My vision was to design and develop the AI-powered Robotics Smart Control Hub as an MVP (Minimum Viable Product) that would address critical gaps in industrial robotics operations. This case study showcases how I shaped the UX strategy, crafted a detailed product roadmap for the MVP, and provided visionary leadership in aligning business goals with user needs to deliver a market-ready solution that could scale over time.

Background

End-to-End MVP development.
Defining UX Strategy.
Creating UX Roadmap.
Concept Design.
Capacity Planning.
Cross functional team collaboration with Design sprints and workshop.
Ensuring intuitive UX and design delivery.

TASK

Design Process

01 Discovery

Understanding Business Goal

Reduce robot downtime and increase operational efficiency for factories.
Improve collaboration between factory operators and remote robotics engineers to speed up troubleshooting.
Ensure data security in robot control and diagnostics to meet compliance standards.
Differentiate the company from competitors by offering an intelligent and user-friendly robotics control platform.

Revenue Stream and Business Value

Subscription Model: Offer a SaaS-based Smart Control Hub for companies managing large-scale robotics deployments.
Value-Added Services: AI-driven diagnostics, predictive maintenance, and training modules as premium features.
Hardware-Software Integration: Upsell to existing industrial automation customers and system integrators by bundling with robots.
Consulting & Support: Monetize enterprise-level setup, customization, and customer training.

End User Pain Points

Complex Robot Monitoring & Troubleshooting only with Expert Support
Delayed Issue Resolution
High Operational Costs Due to Downtime
Security & Compliance Risks

COST FACTOR

CURRENT LOSSES

EXPECTED PROJECTION WITH ROBOT CONTROL HUB

AI assisted Copilot reduces the training and support user
with expert support

$500K–$1M per month

30% reduction in downtime (~$600K savings/month)

Unplanned Downtime

50% faster problem resolution (~$1M annual savings)

Average 40+ hours of delayed issue resolution

Inefficient Collaboration

Training & Onboarding

$200K in training costs per year

Cybersecurity Risks

Encrypted communication ensures compliance

$5M+ liability risk

Cost Analysis & Investment

As part of initial stakeholder meeting , i tried to understand what is the cost impact and what is the projection they expect by implementing solution aka. ROI (Return on Investment)
.

Defining Strategy

It’s often chaotic at the start, with numerous kickoff meetings involving multiple stakeholders to clarify the vision and goals. Then analyze the feedback collected from meetings and interviews to identify common themes, priorities, and gaps in understanding. But this step is crucial for aligning the team towards one direction. After stakeholder meetings, workshop and problem framing sessions . I have arrived at following problems ensuring that the MVP is solving right issues..

Operational
Inefficiencies
and Cost Overruns

Problem

Business Goal

Efficiently perform remote
maintenance and
troubleshooting to
enhance operational
efficiency.

Design an intuitive remote maintenance experience
that enables quick issue diagnosis and resolution with minimal training, enhancing operational efficiency.

UX Goal

Problem

Data Confidentiality
Concerns, Cyber Security Threats

Business Goal

Ensure data security by
bringing permission based
access and control ensuring
confidentiality and prevent
unauthorized access.

Design intuitive security
features that guide users
in protecting
factory data while offering
real-time collaboration

UX Goal

Limited or delayed
On-site support and
Production Downtime

Problem

Business Goal

Enable technicians and factory operators to support robotics engineers by facilitating access and
necessary adjustments
for Remote Access

Design an intuitive
platform that empowers
technicians and factory
operators to easily access and modify robotics
programs remotely with
minimal technical
expertise.

UX Goal

Seemless interaction
with-less technical
expertise in field of
Robotics

Problem

Increase competitive advantage by differentiating from competitors through superior user experience and innovative technology.

Business Goal

Create a cutting-edge,user-centered experience that leverages innovative technology to enhance ease of use of technicians, setting the product apart from competitors.

UX Goal

Approach to UX Strategy & Road Map

A UX strategy and roadmap ensure transparency, alignment, and efficiency in creating user-centered experiences. They provide a shared vision, helping teams work towards common goals while prioritizing user needs. By outlining clear steps, milestones, and priorities, they foster collaboration, reduce miscommunication, and create accountability, leading to a seamless and effective product.

User Persona

Carlos John

50 / Male

Carlos has been working in factory operations for over a decade, managing industrial robots for assembly lines. His daily tasks involve monitoring machine performance, troubleshooting minor faults, and escalating critical issues to remote engineers.

Details

Day Routine

Pain Points

❌ Too many interfaces → Wastes time switching between dashboards. 

❌ Manual documentation → Reports are time-consuming and error-prone. 

❌ Communication gaps → Hard to relay issues quickly to engineers. 

