelligent system is good at replacing human beings to do a lot of low-risk and highly repetitive work, and it is also the ability to directly produce production value.
Then we can divide the degree of system intelligence according to three elements:
- Whether the system can complete tasks automatically (process automation)
- Whether the system can independently initiate and complete tasks (decision-making, process automation)
- Whether the system can complete self evolution (perception, analysis, optimization and execution ability)
Based on this idea, we divide human participation in the implementation of the system, and intelligentize the system into five stages:
Basic labor (S0): all information processing and task disposal must be completed manually. The system only stores and presents information, such as online forms.
Assisted execution (S1): all tasks must be handled manually, but in the process of disposal, the assistance of the system can be obtained. The system provides data analysis help in task distribution, push or processing, such as recommendation, prediction, etc.
Conditional intelligence (S2): manually complete the setting of rules and processes, or complete the establishment of knowledge base. The system is based on preset rules, only experience or intelligent information decision analysis, and can automatically complete specific task processing.
Highly intelligent (S3): only need to train the system manually to complete system optimization. Based on state perception and real-time analysis, the system can make independent decisions, mine tasks and complete tasks automatically.
Complete intelligence (S4): the system has cognitive ability, can independently complete evolution and improvement, and can independently optimize business processes or propose new solutions.
In the process of construction, how do we define the stage of system intelligence?
Through a large number of theoretical verification, we simplified it into the table above, and defined the intelligent level of the system through information presentation, task organization and distribution, task processing capacity, rule process setting, autonomous Optimization & upgrading, who will complete the main work.
Rookie's system products are also gradually evolving to higher-level intelligent capabilities through mid-range capabilities. So what capabilities do we need to build to realize the intelligent transformation of the system?
At each stage, we have set the necessary capabilities with high priority (precious blue) and sustainable strengthening capabilities (blue gray). We believe that these capabilities are the key to the intellectualization of the system, and the resulting changes are disruptive changes in the user experience.
The need for capacity-building means the cost. Based on the rookie's six-year experience in the design of stage landing, not all systems need to pursue the complete intelligence of S4, but need to decide the system intelligence level according to the scene and demand, as needed. The more repetitive the process is, the more intelligent the decision-making risk is, the more the system needs human experience to judge, and the system only needs to provide data analysis ability.
In the next article, "intelligent design in the middle stage - down", we will discuss the changes of user experience caused by the application of some key capabilities in the system. Welcome to pay attention.
