Predictive AI
Designing a GenAI Platform to Drive Proactive Risk Management in Complex Logistics Operations
Predictive AI
Designing a GenAI Platform to Drive Proactive Risk Management in Complex Logistics Operations
PRODUCT VISION
UX/UI
SAAS
ML & GENAI
*Certain details in this case study have been modified or omitted to protect confidentiality. All sensitive and proprietary information has been fully anonymized.
[01]
INTRODUCTION
[01]
INTRODUCTION
Overview
Overview
CLIENT
FlowTrack - B2B SaaS platform (ERP) used by mid-size logistics companies to manage end-to-end operations, from compliance to procurement.
CLIENT
FlowTrack - B2B SaaS platform (ERP) used by mid-size logistics companies to manage end-to-end operations, from compliance to procurement.
PLATFORM
Orion - AI-driven intelligence layer delivering risk prediction, decision support and operational automation on top of the FlowTrack ERP.
PLATFORM
Orion - AI-driven intelligence layer delivering risk prediction, decision support and operational automation on top of the FlowTrack ERP.
MY ROLE
Lead Product Designer on behalf of Kido
MY ROLE
Lead Product Designer on behalf of Kido
TIMELINE
Q3 2024 - Q4 2025
TIMELINE
Q3 2024 - Q4 2025
The Problem
The Problem
High-Stakes Logistics
High-Stakes Logistics
Logistics operations operate in constant risk conditions. Minor disruptions - weather changes, supply shortages, staffing gaps - quickly cascade into delays, financial loss, and compliance exposure. Despite years of accumulated operational data, FlowTrack lacked real-time visibility and predictive intelligence, limiting proactive decision-making.
Logistics operations operate in constant risk conditions. Minor disruptions - weather changes, supply shortages, staffing gaps - quickly cascade into delays, financial loss, and compliance exposure. Despite years of accumulated operational data, FlowTrack lacked real-time visibility and predictive intelligence, limiting proactive decision-making.
0%
Real-time predictive risk capabilities
0%
Real-time predictive risk capabilities
12
Years of operational data unused for foresight
12
Years of operational data unused for foresight
~48–72h
Average delay in detecting operational issues
~48–72h
Average delay in detecting operational issues
The Mission
The Mission
From Reactive Operations to Proactive Risk Management
From Reactive Operations to Proactive Risk Management
Extend FlowTrack with Orion, a generative AI platform that transforms logistics operations into a proactive, intelligence-driven system by delivering:
Extend FlowTrack with Orion, a generative AI platform that transforms logistics operations into a proactive, intelligence-driven system by delivering:
1
Predictive risk detection
Early identification of emerging risks with actionable alerts.
2
AI operations orchestration
Enable teams to act quickly via AI insights, automated workflows, and data-driven decision support.
1
Predictive risk detection
Early identification of emerging risks with actionable alerts.
2
AI operations orchestration
Enable teams to act quickly via AI insights, automated workflows, and data-driven decision support.
[02]
RESEARCH
[02]
RESEARCH
Continuous User Research
Continuous User Research
From the outset, Orion was guided by continuous user research grounded in day-to-day needs. To gather actionable feedback, we held weekly sessions with customer managers, conducted client interviews, and developed bi-weekly prototypes presented by the CEO to users and prospects, consistently generating insights that shaped the product. Key insights we gathered:
From the outset, Orion was guided by continuous user research grounded in day-to-day needs. To gather actionable feedback, we held weekly sessions with customer managers, conducted client interviews, and developed bi-weekly prototypes presented by the CEO to users and prospects, consistently generating insights that shaped the product. Key insights we gathered:
1
Prioritize real-time actions
Many work in high-stress field conditions and prioritize quick decisions over detailed reports.
2
Bridge data gaps across roles
In this hierarchical industry, critical data is limited to higher ranks, leaving frontline roles less visible.
3
Support protocol-driven decision making
Workflows are governed by strict protocols, but users often lack the guidance to follow them all.
4
Ensure workflow integration
AI layer should fit within ERP workflows to maintain established operational patterns.
1
Prioritize real-time actions
Many work in high-stress field conditions and prioritize quick decisions over detailed reports.
