Peer Robot
AI-driven empathetic digital wellness product mitigating fatigue and injury for United Airlines.
OVERVIEW
AI-informed, collaborative fatigue prevention for United Airlines' ramp employees.
Fatigue among ramp workers at United Airlines has caused—and has continued to cause—injuries on the job. But management has not collected fatigue-related data, making the issue worse. Even when injuries occur, the reporting process is cumbersome.  A seniority driven-work culture and lack of trust between workers and management leads to emotional exhaustion. But Peer Robot provides emotional support and accurate injury reporting through tracking individual fatigue levels to combat ramp workers' fatigue, leading to an overall increase in employee well-being and safety.
Who are the ramp workers? They are contract workers, customer service, gate agent, and flight operation like ticket counter, backdrop, departure/arrival flights, moving bridge, etc.
My Role
Product designer and researcher
Core responsibilities
SMEs Interviews
Secondary Research
Design Strategy
Data Visualization
Wireframing
Prototyping
Final Design
Corporations
United Airlines
Airshop
Operations Safety
TechOps
Team
5 designers
Duration
August 2023 - December 2023 (5 months)
PROJECT GOAL
UA Corporate Safety partners wanted to reduce ramp employees' fatigue and associated injuries to enhance employee wellness.
PROBLEMS
Emotional exhaustion due to seniority-driven work culture and lack of trust. On-the-job injuries are routine because of a lack of fatigue-related data collection and a laborious reporting system.
"Lack of trust due to fewer job responsibilities..."
"Common no-shows result in cascading effect on scheduling..."
"Cohort reported back injury after 2 month later..."
SOLUTION
Leverage gen AI to support workers' emotional exhaustion and report accurate injuries by tracking individual fatigue levels.
FINAL DELIVERABLES
Use Case 1. Supportive conversation with Gen AI
Fatigued or injured ramp worker seeks assistance from Peer Robot for consultation and automated injury reporting.
Private, supportive consultant to reduce individual frustrations.
Peer Robot treats ramp workers with care and trust by recognizing their individual fatigue status resulting from work intensity. This support reduces stress and, in turn, engenders a more positive work culture.
Worker-friendly automated injury report to improve safety.
Peer Robot contributes to collecting accurate injury-related data by economizing the report process to allow ramp workers easy access, which prevents ongoing injury occurrences and missing data.
Use Case 2. Gen AI collaborative conversation with users
Manager uses Peer Robot to support their conversation with ramp workers
Affable AI chat assistant to build rapport within the team.
Contextual chatbot improves communication within a team of ramp workers by shifting in manager's communication style towards being more empathetic and friendly. It helps build trust and integrity but also prevents conflicts and emotional exhaustion among the workers. Generative AI acts seamlessly as a layer, like technological peers.
Use Case 3. Gen AI supportive insights for UA safety
United Airlines wants a report to pinpoint the causes of injuries and implement preventive measures.
Individual fatigue datatracking to optimize balanced work environment.
Peer Robot seeks to collect fatigue-related data—for example, on work location, shift schedule, sleep patterns, among others—to inform and improve worker productivity and support advantageous scheduling for management.
Peer Robot helps ramp workers to recognize the reason why they feel tired and what they need to do to recharge. The last feature assists managers in making informed decisions in rescheduling.
Insight analysis of injury causes, allowing for prevention risk.
United Airlines' Safety Team can get a report to pinpoint the causes of injuries and implement preventive measures. Employees receive optimized safety recommendations based on injury data, weather, fatigue levels, and more. They can view monthly injury data categorized by fatigue level, injury type, and location.
FINAL DELIVERABLES
Use Case 1. Supportive conversation with Gen AI
Fatigued or injured ramp worker seeks assistance from Peer Robot for consultation and automated injury reporting.
Private, supportive consultant to reduce individual frustrations.
Peerbot treats ramp workers with care and trust by recognizing their individual fatigue status resulting from work intensity. This support enreaches positivity and improves communication.
Worker-friendly automated injury report to improve safety.
