How Jobs Trigger AI Dispatch
When you create a job in FieldCamp, you have two ways to get AI-powered scheduling recommendations: automatic dispatch when the job is created, or manual re-optimization later when you need to adjust schedules. Both methods use the same intelligent routing engine that analyzes your team's skills, location, availability, and current workload to find the best technician match.
This guide explains how both flows work, what information the AI needs, and what happens during processing.
Automatic Dispatch: Triggering AI on Job Creation
When AI Dispatcher is enabled in your workspace settings, every new unassigned job automatically triggers an optimization request. The moment you click "Create Job," FieldCamp sends a webhook to the AI Dispatcher service containing:
The new job details — customer location, required skills, preferred time window, estimated duration
Your current schedule — all confirmed technician assignments for that day
Pending suggestions — any AI recommendations waiting for your approval
Technician availability — who's working, their schedules, skills, and home locations
The AI processes this data in 2-5 seconds and returns a suggestion showing which technician should handle the job and when.
Why include pending suggestions? When multiple jobs arrive within seconds of each other, pending suggestions prevent double-booking. If Job A suggests Bob at 10 AM and Job B arrives before you accept Job A, the AI sees Bob's 10 AM slot as "occupied" and won't suggest him twice.
What Triggers Automatic Dispatch
Automatic dispatch fires when:
You manually create a job without assigning a technician
A customer books online through your FieldCamp booking page
A job is imported from another system (API, CSV upload, integration)
An email-to-job conversion creates an unassigned job
The trigger does not fire if you create a job and immediately assign it to a specific technician — the AI assumes manual assignment was intentional.
Job Information Required
For the AI to generate accurate suggestions, each job must include:
Required Field | Why It Matters | Example |
Customer location | Calculates travel time from technicians | 123 Main St, New York, NY 10001 |
Required skills | Matches only qualified technicians | HVAC Certified, EPA 608 |
Preferred time window | Schedules within customer expectations | Tomorrow, 9 AM - 12 PM |
Estimated duration | Prevents schedule overlaps | 2 hours |
Priority level | Weighs urgency vs efficiency | Standard, Urgent, Emergency |
Optional but helpful fields:
Preferred technician — customer requests specific person
Job type — Installation, Repair, Maintenance, Inspection
Required equipment — ladder truck, specialty tools
Customer notes — special access instructions, pet warnings
Missing location? If a job lacks a valid address, the AI cannot calculate travel times and will leave it unassigned with a "location required" flag. Always verify addresses are complete with city, state, and zip code.
What Happens During Processing
Behind the scenes, the AI Dispatcher:
Validates data — Checks that required fields are present and location is mappable
Identifies candidates — Filters technicians who have required skills and work on the requested day
Checks availability — Removes technicians with schedule conflicts or at capacity limits
Calculates travel times — Measures actual driving distance from each technician's current location
Scores each option — Weighs skills match (35%), proximity (25%), availability (20%), workload (10%), route efficiency (10%)
Generates suggestion — Returns top recommendation with confidence score and reasoning
Stores pending — Adds suggestion to your review queue until you approve or reject
Processing typically completes in under 5 seconds. You'll see the suggestion appear in your AI Dispatcher dashboard with a blue "AI Suggestion" badge.
Manual Re-Optimization: Adjusting Schedules
Sometimes you need to rethink assignments after initial scheduling:
A technician calls in sick and you need to reassign their jobs
An emergency job arrives and you want to rebalance the day's routes
You rejected an AI suggestion and want to see fresh alternatives
Workload became uneven and you want to redistribute jobs
Instead of manually dragging jobs around the calendar, trigger re-optimization to get updated suggestions based on current conditions.
How to Trigger Re-Optimization
Go to AI Dispatcher in your FieldCamp sidebar
Select one or more unassigned jobs (or jobs you want to reassign)
Click "Get AI Suggestions" or "Re-Optimize Schedule"
The AI analyzes current schedule state and returns fresh recommendations
Re-optimization uses the exact same engine as automatic dispatch, but pulls the latest schedule data — accounting for any changes you made since the job was first created.
Batch re-optimization: You can select up to 20 jobs and re-optimize them together. The AI will balance assignments across the group instead of optimizing each job independently, often resulting in better overall route efficiency.
When to Use Re-Optimization
Good use cases:
Technician availability changed (sick day, early finish, late start)
Customer time window shifted
You want to compare alternatives before committing
Initial suggestion had low confidence (<70%) and you want fresh options
Not needed when:
You're happy with the initial suggestion (just accept it)
You already know who should take the job (assign manually)
The job was correctly assigned and nothing changed
Understanding AI Suggestions
Every suggestion includes:
Header Information
Job number and customer name
AI Suggestion badge (blue) — distinguishes from manual assignments
Status — "Pending Confirmation" (orange) or "Accepted" (green)
Time Details
🤖 AI Scheduled Time — when the AI recommends scheduling based on optimal routing
👤 Customer Preferred Time — the original request window
Travel time — estimated drive from previous job
Time deviation warning: If AI scheduled time is 2+ hours outside customer preference, review carefully. Large gaps usually mean the AI detected conflicts — you may need to call the customer for approval or choose an alternative.