2
Bridge data gaps across roles
In this hierarchical industry, critical data is limited to higher ranks, leaving frontline roles less visible.
3
Support protocol-driven decision making
Workflows are governed by strict protocols, but users often lack the guidance to follow them all.
4
Ensure workflow integration
AI layer should fit within ERP workflows to maintain established operational patterns.
Journey Mapping
Journey Mapping
Based on research insights, we mapped two primary journeys:
Based on research insights, we mapped two primary journeys:
1
Push flow (system-initiated)
AI proactively detects risks and surfaces insights at the right moment.
2
Pull flow (user-initiated)
Users query the AI, explore scenarios, trigger actions, and configure automations.
1
Push flow (system-initiated)
AI proactively detects risks and surfaces insights at the right moment.
2
Pull flow (user-initiated)
Users query the AI, explore scenarios, trigger actions, and configure automations.
In parallel, AI feedback-loop touchpoints were embedded directly into these journeys - allowing the system to learn from confirmations, overrides and outcomes from day one.
In parallel, AI feedback-loop touchpoints were embedded directly into these journeys - allowing the system to learn from confirmations, overrides and outcomes from day one.
AI Trend Monitoring
AI Trend Monitoring
Staying Ahead of the Rapidly Changing AI Landscape
Staying Ahead of the Rapidly Changing AI Landscape
Given the speed of AI evolution, Orion was designed as a living system, not a static product. We continuously tracked emerging AI interaction patterns and capabilities to meet evolving user expectations, including: chat-based automation, voice interaction and reasoning, etc. Orion aligned with these patterns as they emerged, ensuring long-term relevance and future-proofing the platform.
Given the speed of AI evolution, Orion was designed as a living system, not a static product. We continuously tracked emerging AI interaction patterns and capabilities to meet evolving user expectations, including: chat-based automation, voice interaction and reasoning, etc. Orion aligned with these patterns as they emerged, ensuring long-term relevance and future-proofing the platform.
Chat-based automation: we aligned Orion with this capability as soon as it launched in the AI Agents field in Jan' 25.
Voice mode feature: we immediately adopted voice-mode capabilities upon their Sep' 24 release.
[03]
DESIGN PROCESS
[03]
DESIGN PROCESS
Shaping the AI Experience
Shaping the AI Experience
Defining Platform Structure and Behavior
Defining Platform Structure and Behavior
Early in the design process, we focused on Orion’s behavior and its integration with FlowTrack’s ERP. As a separate product being incorporated into an existing system, seamless integration was essential to ensure the platforms worked in harmony and complemented each other, allowing users to navigate both without friction.
Early in the design process, we focused on Orion’s behavior and its integration with FlowTrack’s ERP. As a separate product being incorporated into an existing system, seamless integration was essential to ensure the platforms worked in harmony and complemented each other, allowing users to navigate both without friction.
After exploring multiple approaches, we implemented an ERP overlay, letting users switch smoothly between Orion and core tasks while maintaining context and workflow continuity. Leveraging this familiar industry pattern ensured the experience felt intuitive and consistent.
After exploring multiple approaches, we implemented an ERP overlay, letting users switch smoothly between Orion and core tasks while maintaining context and workflow continuity. Leveraging this familiar industry pattern ensured the experience felt intuitive and consistent.
Laying the Foundations
Laying the Foundations
Defining Orion’s Identity
Defining Orion’s Identity
As a new product in the FlowTrack ecosystem, Orion required a distinct AI-driven identity. We positioned it as the first dark-mode product, creating a modern, intelligent presence.
As a new product in the FlowTrack ecosystem, Orion required a distinct AI-driven identity. We positioned it as the first dark-mode product, creating a modern, intelligent presence.
We extended the design system with a dedicated Orion color mode, reused the core component library for speed and consistency, and followed a design-system-first approach from day one to enable fast, aligned iteration.
We extended the design system with a dedicated Orion color mode, reused the core component library for speed and consistency, and followed a design-system-first approach from day one to enable fast, aligned iteration.
