AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
Status & Year
Launched & Awarded (2024-25)
Status
Launched & Awarded (2024-25)
Status & Year
Launched & Awarded (2024-25)
Company
Walmart
Company
Walmart
Disclaimer
This project is based on real-world experience in a walmart's enterprise environment. Sensitive data, system identifiers have been modified to maintain confidentiality.
Problem Statement & Challenges
Operational teams at Walmart navigated 200+ fragmented modules to fulfill orders, relying heavily on manual decision-making.
This complexity slowed execution, increased errors, and created a strong dependency on training.
Annual training costs ranged from $774–$1,047 per associate, contributing to $5M+ in operational friction driven by cognitive overload, navigation inefficiencies, and communication delays.
My Contribution
I led the end-to-end design, research, and launch of an AI-powered fulfillment assistant that automated repetitive tasks and improved decision quality through transparent, explainable reasoning.
Through user sessions and workflow analysis, I defined the conversational strategy, interaction model, and safety guardrails partnering with Product and Engineering to prototype and operationalize autonomous fulfillment at scale.
Design
1 Member
Design
1 Member
Product
6 Members
Product
6 Members
Engg+Data+Ops
8 Members
Engg+Data+Ops
8 Members
Timeline
2 Sprints
Timeline
2 Sprints
Persona, Need & Wants
Across 16+ sessions, a clear pattern emerged: Teams weren’t lacking effort they were overwhelmed by system fragmentation. They needed automation for routine tasks, faster decision cycles, lower training dependency, and human-like clarity in complex scenarios.
Fulfillment Associates
Navigate 200+ operational modules daily.
Need intelligent, consolidated tools that reduce task overhead and onboarding dependency.
Customer Service Teams
Handle escalations under time pressure.
Require instant, accurate information to resolve issues without workflow disruption.
Operations Managers
Oversee end-to-end efficiency at scale.
Depend on real-time insights to optimize cost, throughput, and service quality.
Our North Star
Reduce operational expenditure while accelerating fulfillment through real-time, intelligent support that improves productivity, accuracy, and customer experience.
Research
16 (User interviewed)
Research
16 (User interviewed)
Market
EU, US (P1 Market)
Market
EU, US (P1 Market)
Activities
5+ (Sample stores)
Activities
5+ (Sample stores)
Timeline
5 days (Data Mapping & Analysis)
Timeline
5 days (Data Mapping & Analysis)
Through natural turn-based interactions and built-in safety guardrails, it enabled faster, more accurate decisions at scale.
I designed a conversational AI system that unified order management across 200+ modules, replacing manual coordination with contextual, real-time guidance.
Ideate, Test & Measure
Designing AI systems requires clarity, transparency, and ethical guardrails especially when decisions affect cost, time, and customer trust. Explainability and human control are central to adoption.
Designing AI systems requires clarity, transparency, and ethical guardrails especially when decisions affect cost, time, and customer trust. Explainability and human control are central to adoption.
Future opportunities include role-based personalization, predictive recommendations, and applying the system to additional fulfillment domains.
From prototype to production operationalizing intelligent decision support at scale.
From prototype to production operationalizing intelligent decision support at scale.
The assistant was rolled out through phased deployment and live workflow validation across teams.
Workflows accelerated by 60%, 300+ daily support sessions were reduced, and the system unlocked $5M+ in operational savings.
What began as automation became a structural shift in how fulfillment decisions were made replacing fragmented coordination with unified, AI-assisted clarity.
Impact
$5 MN (Business savings)
Impact
$5 MN (Business savings)
Impact
- 300 (Sessions per day)
Impact
- 300 (Sessions per day)
Impact
- 24,000 (Clicks per sessions)
Impact
- 24,000 (Clicks per sessions)
Impact
<3 Mins (Task time)
Impact
<3 Mins (Task time)
AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
AI Powered Order Fulfillment
Scaled AI fulfillment support across operations, accelerating workflows by 60% and unlocking $5M+ in savings while eliminating 300+ daily support sessions.
