Case study
AI-enhanced scheduling & field service management
Transforming field service operations with automation, machine learning-driven decision-making, and scalable UX.
Business impact
Starting from an initial demo idea 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.
Reduced manual scheduling workload by 60%, freeing up dispatchers for higher-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.
Secured a 40% increase in contract value, strengthening adoption by large-scale energy companies.
The challenge
A field service management company relied on a basic dispatcher system that caused inefficiencies, delays, and scalability challenges for enterprise clients. Manual scheduling processes, data errors, and slow response times impacted both business performance and customer satisfaction.
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❌ Field operators struggled with a complex and outdated UI, leading to longer training times.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 needed 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.
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.
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.
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.
✅ Secured a 40% increase in contract value, making the platform more attractive to enterprise clients.
These results validated the need for ML-driven scheduling in field service operations and positioned the company for further enterprise expansion.
Key learnings & reflections
Scalable 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 was key – Partnering with engineers and enterprise stakeholders ensured a technically feasible and scalable solution.
This case study highlights my expertise in UX strategy, ML-powered automation, and enterprise UX design.