[04]
FINAL DESIGN
[04]
FINAL DESIGN
Actionable Risk Units
Actionable Risk Units
Risks in Orion are structured as operational decision units, not mere alerts. Each follows a three-layer flow that turns detection into action. Users see clear next steps first, then can drill down to understand the reasoning, balancing speed with insight:
Risks in Orion are structured as operational decision units, not mere alerts. Each follows a three-layer flow that turns detection into action. Users see clear next steps first, then can drill down to understand the reasoning, balancing speed with insight:
1
Recommended actions
Context-aware steps, prioritized by impact and effort.
2
Risk overview & supporting data
Framing the situation with real-time data, trends, and anomalies.
3
Risk analysis
AI-generated insights explaining contributing factors.
1
Recommended actions
Context-aware steps, prioritized by impact and effort.
2
Risk overview & supporting data
Framing the situation with real-time data, trends, and anomalies.
3
Risk analysis
AI-generated insights explaining contributing factors.
Operational Command Chat Layer
Operational Command Chat Layer
Streamlining Operations in the Field
Streamlining Operations in the Field
Orion’s chat layer serves as a command surface, combining various structural components such as text, visuals, timelines, voice input, and more to deliver information quickly and clearly. It streamlines decision-making and accelerates responses, empowering teams at all levels to access internal and external data without relying on senior staff.
Orion’s chat layer serves as a command surface, combining various structural components such as text, visuals, timelines, voice input, and more to deliver information quickly and clearly. It streamlines decision-making and accelerates responses, empowering teams at all levels to access internal and external data without relying on senior staff.
Built for fast-paced field environments, it supports operational workflows such as task automation, troubleshooting malfunctions or delays, and executing rapid actions.
Built for fast-paced field environments, it supports operational workflows such as task automation, troubleshooting malfunctions or delays, and executing rapid actions.
AI Workflow Touchpoints
AI Workflow Touchpoints
Orion embeds trust and continuous machine learning as core product features, not just small, specific additions. By integrating these into daily operations, the AI acts as a reliable, evolving operational partner rather than merely a tool.
Orion embeds trust and continuous machine learning as core product features, not just small, specific additions. By integrating these into daily operations, the AI acts as a reliable, evolving operational partner rather than merely a tool.
Building User Trust
Building User Trust
Users see supporting data, explanations, and predictable reasoning behind every AI recommendation, allowing validation and confident action.
Users see supporting data, explanations, and predictable reasoning behind every AI recommendation, allowing validation and confident action.
Continuous Machine Learning
Continuous Machine Learning
User feedback and overrides are incorporated into every critical interaction - risk validation, confirmations, and execution - allowing the system to learn from real operational decisions without disrupting workflows, while adapting content, tone, and behavior accordingly.
User feedback and overrides are incorporated into every critical interaction - risk validation, confirmations, and execution - allowing the system to learn from real operational decisions without disrupting workflows, while adapting content, tone, and behavior accordingly.
[05]
OUTCOME & IMPACT
[05]
OUTCOME & IMPACT
What We Achieved
What We Achieved
Orion, integrated atop FlowTrack ERP, became an AI-driven operational partner, enabling faster, more confident decisions and delivering measurable improvements across workflows:
~0h
Reduced risk detection time from 2–3 days to near real-time.
~0h
Reduced risk detection time from 2–3 days to near real-time.
X2
Decision speed, with reduced error rates.
X2
Decision speed, with reduced error rates.
90%
Untapped operational data now leveraged.
90%
Untapped operational data now leveraged.
100%
Clear ownership through role-based actions and real-time status.
100%
Clear ownership through role-based actions and real-time status.
Reflection
Reflection
Calibrating Trust
Calibrating Trust
With Orion integrated into decision-making, after significant effort to make it trusted and reliable, the next challenge is calibrating user trust - helping people understand when to rely on AI outputs, how they were generated, and their limitations. This NN/g article on explainable AI underscores the importance of responsible design as the platform scales.
Responsible AI is now as crucial as intelligence itself, ensuring Orion remains a trusted partner rather than an unreliable or harmful.