It will build accurate injury data by economizing the report process to allow ramp workers to access easily, which prevent ongoing injury occurrences and missing data.
Use Case 2. Gen AI collaborative conversation with human
Manager uses Peer Robot to support their conversation with ramp workers
Affable AI chat assistant to build rapport within the team.
Contextual chatbot. Improve communication within a team of ramp workers by shifting in the manager's communication style towards being empathetic and friendly. It helps build trust and integrity but also prevent conflicts and emotional exhaustion among the workers. Generative AI acts seamlessly as a layer, like technological peers.
Use Case 3. Gen AI supportive insights for UA safety
United Airlines wants a report to pinpoint the causes of injuries and implement preventive measures.
Individual fatigue datatracking to optimize balanced work environment.
Tracking fatigue level by work intensity dataset. Work location, shift schedule, weather, noise, and etc decide worker's fatigue level so recognizing their situation is supportive for rescheduling.
The first feature helps ramp workers to recognize the reason why they feel tired and what they need to do to recharge. The last feature assists managers in making informed decisions in rescheduling.
Insight analysis of injury causes, allowing for prevention risk.
United Airlines' Safety Team can get a report to pinpoint the causes of injuries and implement preventive measures. Employees receive optimized safety recommendations based on injury data, weather, fatigue levels, and more. They can view monthly injury data categorized by fatigue level, injury type, and location.
Clickable Prototyping →
SECONDARY RESEARCH
Repetition, time-related pressures, erratic schedules, communication strain, and extreme work environments are common fatigue factors at the workplace.
SITE OBSERVATION
Emotional exhaustion from a seniority-driven work culture is a significant factor in fatigue.
O'hare Airport Tour, Airshop Tour
Veteran UA ramp workers said that "common no-shows result in cascading effect on scheduling" and "customer service [is difficult] because passengers claim that…everything is your fault". Key takeaways include ramp workers being asked to take control of uncontrollable situations, seniority-driven work culture, and a lack of trust between workers and management. For example, there has been a breakdown in communication between ramp workers, their direct supervisors, and those who make their schedules (at UA called the “manpower department”).
ANALYSIS AND SYNTHESIS MAJOR FATIGUE AREA
A seniority-driven work culture leads to erratic schedules for ramp employees and fosters a lack of trust in management, resulting in physical fatigue and emotional exhaustion.
FATIGUE IN UA FLIGHT OPERATIONS ISSUE
On-the-job injuries are routine because of a lack of fatigue-related data collection and a laborious reporting system.
DEFINE HMW THROUGH CASE STUDY
How might we leverage gen AI to enhance communication, reduce emotional exhaustion, and improve fatigue data accuracy to support ramp employee safety?
BREAKTHROUGH SOLUTIONS BY DISCONTINUITIES
AI-collaborative peer supports communication against emotional exhaustion and accurately observes fatigue data to immediately report injuries.
DESIGN STRATEGIES FOR SOLUTIONS
Leverage the fatigue index as objective data to understand potential sources of frustration and identify fatigue-inducing work patterns that lead to long-term consequences.
Tracking individual fatigue data
Metrics-based fatigue-level tracking will help management gain insight and understanding into individual ramp worker's fatigue levels. Fatigue-level tracker based on work intensity data from UA economizes the process, making it more worker-friendly.
Facilitating gen AI to mitigate various fatigue types across different contexts.
HOLISTIC SYSTEM VIEW
Transparent data access enhances accuracy, fosters support, and increases workplace positivity.
ITERATION
Addressed data privacy concerns of ramp workers by designing an interface 'keep private' option across multiple touchpoints.
Before
After
What I did
Alleviated privacy concerns about sensitive information being shared with United when ramp workers engage Peer Robot through confirmation.
Visualization allows for deeper, intuitive understanding of fatigue levels For stakeholders—including ramp workers, managers, and the safety team— to inform decision-making.
Aimed to visualize individual fatigue data with intuitive simplicity, using context-appropriate graphs and colors.
Developed visual and content hierarchy for easily digestible understanding.
VALUE PROPOSITIONS
Peer Robot amplifies ramp workers' voices.