Confidence Score
Each suggestion shows a confidence percentage (0-100%) indicating how optimal the assignment is:
90-100% — 🟢 Excellent match, accept with confidence
70-89% — 🔵 Good match with minor trade-offs
50-69% — 🟠 Acceptable but review considerations
Below 50% — 🔴 Suboptimal, check alternatives
Click "Why this assignment?" to expand the reasoning panel showing:
Key Strengths — green checkmarks for positive factors
Considerations — orange warnings for trade-offs or concerns
Decision breakdown — scores for skills (35%), proximity (25%), availability (20%), workload (10%), route efficiency (10%)
Real Example: HVAC Repair
Job: AC not cooling, customer requested 2-4 PM today
AI Suggestion:
Assigned: Mike (HVAC Certified, EPA 608)
Confidence: 87% (Blue — Good)
Scheduled: 3:15 PM - 5:15 PM
Travel: 12 minutes from previous job
Key Strengths:
✅ Perfect skills match (EPA 608, residential HVAC experience)
✅ Within customer preferred window (3:15 PM starts in 2-4 PM range)
✅ Efficient routing (previous job is 12 min away)
✅ Moderate workload (Mike at 65% capacity today)
Considerations:
⚠️ Starts 1 hour 15 minutes after customer's earliest preference (customer requested 2 PM, AI scheduled 3:15 PM)
Decision: Accept — arrival is within the 2-4 PM window and all other factors are excellent.
Taking Action on Suggestions
After reviewing a suggestion, you have three options:
✅ Accept
Confirms the assignment. The job locks into the technician's schedule, they receive a push notification on their mobile app, and the customer gets an automated confirmation with arrival time.
When to accept:
Confidence 85%+ with no critical concerns
All decision factors show green/blue status
Time deviation from customer preference is acceptable
No better alternative available
❌ Reject
Dismisses this specific suggestion. The system prompts for a rejection reason (skills mismatch, time conflict, workload concerns, route inefficiency). Your feedback trains the AI to avoid similar mistakes in future suggestions.
When to reject:
Critical conflict detected (overlapping appointments, impossible travel)
Skills don't actually match requirements
Technician explicitly unavailable (you know they're on vacation but system doesn't)
Customer specifically requested different technician
🔄 Reassign
Opens the alternatives panel showing 2-3 other technician options ranked by confidence. Each alternative displays trade-offs compared to the primary suggestion.
When to reassign:
Confidence below 70%
Alternative has higher confidence or better fits your preferences
Primary suggestion has workload concerns but alternative has capacity
You want to balance work across multiple technicians
Common Scenarios
Emergency Job: Burst Pipe
Situation: Customer calls with water spraying everywhere, needs help ASAP
Process:
Create job, mark priority as "Emergency"
Set time window to "Next 2 hours"
Required skills: Emergency Plumbing, Water Main Shutoff
AI immediately suggests nearest qualified plumber
Suggestion shows current assignment + travel time: "Carlos finishing service call 8 min away, can arrive in 15 minutes"
Accept → Carlos gets urgent notification, customer gets ETA text
Confidence factors: Proximity weighted highest for emergencies, skills match critical, route efficiency less important.
Technician Sick Day: Redistributing Jobs
Situation: Sarah calls in sick, has 6 jobs scheduled today
Process:
Mark Sarah as unavailable in Team settings
Go to AI Dispatcher, filter for Sarah's jobs
Select all 6 jobs, click "Re-Optimize"
AI analyzes remaining technicians' schedules and generates new suggestions
Review confidence scores — some may be lower due to tighter capacity
Accept high-confidence suggestions, manually handle edge cases
System sends updated notifications to customers with new technician names and ETAs
Customer Reschedules: Time Window Changed
Situation: Customer originally requested 9 AM, now can only do 2 PM
Process:
Edit job, update preferred time window to 2-4 PM
Click "Get AI Suggestions" (re-optimization)
AI may suggest same technician at new time, or different technician if original is now booked
Review new confidence score — route efficiency may change if 2 PM creates backtracking
Accept or explore alternatives
Best Practices
Complete job info upfront — accurate location, skills, duration = better suggestions
Trust high confidence — 85%+ suggestions are usually optimal, minimal review needed
Review considerations — orange warnings highlight trade-offs worth checking
Use batch re-optimization — when multiple jobs need reassignment, optimize together for better routes
Provide rejection feedback — helps AI learn your preferences and avoid repeated mistakes
Keep team info current — update skills, schedules, and availability immediately when changes occur
Call customers for time deviations — 2+ hour shifts from preference usually need approval
Related Articles
Remember: AI Dispatcher suggests — you decide. The system handles the computational work of analyzing hundreds of factors per second, but your operational judgment and customer relationships remain essential to great service.