With the widespread application of AI technology in the recruitment field, video interviews have gradually become an important means for enterprises to screen talents. However, how to adapt general AI video interview technology to specific vertical scenarios, such as blue-collar recruitment, has become a key challenge in design and operation. This article shares how to create a personalized, efficient and humanized recruitment experience for the recruitment needs of enterprises of different sizes through an in-depth review of the design practice of the entire process of AI video interview B-end in 58.com.
As AI technology penetrates from general-purpose video interviews to vertical fields, the specificity of blue-collar recruitment scenarios brings new design challenges – not only to meet the standardized screening needs of large B-end merchants, but also to solve the fragmented time management and reach efficiency problems of small and medium-sized B-end merchants.
This article combines universal design frameworks and blue-collar scenario practices to share how AI can become an “efficiency accelerator” and “decision-making partner” for recruiting B-end merchants through user insights, technology adaptation, and experience innovation.
01 B-end user portrait reconstruction
Through in-depth research on target users such as chain enterprise HR and small and medium-sized business managers, we found that recruiting B-end merchants presents a clear hierarchy:
Large enterprises (standardized assembly line requirements)
The core pain point of large enterprises is that manual screening is time-consuming and the evaluation criteria are not uniform, and AI needs to assume the role of “intelligent quality inspector” to automatically filter hard conditions such as age, gender, height, and health certificates, and at the same time achieve standardized scoring through multi-dimensional ability models (such as the “image and temperament” of waiters and the “persuasion logic” of sales posts) to reduce subjective bias.
Small and Medium Businesses (Fragmented Time Management)
Small and medium-sized business managers wear multiple hats and face problems such as fragmented recruitment time, high threshold for tool use, and delayed response leading to candidate loss, so there is an urgent need for AI to become a “24-hour recruitment butler” to automate the whole process from job recruitment requirements collection, resume screening to interview communication, and only push key decision-making information such as the “match” label for job seeker comparison to reduce time investment.
02 Three core strategies of scenario-based
1. Customized strategy: personalized customization to meet diverse needs
To meet the recruitment needs of enterprises of different sizes, we rely on AI interview systems and customized functions for key customers to create hierarchical and differentiated solutions:
Exclusive plan for small and medium-sized businesses: low threshold and high intelligence
How can product managers do a good job in B-end digitalization?
All walks of life have taken advantage of the ride-hail of digital transformation and achieved the rapid development of the industry. Since B-end products are products that provide services for enterprises, how should enterprises ride the digital ride?
View details >
In view of the limited manpower of small and medium-sized merchants and the pursuit of quick start-up, the platform lowers the threshold for use through lightweight design. AI algorithms will automatically analyze the job information posted by users and platform behavior data, and intelligently predict job recruitment requirements. Users can quickly complete the personalization with a simple drag and drop operation. The system automatically screens out C-end job seekers who are highly compatible with the job requirements, helping small and medium-sized businesses improve recruitment efficiency while saving labor costs.
Customized solutions for large enterprises: in-depth and specialized
In the face of the complex job requirements and refined recruitment requirements of large chain enterprises, the platform supports in-depth customized services. Enterprises can freely design their own evaluation systems and interview processes according to their own unique employment standards. From the construction of the job competency model, to the configuration of the structured interview question bank, to the multi-dimensional candidate scoring mechanism, it can be customized on demand to ensure that the recruitment process is highly matched with the corporate strategy and achieve accurate and efficient talent selection.
2. Multi-modal assessment: AI hierarchical hits the pain points of the post
In the field of recruitment, the requirements for different positions vary widely. Blue-collar positions focus on execution ability, while white-collar positions focus on strategic matching. In order to effectively meet the recruitment needs, we use AI large models to create multi-modal assessments.
Information layering + visual effects fusion
The evaluation report fully considers the characteristics of different customer types and scenarios, and carefully layers the information. At the same time, the report is combined with the video, and the head video is automatically played silently, intuitively presenting the status of C-end job seekers; The core information follows, giving priority to key dimensions by job category, such as blue-collar operational ability, attendance stability, white-collar skill matching, and project experience.
Efficiency upgrades
Users do not need to pay attention to the AI operation logic, and directly obtain a streamlined report of “AI Score + Video Clip + Core Tags”, with the help of this evaluation report, they can quickly conduct a comprehensive evaluation of job seekers and accurately screen out suitable talents.
3. Human-machine collaboration balance: Real butler + AI technology to build a closed loop of trust
In the core scenario of Enterprise WeChat, we have carefully created an exclusive customer service butler of “real image + intelligent kernel” – Xiao Zhang. Xiao Zhang plays a unique role in bringing “warm real-time service” to recruiters.
True companionship
Xiao Zhang appeared in the form of corporate WeChat/APP messages. Its avatar, nickname (such as “exclusive customer service butler Xiao Zhang”), and greetings are very close to real workplace personas. In daily communication, Xiao Zhang will take the initiative to send dynamic reminders of job seekers, such as “Boss, you haven’t watched the job search video sent to you by XXX just now, please check it out in time.” Such colloquial and down-to-earth expression breaks the coldness of traditional intelligent customer service and conveys the real companionship experience to B-end merchants.
Intelligent interaction
Xiao Zhang opened the show by affectionately calling “Hello boss”, instantly shortening the distance with the user. When promoting recruitment-related functions, Xiao Zhang can not only intelligently guide the recruitment B-side to complete account binding, traffic and other operations, but also give life-like care and advice at the right time, such as asking the recruitment B-side about the views of job seekers, prompting them to think deeply and give feedback.
This human-machine collaboration model achieves a balance between service efficiency and emotional experience: AI is responsible for handling massive tasks and ensuring the efficiency of information transmission; Xiao Zhang’s image of a real butler injects emotion into the service and wins the trust of merchants. The combination of the two has successfully built a closed loop of trust between C-end job seekers and B-end merchants, providing strong support for the efficient promotion of the recruitment process.
03 Vertical scene technology adaptation
In the practice of blue-collar recruitment scenarios, we summarize three design principles:
Scenarios take precedence over technology: Do not blindly stack AI functions during the design process, but focus on high-frequency pain points such as time fragmentation for small and medium-sized businesses, and transform technology into scenario-based solutions, such as AI interviewers and AI recruitment housekeepers.
Lightweight over complexity: Use “template + AI pre-fill” to lower the operating threshold, and use “labeling + fragmentation” to adapt fragmented attention to avoid technical terms and complex interactions.
Transparency over black box: Build user trust through decision visualization and manual intervention convenience, and upgrade AI from a tool to a trusted collaboration partner.
04 Write at the end
From general video interviews to vertical landing in blue-collar scenarios, the essence is the deep coupling of technical capabilities and industry characteristics. As a designer, you need to find a balance between technical possibilities and user feasibility – making the standardized process of large enterprises more efficient because of AI, making the fragmented recruitment of small and medium-sized businesses easier because of AI, and finally bridging the gap in recruitment resources of businesses of different sizes with technological dividends.
Good AI product design is not about showing the power of technology, but about making technology invisible in the scene – when recruiting B-end merchants no longer perceive the existence of AI, but enjoy the convenience brought by AI everywhere, it is the best state of human-machine collaboration.