Accurate Data for Safety
Tracking fatigue levels through UA's work intensity empowers the safety team to prevent injury. The worker-friendly automated injury reporting facilitates this.
Supportive Work Culture
Peer Robot enhances workplace positivity and improves communication through contextual chatbot. Ramp workers' personal concerns are kept private.
Consideration
Thoughtful scheduling and task management considering individuals contribute to enhancing overall work well-being by alleviating the stress of seniority-driven work culture.
IMPACT AND FEEDBACK FROM UA
Opportunity for empathetic culture-integrated support.
"Providing support for managers during tense moments of communication, especially when they lack training in race relations, is invaluable. Maintaing awareness of one's whereabouts throughout the day is crucial to prevent injury, even for those hesitant to share personal information."
- Corporate Safety Team @United Airlines
"The HR team utilizes Peerbot for its ability to synthesize supportive messaging, easing communication for managers lacking training. Integrating interactive assistance into our culture simplifies tasks for employees, ensuring they are guided through situations smoothly and without added burden."
- Airshop @United Airlines, Corporate Safety Team @United Airlines
WHAT I LEARNED
Implemented collaborative gen AI to address human relationship issues.
Analyzed fatigue areas at O'Hare Airport through ramp worker interviews to define problem areas, built an accurate fatigue data analysis system by understanding the work environment, and designed a generative AI-driven UX that considers work culture. Conducted weekly SME interviews to synthesize employee and employer perspectives, developed tailored communication strategies for stakeholders, valued field research to capture ramp workers' voices on work culture and injury report gaps, and utilized innovation methods to present ideas effectively.
Peer Robot
AI-driven empathetic digital wellness product mitigating fatigue and injury.
OVERVIEW
AI-informed, collaborative fatigue prevention for United Airlines' ramp employees.
Fatigue among ramp workers has been an issue related to injuries since a consistent risk of injury from no specific fatigue data and the complex injury reporting process. A seniority work culture and lack of trust from fewer job responsibilities lead to emotional exhaustion. Peer Robot provides emotional support and accurate injury reporting through tracking individual fatigue level to combat ramp workers' fatigue, leading to an overall increase in employee well-being and safety.
Who are the ramp employees? working in customer service, gate agent, and flight operation like ticket counter, backdrop, departure/arrival flights, moving bridge, etc.
My Role
Product designer /Researcher
Core responsibilities
SMEs Interviews
Secondary Research
Design Strategy
Data Visualization
Wireframing
Prototyping
Final Design
Corporations
Airshop
Operations Safety
TechOps
Team
5 Designers
Duration
August - December 2023 (5 months)
PROJECT GOAL
UA Corporate Safety partners wanted to reduce ramp employees' fatigue and associated injuries to enhance employee wellness.
PROBLEMS
Emotional exhaustion due to seniority-driven work culture and lack of trust. On-the-job injuries are routined because of a lack of fatigue-related data collection and a laborious reporting system.
Mentally, a work culture causes emotional exhaustion due to seniority and filled with lack of trust from fewer job responsibility. Physically, ongoing injuries occur due to no specific fatigue data and complex injury report system.
"Lack of trust due to fewer job responsibilities..."
"Common no-shows result in cascading effect on scheduling..."
"Cohort reported back injury after 2 month later..."
SOLUTION
Leverage gen AI to support workers' emotional exhaustion and report accurate injuries by tracking individual fatigue levels.
Ramp workers want to work in a well-balanced place where colleagues treat them fairly, in stressful situations where customers claim responsibility for uncontrollable issues. Injury prevention is required to stop consistent problems.
Peer Robot
AI-driven empathetic digital wellness mitigating fatigue and injury.
FINAL DELIVERABLES
Use Case 1. Supportive conversation with Gen AI
Fatigued or injured ramp worker seeks assistance from Peer Robot for consultation and automated injury reporting.
Private, supportive consultant to reduce individual frustrations.
Peerbot treats ramp workers with care and trust by recognizing their individual fatigue status resulting from work intensity. This support enreaches positivity and improves communication.
