# Field service dispatch and scheduling

Source: https://altivusops.ai/use-cases/field-service-dispatch-and-scheduling/
Last modified: 2026-10-13

> Analyzes technician availability and job priority to recommend task assignment, rescheduling, and route changes. A dispatcher accepts or rejects every recommendation before it takes effect.

An AI workflow Blueprint — a documented use case for Operations in Construction & Engineering. Complexity: Standard. Deployment: Platform-native.

## The problem

Field service dispatch runs on coordinators. They assign technicians to jobs, handle no-shows and reschedules, optimize routes and rebalance the day as calls come in. In busy periods this becomes a bottleneck, and one missing technician cascades across the whole schedule.

- Every assignment and reschedule is a manual decision
- A sick call forces a coordinator to reshuffle the day by hand
- Skill and certification matching depends on what the dispatcher remembers
- Remote jobs lose connectivity, so field tools must keep working offline

## The solution

A dispatch workflow analyzes technician availability and job priority, then recommends assignments, reschedules and route changes that a dispatcher accepts or rejects.

1. Queries workforce utilization and technician availability in real time
2. Applies the dispatcher's rules: nearest available technician, required certifications, geographic zones and priority
3. Recommends an assignment for each new job, or a full reassignment when a technician calls in sick
4. Gives technicians work instructions and procedures on the job through the mobile app
5. Executes the schedule change once the dispatcher accepts it

## Where people stay in control

- A dispatcher accepts or rejects every recommendation before it takes effect
- No customer-facing change happens silently
- Teams start with utilization queries and procedures, and turn on scheduling actions once they trust the recommendations

## Systems and data

The workflow reads technician availability, job priority and utilization data from the field service platform, provides procedures to technicians through a mobile app that works offline and syncs on reconnection, and writes approved schedule changes back to the dispatch system.

- Mobile app

## Results

- Data query agents achieved over 95 percent accuracy in the documented deployment
- Some deployments reported a 30 percent improvement in field productivity

## Why platform-native

The workflow recommends and a dispatcher approves, inside one field service platform. It runs well platform-native, directly on the model platform with Altivus providing monitoring and governance.

## Frequently asked questions

### Does AI dispatch technicians without a person approving?

No. It recommends assignments, reschedules and route changes, and a dispatcher accepts or rejects each one before it takes effect.

### What happens when a technician calls in sick?

The workflow recommends reassigning that technician's jobs across the team based on availability, skills and priority, and the dispatcher approves the new plan.

### Does it work where there is no signal?

Yes. The technician mobile app works offline and syncs when the connection returns.

## Related Blueprints

- [Tiered estimate generation from site photos](https://altivusops.ai/use-cases/tiered-estimate-generation-from-site-photos/)
- [Concrete delivery and test tracking](https://altivusops.ai/use-cases/concrete-delivery-and-test-tracking/)
- [Inbound call answering and booking](https://altivusops.ai/use-cases/inbound-call-answering-and-booking/)

[Discuss this workflow](https://altivusops.ai/strategy-call/?workflow=field-service-dispatch-and-scheduling)

[Back to the Blueprint library](https://altivusops.ai/use-cases/)

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