Operating in 50 cities30-day installYou own every account
Home / AI services / AI Scheduling & Dispatch

Operations · Application area 16 of 26

AI Scheduling and Dispatch for Field Teams

CyberGipsy installs scheduling that accounts for skills, travel time, parts, permits and priority, then re-plans automatically when a job overruns or a customer cancels. Dispatchers stop rebuilding the day by hand every morning.

Definition. AI scheduling and dispatch is the automated assignment of jobs to people and time slots, optimised for travel, skills, availability and priority, with continuous re-planning as reality changes.

Fleet of service vans leaving a depot at dawn, wide shot, city skyline in the distance, cool morning light with warm
Field contextCyberGipsy / 35mm field notes

The problem this removes

  • The schedule is a whiteboard and it is wrong by 10:00.
  • Crews spend hours in traffic crossing the city in the wrong order.
  • One cancellation collapses the whole day and nobody backfills the slot.

What we actually build

  1. Constraint model

    Skills, certifications, vehicles, tools, parts, working hours, zones, customer windows.

  2. Optimisation

    Routes and sequences that minimise travel and maximise completed jobs per day.

  3. Live re-planning

    Overruns, cancellations and emergencies trigger an automatic reshuffle with human approval.

  4. Backfill

    Cancelled slots offered instantly to waiting customers.

  5. Customer communication

    Confirmations, on-the-way messages and accurate arrival windows.

  6. Capacity forecasting

    Where you will run out of people, and when, by service and season.

  7. Field app

    Today's list, navigation, job detail, checklists, photo capture, sign-off.

Dispatcher screen showing an optimised route map with job pins across a city
System in useCyberGipsy / 35mm field notes

How the install works

Five steps. Nothing here is a workshop.

  1. Model the constraints

    Written down properly, usually for the first time.

  2. Import the real data

    Historical job durations, not optimistic ones.

  3. Run parallel

    Optimised plan compared to the dispatcher's plan for two weeks.

  4. Hand over control

    Automatic planning with dispatcher override, never the reverse.

  5. Tune seasonally

    Demand patterns change; the model is reviewed quarterly.

What changes, in numbers

Honest ranges from installs of this type. Your baseline is measured during the diagnostic so the comparison is yours, not an industry average.

MeasureTypical beforeAfter install
Time spent building the daily plan1-2 hMinutes, reviewed
Travel time per crew per dayBaselineTypically reduced
Jobs completed per crew per dayBaselineIncreased where travel dominates
Accurate arrival windows given to customersRarelyEvery job
Cancelled slots refilledSeldomAutomatically offered

What AI will not do here

Optimisation is bounded by data quality. If your job durations are guesses, the plan will be a well-organised guess. It also cannot fix understaffing or unrealistic promises made at the sales stage, and it should never override a dispatcher's local knowledge about a difficult customer or a difficult building. Human override always stays available.

Dispatcher and technician talking beside a van, both looking at a phone, morning light
People running itCyberGipsy / 35mm field notes

AI Scheduling & Dispatch: questions we get asked

Do I need a fleet tracking system?

It helps but it is not required. Phone-based location during working hours is usually enough, and it raises fewer privacy issues with the crew.

How does it handle emergencies?

Priority rules insert urgent jobs and reshuffle the rest, showing the dispatcher exactly what changed and who needs to be told.

Will it schedule people into unrealistic days?

Not if the duration data is honest. We deliberately load historical actuals rather than planned durations, because optimism in the model becomes overtime in reality.

What about crew preferences and fairness?

Fairness rules can be encoded: rotation of unpopular jobs, distance from home, preferred zones. Ignoring this is the fastest way to lose good technicians.

Does it work with subcontractors?

Yes, with separate availability, rates and skill sets, and with clear rules about which work can be subcontracted.

How long to install?

Two to four weeks, with the constraint modelling being the slow part rather than the software.

Is ai scheduling & dispatch your highest-return system?

The audit ranks every automation by return for your specific business. Most companies discover the answer is not the one they expected.

Book an audit Cities