Worker-friendly automated injury report to improve safety.
It will build accurate injury data by economizing the report process to allow ramp workers to access easily, which prevent ongoing injury occurrences and missing data.
Use Case 2. Gen AI collaborative conversation with human
Manager uses Peer Robot to support their conversation with ramp workers
Affable AI chat assistant to build rapport within the team.
Contextual chatbot. Improve communication within a team of ramp workers by shifting in the manager's communication style towards being empathetic and friendly. It helps build trust and integrity but also prevent conflicts and emotional exhaustion among the workers. Generative AI acts seamlessly as a layer, like technological peers.
Use Case 3. Gen AI supportive insights for UA safety
United Airlines wants a report to pinpoint the causes of injuries and implement preventive measures.
Individual fatigue datatracking to optimize balanced work environment.
Tracking fatigue level by work intensity dataset. Work location, shift schedule, weather, noise, and etc decide worker's fatigue level so recognizing their situation is supportive for rescheduling.
The first feature helps ramp workers to recognize the reason why they feel tired and what they need to do to recharge. The last feature assists managers in making informed decisions in rescheduling.
Insight analysis of injury causes, allowing for prevention risk.
United Airlines' Safety Team can get a report to pinpoint the causes of injuries and implement preventive measures. Employees receive optimized safety recommendations based on injury data, weather, fatigue levels, and more. They can view monthly injury data categorized by fatigue level, injury type, and location.
Clickable Prototyping →
proJect Goal
UA Corporate Safety partners want to look at technology and process adjustments that can reduce fatigue and associated injuries to enhance employee wellness.
Discover
Ramp Worker Context
Background
Fatigue in United Airlines(UA) flight operations groups has been an issue when it comes to injuries in the operation. Ramp workers face mental and physical exhaustion caused by communication challenges and dissonant team dynamics.
Who are the ramp workers?
Working in customer service, gate agent, and flight operation like ticket counter, backdrop, departure/arrival flights, moving bridge, etc.
Needs
Ramp workers prefer working in a well-balanced place where colleagues treat them fairly, in stressful situations where customers claim responsibility for uncontrollable issues. Injury prevention is required to stop consistent problems.
"Lack of trust due to fewer job responsibilities..."
"Common no-shows result in cascading effect on scheduling..."
"Cohort reported back injury after 2 month later..."
SECONDARY RESEARCH
Repetition, time-related pressures, erratic schedules, communication strain, and extreme work environments are common fatigue factors at the workplace.
SITE OBSERVATION
Emotional exhaustion from a seniority-driven work culture is a significant factor in fatigue.
O'hare Airport Tour, Airshop Tour
Veteran UA ramp workers said that "common no-shows result in cascading effect on scheduling" and "customer service [is difficult] because passengers claim that…everything is your fault". Key takeaways are ramp workers are asked to take control of uncontrollable situations, lack of trust due to fewer job responsibilities, Work culture based on seniority, Manpower department in charge of scheduling one-sided communication with ramp workers who they oversee, Lead ramp workers(managers) are responsible for staff and problems that arise on the job.
ANALYSIS AND SYNTHESIS MAJOR FATIGUE AREA
A seniority-driven work culture leads to erratic schedules for ramp employees and fosters a lack of trust in management, resulting in physical fatigue and emotional exhaustion.
FATIGUE IN UA FLIGHT OPERATIONS ISSUE
On-the-job injuries are routined because of a lack of fatigue-related data collection and a laborious reporting system.
Mission statement
Our mission is to enhance safety by reducing fatigue amongst airline employees through technology and new processes, aiming to lessen injuries and boost on-time productivity.
DISCOVER OPPORTUNITY AREAS THROUGH CASE STUDY
How might we leverage gen AI to enhance communication, reduce emotional exhaustion, and improve fatigue data accuracy to support ramp employee safety?
Reasoning by analogy, "HMW help improve relationships between ramp workers and reduce seniority?"