Status & Year
Launched & Awarded (2024-25)
Status
Launched & Awarded (2024-25)
Status & Year
Launched & Awarded (2024-25)
Company
Walmart
Company
Walmart
Disclaimer
This project is based on real-world experience in a walmart's enterprise environment. Sensitive data, system identifiers have been modified to maintain confidentiality.
Problem Statement & Challenges
Operational teams at Walmart navigated 200+ fragmented modules to fulfill orders, relying heavily on manual decision-making.
This complexity slowed execution, increased errors, and created a strong dependency on training.
Annual training costs ranged from $774–$1,047 per associate, contributing to $5M+ in operational friction driven by cognitive overload, navigation inefficiencies, and communication delays.
My Contribution
I led the end-to-end design, research, and launch of an AI-powered fulfillment assistant that automated repetitive tasks and improved decision quality through transparent, explainable reasoning.
Through user sessions and workflow analysis, I defined the conversational strategy, interaction model, and safety guardrails partnering with Product and Engineering to prototype and operationalize autonomous fulfillment at scale.
Design
1 Member
Design
1 Member
Product
6 Members
Product
6 Members
Engg+Data+Ops
8 Members
Engg+Data+Ops
8 Members
Timeline
2 Sprints
Timeline
2 Sprints
Persona, Need & Wants
Across 16+ sessions, a clear pattern emerged: Teams weren’t lacking effort they were overwhelmed by system fragmentation. They needed automation for routine tasks, faster decision cycles, lower training dependency, and human-like clarity in complex scenarios.
Fulfillment Associates
Navigate 200+ operational modules daily.
Need intelligent, consolidated tools that reduce task overhead and onboarding dependency.
Customer Service Teams
Handle escalations under time pressure.
Require instant, accurate information to resolve issues without workflow disruption.
Operations Managers
Oversee end-to-end efficiency at scale.
Depend on real-time insights to optimize cost, throughput, and service quality.
Our North Star
Reduce operational expenditure while accelerating fulfillment through real-time, intelligent support that improves productivity, accuracy, and customer experience.
Research
16 (User interviewed)
Research
16 (User interviewed)
Market
EU, US (P1 Market)
Market
EU, US (P1 Market)
Activities
5+ (Sample stores)
Activities
5+ (Sample stores)
Timeline
5 days (Data Mapping & Analysis)
Timeline
5 days (Data Mapping & Analysis)
Through natural turn-based interactions and built-in safety guardrails, it enabled faster, more accurate decisions at scale.
I designed a conversational AI system that unified order management across 200+ modules, replacing manual coordination with contextual, real-time guidance.
Ideate, Test & Measure
Designing AI systems requires clarity, transparency, and ethical guardrails especially when decisions affect cost, time, and customer trust. Explainability and human control are central to adoption.
Designing AI systems requires clarity, transparency, and ethical guardrails especially when decisions affect cost, time, and customer trust. Explainability and human control are central to adoption.
Future opportunities include role-based personalization, predictive recommendations, and applying the system to additional fulfillment domains.
From prototype to production operationalizing intelligent decision support at scale.
From prototype to production operationalizing intelligent decision support at scale.
The assistant was rolled out through phased deployment and live workflow validation across teams.
Workflows accelerated by 60%, 300+ daily support sessions were reduced, and the system unlocked $5M+ in operational savings.
What began as automation became a structural shift in how fulfillment decisions were made replacing fragmented coordination with unified, AI-assisted clarity.
Impact
$5 MN (Business savings)
Impact
$5 MN (Business savings)
Impact
- 300 (Sessions per day)
Impact
- 300 (Sessions per day)
Impact
- 24,000 (Clicks per sessions)
Impact
- 24,000 (Clicks per sessions)
Impact
<3 Mins (Task time)
Impact