Smart Service Solution for field technicians and operations
Unified digital platform that combines AI‑driven diagnostics, scheduling and communication tools to help field technicians and operations teams deliver faster, more efficient service in the field.
Role: UX Research Lead and Service Designer
Duration: 6 Weeks
Overview
This was not just an app redesign. It was a service transformation initiative to connect farmers, field technicians, and operations managers through an AI-powered platform.
As a service designer, my role was to map the ecosystem, identify systemic breakdowns, and design a coordinated service experience that integrates digital and human touchpoints across the value chain.
My Role: Service Design Lead (research, blueprinting, orchestration design)
Scope: End-to-end service design for tractor maintenance workflows
Key Deliverables: Service blueprints, journey maps, omni-channel touchpoint design, prototype
Discovery
Through stakeholder mapping and ecosystem analysis, we discovered:
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Farmers experienced fragmented communication and unpredictable service timelines.
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Technicians worked in isolation, without diagnostic tools or clear escalation paths.
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Operations managers lacked visibility across service demand, technician workload, and parts availability.
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Support processes like scheduling and knowledge sharing were still paper-based or siloed.
Service insight: The problem wasn’t only broken apps — it was a broken service chain. Every handoff (farmer → helpline → ops manager → technician) lost information, creating inefficiency and mistrust.
Solution
We designed an AI-enabled service ecosystem, ensuring seamless flow between customer actions, frontstage touchpoints, backstage processes, and support functions.
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Customer Layer (Farmers): Omni-channel entry points — SMS, calls, or app — with real-time status updates.
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Frontstage (Technicians): A guided AI mobile tool for diagnostics, offline access, and easy updates shared directly with farmers.
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Backstage (Operations): Dynamic task allocation, predictive diagnostics, and workload balancing via a dashboard.
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Support Processes: Cloud scheduling, parts availability integration, and continuous knowledge base updates.
By mapping these flows into a before/after service blueprint, we could show leadership how AI doesn’t just improve one app — it re-wires the entire service model.
Impact
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⏱ Reduced average downtime of tractors by 30% through faster dispatch.
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🔄 Improved information continuity across channels — no repeated farmer complaints.
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📊 Managers gained visibility at system level, enabling proactive planning.
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🌾 Farmers regained trust in the brand due to transparent, reliable service.
Service Design Impact: Beyond product usability, the redesign delivered a scalable service system that works across people, processes, and platforms.
Prototype