1. How might we optimize gathering and using data to identify and improve problem areas?
2. How might we develop team building to help improve relationships between ramp workers and reduce hierarchy?
3. How might we better engage users to utilize tech to help prevent future injuries when fatigue does occurs?
4. How might we use tech to improve ramp worker scheduling?
BREAKTHROUGH SOLUTIONS BY DISCONTINUITIES
AI-collaborative peer supports communication against emotional exhaustion and accurately observes fatigue data to immediately report injuries.
DESIGN STRATEGIES FOR SOLUTIONS
Leverage the fatigue index as objective data to understand potential sources of frustration and identify fatigue-inducing work patterns that lead to long-term consequences.
Tracking individual fatigue data
Accurate Fatigue Data Analysis System by tracking individual fatigue. For Current Injury Reporting System. Fatigue level tracker based on work intensity data from UA and economize the process, making it more worker-friendly and efficient.
Facilitating gen AI to mitigate various fatigue types across different contexts.
For Current UROC System, technology works as a layer to reduce emotional exhaustion by shifting the communication style in which ramp workers receive feedback to something more friendly.
HOLISTIC SYSTEM VIEW
Transparent data access enhances accuracy, fosters support, and increases workplace positivity.
develop
Adaptive solution
∙ AI-Powered Emotional Support
∙ Fatigue Analysis + Automated Injury Report
ITERATION
Addressed data privacy concerns of ramp workers by designing an interface 'keep private' option across multiple touchpoints.
Before
After
What I did
Intended to engage ramp workers to use Peer Robot private consultant without any concerns about sharing private information with UA by providing them with confirmation.
Aim to navigate managers, ramp employees and safety team share the consistent data that is easier to understand by prioritizing fatigue context in decision-making and developing visual and content hierarchy.
develop
3 Big Takeaways
1. Empowering a supportive work culture and safe environment via emotional support.
2. Optimizing conversation and facilitating injury report with Gen AI that seamlessly works as a layer like technical peers.
3. Improving work balance and mitigating fatigue through data tracking.
VALUE PROPOSITIONS
Peer Robot amplifies ramp workers' voices.
By mitigating emotional exhaustion, facilitating automated reporting for injuries, and overseeing injury data to prevent and predict further incidents.
Accurate Data Safety
Supportive Work Culture
Fairness
Tracking fatigue levels through UA's work intensity empowers the safety team to prevent injury situations. The worker-friendly automated injury reporting facilitates their issues.
Technical peer enhances workplace positivity and improving communication through contextual chatbot. Ramp workers' personal concerns keep in private.
Thoughtful scheduling and adaptive task management considering individuals contribute to enhancing overall work well-being by reducing seniority, ensuring fairness for everyone.
IMPACT AND FEEDBACK FROM UA
Opportunity for empathetic culture-integrated support
"Providing support for managers during tense moments of communication, especially when they lack training in race relations, is invaluable. Maintaing awareness of one's whereabouts throughout the day is crucial to prevent injury, even for those hesitant to share personal information."
- Corporate Safety Team @United Airlines
"The HR team utilizes Peerbot for its ability to synthesize supportive messaging, easing communication for managers lacking training in race relations. Integrating interactive assistance into our culture simplifies tasks for employees, ensuring they are guided through situations smoothly and without added burden."
- Airshop @United Airlines
WHAT I LEARNED
Implemented collaborative gen AI to address human relationship issues.
∙Define problem area through analyzing fatigue areas and site observation in O'hare airport by interviewing ramp workers.
∙Build accurate fatigue data analysis system by understanding their work environment and common/major fatigue areas.
∙Generative AI-driven UX design considering their work culture in seniority and conflicts.
∙User Interface design with data visualization to express from complexity to simplicity.
∙Synthesizing the different point of view both employees and employers in design through weekling SMEs interviews with UA corporations.∙The ways of communication skills for each different stakeholders.∙Value of field research by listening to ramp workers' voice that couldn't discover in secondary research like work culture and reasons of missing injury reports.∙Discovering opportunities by innovation methods and frameworks that help us to explain our ideas to different stakeholders.
