Case study
AI-enhanced scheduling & field service management
Transforming field service operations with data-driven decision-making, automation, and machine learning.
Business impact
Our team delivered a fully automated, early-stage AI-enhanced field service management platform, helping the digital transformation of German field service management enterprises and improving efficiency of field service teams.
Feedback from our clients:
✅ Reduced manual scheduling workload by 60%, freeing up the capacity of dispatchers for high-value tasks.
✅ Increased task completion rate by 45%, reducing downtime and delays.
✅ Decreased data entry errors by 30%, improving dispatch accuracy.
✅ Improved response times by 25%, enabling faster service execution.
✅ Boosted customer satisfaction by 35%, as job assignments became more efficient and reliable.
The challenge
Field service companies were in the middle of digital transformation.
Most of the planning and administration was still tied to paper-based processes.
Manual scheduling processes, data errors, and slow response times impacted both business performance and customer satisfaction.
Basic dispatcher systems caused inefficiencies, delays, and scalability challenges for enterprise clients.
Key issues
❌ Scheduling relied heavily on manual input, making dispatch slow and prone to errors.
❌ No real-time visibility, causing inefficiencies in workforce allocation.
❌ The platform was difficult to scale, limiting enterprise adoption.
❌ Field operators struggled with a complex and outdated UI, leading to longer training times.
We wanted to transform the scheduling workflow, integrate automation, and create an ML-powered system that would improve efficiency, reduce manual workloads, and scale seamlessly for enterprise clients.
Strategic approach
Research & Discovery
Conducted stakeholder interviews with dispatchers and field operators to identify workflow pain points.
Mapped inefficiencies in manual scheduling and delayed workforce allocation.
Audited enterprise client needs, ensuring solutions met large-scale operational challenges.
UX Strategy & Design
Developed a data-driven scheduling flow, optimising technician availability, location, and priority levels.
Designed a real-time dashboard with live workforce tracking and dispatch monitoring.
Created an intelligent job allocation system, automating scheduling to reduce human intervention.
Built a modular, scalable UI framework, based on Ant.Design, allowing for seamless expansion.
Iteration & Development
Worked closely with developers to integrate automation and machine learning insights.
Ran iterative testing to refine dispatch workflows and scheduling logic.
Ensured accessibility, optimising the platform for clarity, readability, and efficiency.
My role & responsibilities
Role: Led UX strategy, system architecture, and interaction design, collaborating closely with engineering and product teams.
Focus: UX Research, Flow Design, Wireframing, ML-driven UX, and Scalable Design Systems.
Collaboration: Partnered with engineers, product managers, and enterprise clients to ensure smooth implementation.
Key contributions
Designed the ML-powered scheduling system, streamlining dispatch workflows.
Developed a scalable design system, ensuring consistency across multiple applications.
Redefined task prioritisation and job allocation logic, reducing inefficiencies.
Collaborated with engineering teams to integrate automation and real-time data.
Simplified UI and streamlined UX, reducing training time for field operators.
Results & business impact
✅ Reduced manual scheduling workload by 60%, increasing operational efficiency.
✅ Task completion rate increased by 45%, improving workforce productivity.
✅ Data entry errors decreased by 30%, ensuring more accurate dispatching.
✅ Response times improved by 25%, leading to better service execution.
✅ Customer satisfaction scores increased by 35%, as job allocation became more reliable.
These results validated the need for ML-powered scheduling in field service operations and positioned the company for further enterprise expansion.
Key learnings & reflections
Scalable IA and UX design is critical – Creating a modular design system allowed for seamless expansion as client needs grew.
Automation and ML-driven scheduling significantly reduce inefficiencies – Minimising manual tasks allowed teams to focus on higher-value work.
Cross-functional collaboration is key – Partnering with engineers and enterprise stakeholders ensured a technically feasible and scalable solution.