AI-powered Screening

The project was aimed at reducing screening time and manual effort for recruiters, laying the groundwork for a revamped user experience for applicants seeking and applying to internal opportunities. Collaborating with Doosan, I spearheaded the entire design process to address employee retention issues, working alongside three software engineers and another designer.

Role

Design Lead
User Research
Prototype
UI Design

Team

Project Manager
Solution Experts
3 Engineers
2 Designers

Platform & Tools

Responsive Web
SAP Fiori design system
Figma

Period

3 months

Button Text
overview image showing what the screen looks like.
Challenge

92%

of employees who want to change their careers left the company.

37%

of employees left after internal transfer within 3 years.

The company struggled with high employee turnover, leading to increased costs for hiring new staff. Additionally, employees who moved to different roles within the company often remained unhappy, indicating deeper issues with job satisfaction.

Benefits
As a benefit, the turnover decreased by over 50 percent.

Reduced turnover by 39%

The recruitment cost decreased after deploying.

Recruitment cost down

What are the right problems?

From the user interviews, we identified and focused on three primary problems in the existing process.

Lack of System

Employees struggled to find and apply for internal roles due to a lack of a centralized system.

Manual Screening

Recruiters relied on spreadsheets for candidate management, prolonging the application process.

Lack of Privacy

The absence of privacy in the internal job application process led to workplace tension, as recruiters required manager referrals.

How might we build a healthy, sustainable internal hiring culture through technology?

How I reached these insights

Exploration workshop

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Discussion stickies on the whiteboard.

Scoping the project

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User interviews

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Discover workshop

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Define right problems

Synthesize data

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Research summary

How I did address those problems

From the user interviews, we identified and focused on three primary problems in the existing process.

Easy accessibility

Create an HR solution based on the process and user needs

Recruiting Support

Design an HR solution that can help recruiters spend less time on paperwork, phone calls, and processing applicants

Data Protection

Create a system that prevents exposure of job application information

Design Challenge

Recruiters had the challenging task of manually reviewing hundreds of resumes in first screening process.
Thereby reducing the workload for recruiters and allowing them to focus on more strategic tasks.

To relieve recruiters from the burden of screening tasks

To provide recruiters with more insights into applications

Prototyping and Iterations

We leveraged the machine learning capability to the system to provide recruiters insightful first screening results, which reduce the tedious tasks and make it shorter by 3 times.

Initial prorototypes
How I reached the final design

Co-creation session

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co-creation session to develop initial ideas

Rapid prototypes

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User validations

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prototypes

Design iteration

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Development

Design mocks

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final design
artificial intelligence
AI Screening
Gaining Insights into Potential

Recruiters can now leverage intelligent screening feature to swiftly access valuable insights about potential candidates.

The flow of recruiter's job from screening through selecting and reporting.
The final design having the dashboard and the application list with scores.

The results after deployment

We leveraged the machine learning capability to the system to provide recruiters insightful first screening results, which reduce the tedious tasks and make it shorter by 3 times.

  • Employee retention increased by 32%
  • The productivity of recruiters increased by 2.8 times
  • The hiring process sped up by 1.4 times

Improvements for next

Design the third explanation level visually to offer deeper insights into applications.

The level one of Improvements for next.right arrowThe level two of Improvements for next.right arrowThe level three of Improvements for next.

Develop Auto-generated job posting use cases based on the needs of business status.

The second point for future improvement is to integrate all the system to make the hiring process simple and smart.

The results after deployment

  • Many aspects to be considered when designing AI system such as a volume of data, integration, AI ethic and so on.
  • If utilizing AI capability, users can spend their time for more creative tasks by reducing redundant works.
  • AI provides new Interactions which make users feel emotionally in different ways.

Appreciation

“It was a great opportunity to experience the whole process of Human Centered Design approach, development, and collaboration. It was a delightful experience due to the strong backup of UX designers that made such development possible.”

Project manager from the client