UST DispatchIQ - AI Field Dispatch & Truck Roll Optimization

DispatchIQ is a professional services engagement from UST that deploys predictive analytics to determine whether a reported issue genuinely requires a site visit or can be resolved remotely - eliminating a significant share of unnecessary truck rolls. When a visit is required, it automatically selects the best-matched technician based on skill set, spare-parts availability, and proximity.

The result is a leaner, smarter field operation: fewer wasted trips, optimized routing across multiple sites, and higher first-time-fix rates - all while keeping field crews focused on the visits that genuinely need them.

UST delivers DispatchIQ through a phased professional services engagement:

Phase 1 - Discovery and Scoping (2-4 weeks)

• Assessment of current dispatch workflows, ticketing systems, and field operations data
• Identification of integration points and data requirements
• Definition of success metrics and baseline measurements
• Deliverable: Scoping document with architecture blueprint and implementation roadmap

Phase 2 - Pilot Deployment (4-8 weeks)

• Configuration of predictive truck-roll scoring model using historical ticket data
• Integration with existing ticketing and workforce management systems (e.g., ServiceNow, Salesforce Field Service)
• Limited rollout to a subset of field operations for validation
• Deliverable: Configured predictive model, integration layer, and pilot performance report

Phase 3 - Production Rollout and Optimization (4-6 weeks)

• Full-scale deployment across all dispatch operations
• Model tuning based on pilot learnings
• Training for operations teams on dashboards and reporting
• Deliverable: Production-ready system, runbook, operational dashboards, and knowledge transfer

DispatchIQ leverages AWS cloud services to deliver scalable, secure analytics. The solution can integrate with Amazon SageMaker for ML model training and inference, and Amazon Connect for remote resolution workflows - enabling a seamless handoff between AI-driven triage and technician dispatch.

• Predictive truck-roll necessity scoring - ML-driven analysis of ticket data to classify issues as remote-resolvable or requiring a site visit
• Automated technician-skill and parts matching - Assigns the right technician with the right parts based on job requirements
• Location-aware route and schedule optimization - Minimizes travel time across multi-site dispatch schedules
• Real-time field-crew status tracking - Visibility into technician availability and job progress
• First-time-fix analytics and reporting - Dashboards measuring resolution rates and operational efficiency
• System integrations - Compatible with leading platforms including ServiceNow, Salesforce Field Service, and existing workforce management systems

• Reduces unnecessary truck rolls by approximately 34%
• Improves first-time-fix rate to over 88%
• Shortens overall field-resolution cycle time
• Optimizes technician utilization and travel time
• Improves customer satisfaction with faster, accurate field fixes

UST applies enterprise-grade security practices throughout the engagement. Buyer data ingested for model training is encrypted in transit and at rest. Access to systems and data is governed by role-based controls, and all credentials and integration tokens are managed securely. At engagement completion, UST follows agreed-upon data retention and deletion policies to ensure buyer data is handled in accordance with organizational and regulatory requirements.

• Telecom network fault dispatch - Scores the likelihood that remote resolution is possible before dispatching a technician, reducing unnecessary site visits
• Utilities preventive maintenance scheduling - Optimizes preventive maintenance visits across geographically distributed infrastructure, pre-staging spare parts at regional depots
• Cable and broadband multi-site route optimization - Sequences daily dispatch routes for field crews, minimizing drive time and maximizing jobs completed per shift
• Spare-parts pre-staging for scheduled visits - Predicts required parts based on ticket classification and ensures technicians arrive equipped for first-time resolution

To engage with DispatchIQ, buyers should have:

• An existing ticketing or work-order management system (e.g., ServiceNow, Salesforce Field Service, BMC Helix) with historical dispatch data
• Minimum 6 months of historical ticket data for model training
• Designated technical and operations stakeholders for discovery workshops

Request a discovery call through AWS Marketplace to discuss your dispatch operations and receive a tailored scoping proposal from UST.