❌ Lack of predictive diagnostics

Logs into multiple dashboards to monitor machine status, error reports, and pending maintenance tasks.
Performs manual inspections on robotic systems, checking for warning signals or performance issues.
Updates machine status manually in factory logs and reports minor issues.
Receives operator alerts about machine downtime, attempts to troubleshoot using outdated documentation.
Collaborates with Carlos (remote engineer) via email or phone to diagnose robotic system failures.
Fixes minor technical issues but struggles with dashboards due to fragmented UI.
Manually documents troubleshooting steps, updates logs for the next shift.

Occupation:

Factory Operator

Location:

Spain

Experience:

10+ Yrs

Education:

Diploma in Industrial
Automation

Technology Usages

. Uses factory control panels daily.
. Limited experience with advanced software or AI tools.
. Prefers mobile-friendly interfaces for quick checks.

Phillip Kraus

40 / Male

Day Routine

Occupation:

Robotics Engineer

Location:

Germany

Experience:

10+ Yrs

Education:

Masters in AI & Robotics

Phillip specializes in robotic system optimization and troubleshooting for factories worldwide. He works remotely, assisting factory operators with complex issues. Without direct physical access, he relies on logs, emails, and delayed error reports. This results in slow resolutions and costly downtime.

Details

Pain Points

❌ Slow troubleshooting → Delayed reports lead to production loss. 

❌ Lack of real-time data → Often relies on second-hand operator inputs. 

❌ Limited collaboration tools → Communicating with factory teams is fragmented. 

❌ Security constraints → Remote access to factory systems is often restricted.

Starts the day by checking emails and overnight incident reports from multiple factory sites.
Logs into separate dashboards for each factory to analyze machine performance data.
Receives urgent escalation from Emma, but lacks real-time diagnostics, requiring manual report reviews.
Uses AI-assisted analysis tools to predict machine failures and sends recommendations.
Joins a virtual troubleshooting session with Carlos, guiding him to check system logs.
Reviews machine learning data to fine-tune predictive maintenance models.
Documents troubleshooting solutions, updates reports for factory operators.
Joins on-calls for critical system failures.

Technology Usages

. Uses AI-driven robotics platforms and remote monitoring tools.
. Comfortable with data analytics and machine learning applications.
. Prefers web-based dashboards with real-time alerts.

Journey Map

How Might We (HMW)

MVP Features After Ideation and Prioritization

HMW we help operator
do first level
troubleshooting even
without expert support ?

HMW we help the
operator provide data
access to remote engineers
without the risk of sharing
field data.

HMW we help the operator support remote engineers for troubleshooting with
defined context setting ?

HMW use AI-driven
diagnostics to detect and
resolve robotic errors
before failures occur?

Carlos John
The Operator

HMW enable real-time
remote collaboration for
troubleshooting?

Phillip Kraus
Robotic Engineer

HMW to understand
previous issues, maintenance
and service history while
troubleshooting ?

HMW to keep both operator

and robotic engineer informed

about the context for ease of troubleshooting ?

HMW accelerate
troubleshooting with
AI-guided diagnostics?

Devil In the Game

I failed, but Evolved & Learned

User-Centered Design (UCD) is a well-established approach, but it does not fully align with the nature of AI products, especially Generative AI and Copilot systems. This is because AI products involve dynamic, probabilistic, and evolving behavior that differs from traditional deterministic software. Rather than failing outright, UCD needs significant adaptation to be effective in AI-driven product development.

Copilot UX is Not Just About Users—
It’s About AI-Human Collaboration

Traditional UI/UX
Patterns Lead to
Information Overload

Users Can’t Always Define Their Needs in
AI-Assisted
Environments

Need a system level approach, as model consistency is questionable

Predefined User Journeys Don’t Work in an
Adaptive AI System

Human - AI Higher Level Interaction Flow

Concepts

* Due to intellectual property protections and confidentiality agreements, some of the concepts & final designs are not shared.

Insights & Takeaways

Evolving UX Design for AI-Driven Robotics

Shift from static to adaptive, learning-based UIs as AI matures.
AI copilots must balance automation & human oversight with intuitive control handoffs.
Users must understand AI reasoning through clear, explainable feedback loops. With Expert users the system should be capable of adopting inputs or make changes to its reasoning based on validation.
Trust and validation is a quite complex topic with respect to UX . I am learning in this area in terms of market frameworks and adaptation.

Adapting UCD for AI Copilot Evolution

Context-Aware Interfaces: UI should adjust based on AI confidence, user role, and task complexity.
Proactive Guidance: Instead of passive dashboards, copilots should provide real-time, predictive recommendations.

Key AI UX Strategies

Adaptive UI Frameworks: Interfaces that evolve with AI capabilities & user expertise.
Human Override & Fail-Safes: Emergency controls for unpredictable AI failures. Safety is not compromised in industrial robots
Continuous Learning Feedback Loops: User interactions refine AI behavior over time.

Hello , I am Pepper . How can i help you ?

I am facing performance issue in station number 12 .

Ok, let me analyze the data of station number 12.

Ask Pepper ....