[06]
MORE PROJECTS
[06]
MORE PROJECTS
Designing a GenAI Platform to Drive Proactive Risk Management in Complex Logistics Operations
PRODUCT VISION
UX/UI
SAAS
ML & GENAI
Designing a GenAI Platform to Drive Proactive Risk Management in Complex Logistics Operations
PRODUCT VISION
UX/UI
SAAS
ML & GENAI
*Certain details in this case study have been modified or omitted to protect confidentiality. All sensitive and proprietary information has been fully anonymized.
[01]
INTRODUCTION
[01]
INTRODUCTION
Overview
Overview
CLIENT
FlowTrack - B2B SaaS platform (ERP) used by mid-size logistics companies to manage end-to-end operations, from compliance to procurement.
CLIENT
FlowTrack - B2B SaaS platform (ERP) used by mid-size logistics companies to manage end-to-end operations, from compliance to procurement.
PLATFORM
Orion - AI-driven intelligence layer delivering risk prediction, decision support and operational automation on top of the FlowTrack ERP.
PLATFORM
Orion - AI-driven intelligence layer delivering risk prediction, decision support and operational automation on top of the FlowTrack ERP.
MY ROLE
Lead Product Designer on behalf of Kido
MY ROLE
Lead Product Designer on behalf of Kido
TIMELINE
Q3 2024 - Q4 2025
TIMELINE
Q3 2024 - Q4 2025
The Problem
The Problem
High-Stakes Logistics
High-Stakes Logistics
Logistics operations operate in constant risk conditions. Minor disruptions - weather changes, supply shortages, staffing gaps - quickly cascade into delays, financial loss, and compliance exposure. Despite years of accumulated operational data, FlowTrack lacked real-time visibility and predictive intelligence, limiting proactive decision-making.
Logistics operations operate in constant risk conditions. Minor disruptions - weather changes, supply shortages, staffing gaps - quickly cascade into delays, financial loss, and compliance exposure. Despite years of accumulated operational data, FlowTrack lacked real-time visibility and predictive intelligence, limiting proactive decision-making.
0%
Real-time predictive risk capabilities
0%
Real-time predictive risk capabilities
12
Years of operational data unused for foresight
12
Years of operational data unused for foresight
~48–72h
Average delay in detecting operational issues
~48–72h
Average delay in detecting operational issues
The Mission
The Mission
From Reactive Operations to Proactive Risk Management
From Reactive Operations to Proactive Risk Management
Extend FlowTrack with Orion, a generative AI platform that transforms logistics operations into a proactive, intelligence-driven system by delivering:
Extend FlowTrack with Orion, a generative AI platform that transforms logistics operations into a proactive, intelligence-driven system by delivering:
1
Predictive risk detection
Early identification of emerging risks with actionable alerts.
2
AI operations orchestration
Enable teams to act quickly via AI insights, automated workflows, and data-driven decision support.
1
Predictive risk detection
Early identification of emerging risks with actionable alerts.
2
AI operations orchestration
Enable teams to act quickly via AI insights, automated workflows, and data-driven decision support.
[02]
RESEARCH
[02]
RESEARCH
Continuous User Research
Continuous User Research
From the outset, Orion was guided by continuous user research grounded in day-to-day needs. To gather actionable feedback, we held weekly sessions with customer managers, conducted client interviews, and developed bi-weekly prototypes presented by the CEO to users and prospects, consistently generating insights that shaped the product. Key insights we gathered:
From the outset, Orion was guided by continuous user research grounded in day-to-day needs. To gather actionable feedback, we held weekly sessions with customer managers, conducted client interviews, and developed bi-weekly prototypes presented by the CEO to users and prospects, consistently generating insights that shaped the product. Key insights we gathered:
1
Prioritize real-time actions
Many work in high-stress field conditions and prioritize quick decisions over detailed reports.
2
Bridge data gaps across roles
In this hierarchical industry, critical data is limited to higher ranks, leaving frontline roles less visible.
3
Support protocol-driven decision making
Workflows are governed by strict protocols, but users often lack the guidance to follow them all.
4
Ensure workflow integration
AI layer should fit within ERP workflows to maintain established operational patterns.
1
Prioritize real-time actions
Many work in high-stress field conditions and prioritize quick decisions over detailed reports.
2
Bridge data gaps across roles
In this hierarchical industry, critical data is limited to higher ranks, leaving frontline roles less visible.