Analyzed fatigue areas at O'Hare Airport through ramp worker interviews to define problem areas, built an accurate fatigue data analysis system by understanding the work environment, and designed a generative AI-driven UX that considers work culture. Conducted weekly SME interviews to synthesize employee and employer perspectives, developed tailored communication strategies for stakeholders, valued field research to capture ramp workers' voices on work culture and injury report gaps, and utilized innovation methods to present ideas effectively.
APPENDIX
Approach
UA desired outcomes
- Look at the existing processes and introduce ideas for new processes and incorporation of new technology. 
- Identify the Ramp Service Employees.
- Identify areas that can lead to fatigue by analyzing exist process.
- Use cases for technology that can mitigate fatigue.
- Report out on technologies considered and findings leading to your decision.
Secondary Research
Desk Research
Understand ramp workers' situation: identify fatigue areas by analyzing existing process
Repetition
Seniority
Time Pressure
Erratic Schedule
Case study
Reasoning by analogy, "HMW help improve relationships between ramp workers and reduce seniority?"
1. How might we optimize gathering and using data to identify and improve problem areas?
2. How might we develop team building to help improve relationships between ramp workers and reduce hierarchy?
3. How might we better engage users to utilize tech to help prevent future injuries when fatigue does occurs?
4. How might we use tech to improve ramp worker scheduling?
Discontinuities
Breakthrough solutions, "Peers' Emotional Support"
Data accuracy
Emotional support
Streamlined
injury report
Schedule management
Define point of view
Opportunity Areas
Ramp workers need a supportive and safe environment for injury prevention.
Build Use Cases
Supportive conversation with Gen AI
Manager uses Peer Robot to support their conversation with ramp workers
Fatigued or injured ramp worker seeks assistance from Peer Robot for consultation and automated injury reporting.
- Helps ramp workers to recognize the reason why they feel tired and what they need to do to recharge.
- Peer Robot helps to build injury reports quickly and easily.
Gen AI collaborative conversation with human
Manager uses Peer Robot to support their conversation with ramp workers
- Peer Robot provides emotional support to reduce conflict and emotional exhaustion between ramp workers and managers. Peer Robot can shift the communication style in which ramp workers receive feedback to ensure friendliness.
- Peer Robot assists the manager in making informed decisions in rescheduling. Furthermore, Peer Robot delivers the changed schedule to alternative ramp workers to reduce conflict.
Gen AI supportive insights for UA Safety team
United Airlines wants a report to pinpoint the causes of injuries and implement preventive measures.
- Helps managers to intelligently schedule workers based on fatigue levels and other insights.
Define Problem Areas
a. Emotional exhaustion
Communication challenges, Dissonant team dynamic, lack of trust
b. Missing/Inaccurate data
No specific fatigue data, Ongoing injury occurrences, Complex injury report system (8 pages)
“Manager and ramp workers don't trust each other due to temporary job contracts."
“ There is no specific data on fatigue, and a consistent risk of injury. ”
Design Strategy
a. For Current UROC System
Technology works as a layer to reduce emotional exhaustion by shifting the communication style in which ramp workers receive feedback to something more friendly.
b. For Current Injury Reporting System
Fatigue level tracker based on work intensity data from UA.
Economize the process, making it more worker-friendly and efficient.
“ Tracking Fatigue Level”
“ Leverage Generative AI Capabilities”
Final Deliverable
Peerbot is...
Amplifies ramp workers' voices by mitigating emotional exhuation and facilitating automated reporting for injuries. Oversees all injury data to prevent and predict further incidents.
Hi, I'm Peerbot
I empower ramp workers by streamlining processes, reducing pressure, and automating injury reporting. I also give you great suggestions to prevent further injuries!
View of How the System Works
Valuable Impact
Accurate data, Supportive culture, Fairness
Peer Robot amplifies ramp workers' voices by mitigating unnecessary emotional consumption, facilitating automated reporting for injuries, and overseeing all injury data to prevent and predict further incidents.
a. Transparent Data
Accessing more accurate data for further optimization
b. Supportive Culture
Enhancing workplace positivity and improving communication. Emotional support is provided to foster a supportive work culture
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