2024 | Web Application

Robotics Smart
Control Co-pilot

UX Case Study in Industrial Robotics & Copilot

The Robotics Smart Control Hub is an innovative,AI-powered collaborative robotics control platform designed to enhance industrial robotics operations. Leveraging an industrial copilot, this platform allows factory operators and technicians to remotely access and analyze robotics data and deploy solution in real-time, without sharing sensitive information with external vendors.

Generative AI / Copilot / Robotics / XAI

My Role

Define Strategy & Vision – Align UX, AI, and business goals, creating a clear roadmap from concept to MMP.
Team Enablement & Collaboration – Ramp up the team on key technologies, foster innovation, and drive cross-functional synergy.
Task Distribution & Execution – Prioritize and allocate tasks using Agile, ensuring efficiency and measurable outcomes.
UX & Information Architecture – Design scalable, AI-driven experiences with intuitive interaction and seamless data flow.
Market Readiness & Launch – Execute go-to-market strategy, iterate based on user feedback, and optimize post-launch performance.

Online

Ask Pepper

Carlos, I detected that Robot in Line 5 stopped operating 3 minutes ago.
Would you like to see the probable cause?

Pepper

Yes, show me details.

Would you like me to suggest a
troubleshooting workflow?

Carlos, Operator

Pepper

Yes, show me step-by-step.

Carlos, Operator

Ask Pepper

Background

TASK

Design Process

01 Discovery

Understanding Business Goal

Reduce robot downtime and increase operational efficiency for factories.

Improve collaboration between factory operators and remote robotics engineers to speed up troubleshooting.

Ensure data security in robot control and diagnostics to meet compliance standards.

Differentiate the company from competitors by offering an intelligent and user-friendly robotics control platform.

Revenue Stream and Business Value

Subscription Model: Offer a SaaS-based Smart Control Hub for companies managing large-scale robotics deployments.

Value-Added Services: AI-driven diagnostics, predictive maintenance, and training modules as premium features.

Hardware-Software Integration: Upsell to existing industrial automation customers and system integrators by bundling with robots.

Consulting & Support: Monetize enterprise-level setup, customization, and customer training.​

End User Pain Points

Complex Robot Monitoring & Troubleshooting only with Expert Support

Delayed Issue Resolution

High Operational Costs Due to Downtime

Security & Compliance Risks

Cost Analysis & Investment

As part of initial stakeholder meeting , i tried to understand what is the cost impact and what is the projection they expect by implementing solution aka. ROI (Return on Investment) .

Defining Strategy

Problem

Business Goal

UX Goal

Problem

Business Goal

UX Goal

Problem

Business Goal

UX Goal

Problem

Business Goal

UX Goal

Approach to UX Strategy & Road Map

User Persona

Carlos John

50 / Male

Details

Day Routine

Pain Points

Technology Usages

Phillip Kraus

40 / Male

Day Routine

Details

Pain Points

Technology Usages

Journey Map

Journey Map

How Might We (HMW)

MVP Features After Ideation and Prioritization

Devil In the Game

I failed, but Evolved & Learned

Copilot UX is Not Just About Users— It’s About AI-Human Collaboration

Traditional UI/UX Patterns Lead to Information Overload

Users Can’t Always Define Their Needs in AI-Assisted Environments

Need a system level approach, as model consistency is questionable

Predefined User Journeys Don’t Work in an Adaptive AI System

Human - AI Higher Level Interaction Flow

Concepts

* Due to intellectual property protections and confidentiality agreements, some of the concepts & final designs are not shared.

Insights & Takeaways

Evolving UX Design for AI-Driven Robotics

Shift from static to adaptive, learning-based UIs as AI matures.

AI copilots must balance automation & human oversight with intuitive control handoffs.

Adapting UCD for AI Copilot Evolution

Context-Aware Interfaces: UI should adjust based on AI confidence, user role, and task complexity.

Proactive Guidance: Instead of passive dashboards, copilots should provide real-time, predictive recommendations.

Key AI UX Strategies

Adaptive UI Frameworks: Interfaces that evolve with AI capabilities & user expertise.

Human Override & Fail-Safes: Emergency controls for unpredictable AI failures. Safety is not compromised in industrial robots

Continuous Learning Feedback Loops: User interactions refine AI behavior over time.

2024 | Web Application

Robotics Smart Control Co-pilot

UX Case Study in Industrial Robotics & Copilot

Generative AI / Copilot / Robotics / XAI

My Role

Consulting & Support: Monetize enterprise-level setup, customization, and customer training.

As part of initial stakeholder meeting , i tried to understand what is the cost impact and what is the projection they expect by implementing solution aka. ROI (Return on Investment)
.

Copilot UX is Not Just About Users—
It’s About AI-Human Collaboration

Traditional UI/UX
Patterns Lead to
Information Overload

Users Can’t Always Define Their Needs in
AI-Assisted
Environments

Predefined User Journeys Don’t Work in an
Adaptive AI System

Robotics Smart
Control Co-pilot