3
Support protocol-driven decision making
Workflows are governed by strict protocols, but users often lack the guidance to follow them all.
4
Ensure workflow integration
AI layer should fit within ERP workflows to maintain established operational patterns.
Journey Mapping
Journey Mapping
Based on research insights, we mapped two primary journeys:
Based on research insights, we mapped two primary journeys:
1
Push flow (system-initiated)
AI proactively detects risks and surfaces insights at the right moment.
2
Pull flow (user-initiated)
Users query the AI, explore scenarios, trigger actions, and configure automations.
1
Push flow (system-initiated)
AI proactively detects risks and surfaces insights at the right moment.
2
Pull flow (user-initiated)
Users query the AI, explore scenarios, trigger actions, and configure automations.
In parallel, AI feedback-loop touchpoints were embedded directly into these journeys - allowing the system to learn from confirmations, overrides and outcomes from day one.
In parallel, AI feedback-loop touchpoints were embedded directly into these journeys - allowing the system to learn from confirmations, overrides and outcomes from day one.
Double click to explore
AI Trend Monitoring
AI Trend Monitoring
Staying Ahead of the Rapidly Changing AI Landscape
Staying Ahead of the Rapidly Changing AI Landscape
Given the speed of AI evolution, Orion was designed as a living system, not a static product. We continuously tracked emerging AI interaction patterns and capabilities to meet evolving user expectations, including: chat-based automation, voice interaction and reasoning, etc. Orion aligned with these patterns as they emerged, ensuring long-term relevance and future-proofing the platform.
Given the speed of AI evolution, Orion was designed as a living system, not a static product. We continuously tracked emerging AI interaction patterns and capabilities to meet evolving user expectations, including: chat-based automation, voice interaction and reasoning, etc. Orion aligned with these patterns as they emerged, ensuring long-term relevance and future-proofing the platform.
Chat-based automation: we aligned Orion with this capability as soon as it launched in the AI Agents field in Jan' 25.
Chat-based automation: we aligned Orion with this capability as soon as it launched in the AI Agents field in Jan' 25.
Voice mode feature: we immediately adopted voice-mode capabilities upon their Sep' 24 release.
Voice mode feature: we immediately adopted voice-mode capabilities upon their Sep' 24 release.
[03]
DESIGN PROCESS
[03]
DESIGN PROCESS
Shaping the AI Experience
Shaping the AI Experience
Defining Platform Structure and Behavior
Defining Platform Structure and Behavior
Early in the design process, we focused on Orion’s behavior and its integration with FlowTrack’s ERP. As a separate product being incorporated into an existing system, seamless integration was essential to ensure the platforms worked in harmony and complemented each other, allowing users to navigate both without friction.
Early in the design process, we focused on Orion’s behavior and its integration with FlowTrack’s ERP. As a separate product being incorporated into an existing system, seamless integration was essential to ensure the platforms worked in harmony and complemented each other, allowing users to navigate both without friction.
After exploring multiple approaches, we implemented an ERP overlay, letting users switch smoothly between Orion and core tasks while maintaining context and workflow continuity. Leveraging this familiar industry pattern ensured the experience felt intuitive and consistent.
After exploring multiple approaches, we implemented an ERP overlay, letting users switch smoothly between Orion and core tasks while maintaining context and workflow continuity. Leveraging this familiar industry pattern ensured the experience felt intuitive and consistent.
Laying the Foundations
Laying the Foundations
Defining Orion’s Identity
Defining Orion’s Identity
As a new product in the FlowTrack ecosystem, Orion required a distinct AI-driven identity. We positioned it as the first dark-mode product, creating a modern, intelligent presence.
As a new product in the FlowTrack ecosystem, Orion required a distinct AI-driven identity. We positioned it as the first dark-mode product, creating a modern, intelligent presence.
We extended the design system with a dedicated Orion color mode, reused the core component library for speed and consistency, and followed a design-system-first approach from day one to enable fast, aligned iteration.
We extended the design system with a dedicated Orion color mode, reused the core component library for speed and consistency, and followed a design-system-first approach from day one to enable fast, aligned iteration.
[04]
FINAL DESIGN
[04]
FINAL DESIGN
Actionable Risk Units
Actionable Risk Units
Orion proactively detects risks and surfaces insights at the right moment. Risks are structured as operational decision units, not mere alerts. Each follows a three-layer flow that turns detection into action. Users see clear next steps first, then can drill down to understand the reasoning, balancing speed with insight:
Orion proactively detects risks and surfaces insights at the right moment. Risks are structured as operational decision units, not mere alerts. Each follows a three-layer flow that turns detection into action. Users see clear next steps first, then can drill down to understand the reasoning, balancing speed with insight:
1
Recommended actions
Context-aware steps, prioritized by impact and effort.
2
Risk overview & supporting data
Framing the situation with real-time data, trends, and anomalies.
3
Risk analysis
AI-generated insights explaining contributing factors.
1
Recommended actions
Context-aware steps, prioritized by impact and effort.
2
Risk overview & supporting data
Framing the situation with real-time data, trends, and anomalies.
3
Risk analysis
AI-generated insights explaining contributing factors.
Operational Command Chat Layer
Operational Command Chat Layer
Streamlining Operations in the Field
Streamlining Operations in the Field
Orion’s chat layer serves as a command surface, combining various structural components such as text, visuals, timelines, voice input, and more to deliver information quickly and clearly. It streamlines decision-making and accelerates responses, empowering teams at all levels to access internal and external data without relying on senior staff.
Orion’s chat layer serves as a command surface, combining various structural components such as text, visuals, timelines, voice input, and more to deliver information quickly and clearly. It streamlines decision-making and accelerates responses, empowering teams at all levels to access internal and external data without relying on senior staff.
Built for fast-paced field environments, it supports operational workflows such as task automation, troubleshooting malfunctions or delays, and executing rapid actions.
Built for fast-paced field environments, it supports operational workflows such as task automation, troubleshooting malfunctions or delays, and executing rapid actions.
AI Workflow Touchpoints
AI Workflow Touchpoints
Orion embeds trust and continuous machine learning as core product features, not just small, specific additions. By integrating these into daily operations, the AI acts as a reliable, evolving operational partner rather than merely a tool.
Orion embeds trust and continuous machine learning as core product features, not just small, specific additions. By integrating these into daily operations, the AI acts as a reliable, evolving operational partner rather than merely a tool.
Building User Trust
Building User Trust
Users see supporting data, explanations, and predictable reasoning behind every AI recommendation, allowing validation and confident action.
Users see supporting data, explanations, and predictable reasoning behind every AI recommendation, allowing validation and confident action.
Continuous Machine Learning
Continuous Machine Learning
User feedback and overrides are incorporated into every critical interaction - risk validation, confirmations, and execution - allowing the system to learn from real operational decisions without disrupting workflows, while adapting content, tone, and behavior accordingly.
User feedback and overrides are incorporated into every critical interaction - risk validation, confirmations, and execution - allowing the system to learn from real operational decisions without disrupting workflows, while adapting content, tone, and behavior accordingly.
[05]
OUTCOME & IMPACT
[05]
OUTCOME & IMPACT
What We Achieved
What We Achieved
Orion, integrated atop FlowTrack ERP, became an AI-driven operational partner, enabling faster, more confident decisions and delivering measurable improvements across workflows:
~0h
Reduced risk detection time from 2–3 days to near real-time.
~0h
Reduced risk detection time from 2–3 days to near real-time.
X2
Decision speed, with reduced error rates.
X2
Decision speed, with reduced error rates.
90%
Untapped operational data now leveraged.
90%
Untapped operational data now leveraged.
100%
Clear ownership through role-based actions and real-time status.
100%
Clear ownership through role-based actions and real-time status.
Reflection
Reflection
Calibrating Trust
Calibrating Trust
With Orion integrated into decision-making, after significant effort to make it trusted and reliable, the next challenge is calibrating user trust - helping people understand when to rely on AI outputs, how they were generated, and their limitations. This NN/g article on explainable AI underscores the importance of responsible design as the platform scales.
Responsible AI is now as crucial as intelligence itself, ensuring Orion remains a trusted partner rather than an unreliable or harmful.
[06]
MORE PROJECTS
[06]
MORE PROJECTS