Inside Dynamic Rerouting: How AI Responds to Delays in Minutes

What Triggers a Dynamic Reroute?

Dynamic rerouting is triggered when the AI detects that a technician’s planned schedule can no longer be executed as expected.

FieldCamp’s AI monitors specific data signals that indicate schedule adjustments are needed. These triggers fall into four categories: GPS deviations, technician status updates, time-based variance, and external events.

GPS Deviation Detection

How does the system detect a technician delay automatically?

FieldCamp’s mobile app sends GPS updates every 1–5 minutes. The AI continuously compares a technician’s actual location against their planned location for that time of day.

A potential delay is flagged when both conditions are met:

• The technician is more than 0.5 miles away from their planned next location
• There has been no movement for 10+ minutes

Once flagged, the system immediately begins reroute evaluation. GPS deviation analysis plays a key role in reducing unnecessary drive time while preserving customer commitments.

Example:
At 2:00 PM, a technician is still at 123 Oak Street.

• Job started: 1:00 PM
• Estimated duration: 45 minutes
• Planned departure: 1:45 PM
• Actual state: no movement for 12 minutes

The AI identifies a 15-minute delay and initiates dynamic rerouting.

Status Update Triggers

Can technicians manually trigger dynamic rerouting?

Yes, technicians can manually trigger reroutes through status updates in the mobile app:

• “Needs parts” – Tech is stuck waiting for equipment
• “Running late” – Tech acknowledges delay
• “Completed early” – Opens capacity for additional work
• Job marked complete – Frees technician for next assignment

These updates immediately feed into the AI’s decision engine, complementing automated detection and are part of a broader AI-driven technician assignment strategy.

Time-Based Variance Checks

When does the AI intervene based on schedule variance?

The system continuously compares actual job progress against the planned schedule.

If the variance exceeds 15 minutes, the AI evaluates whether downstream jobs are at risk and begins calculating alternative assignments.

Variance detection acts as an early signal, allowing the AI to protect downstream time-window commitments before delays cascade.

External Triggers

What external events can initiate dynamic rerouting?

Dynamic rerouting can also be triggered by external disruptions, including:

• Customer cancellation – Creates a gap that needs filling
• Emergency job insertion – High-priority work that must fit into existing schedules
• Traffic spike alerts – Real-time traffic data showing significant delays on planned routes
• Weather disruptions – Conditions that affect travel times or job completion

These triggers are evaluated using a decision framework similar to how AI dispatchers weigh constraints, priorities, and trade-offs in real-world scheduling.

Automatic vs. Manual Triggers: Does every reroute happen automatically?

No, some reroutes fire automatically based on business rules, while others require dispatcher approval—particularly when VIP customers or cross-zone reassignments are involved.

Let’s see some examples: 

For more on how FieldCamp’s algorithms process these triggers, see our guide to How AI Dispatcher Algorithms Work: Complete Guide.

The 4-Minute Cascade: Inside a Live Reroute

What happens between the moment a delay is detected and when new routes are deployed? Here’s the step-by-step breakdown of FieldCamp’s dynamic rerouting process.

2:00:00 PM – Delay Detected

GPS shows Tech #3 still at the job site, 15 minutes past the planned departure. System flags deviation: more than 0.5 miles from next location with no movement for 12 minutes.

The AI immediately begins impact analysis rather than waiting for manual notification.

2:00:15 PM – Impact Analysis

Within 15 seconds, the AI evaluates Tech #3’s remaining jobs:

• Job #5 – Scheduled 2:30 PM (2:30–3:00 PM time window)
• Job #6 – Scheduled 4:00 PM (4:00–5:00 PM time window)
• Job #7 – Scheduled 5:30 PM (VIP customer, protected status)

Based on the current delay, new ETAs are calculated. Job #5 would now start at 3:15 PM—violating the 2:30–3:00 PM customer time window.

2:01:00 PM – Capacity Scan

The system checks nearby technicians for available capacity:

• Tech #2: Finishes at 2:15 PM, HVAC + EPA Certified, 8 minutes to Job #5, available capacity
• Tech #5: Finishes at 2:45 PM, HVAC only, 18 minutes away, available capacity
• Tech #4: En route to job, plumbing only (wrong skills), 6 minutes away, no capacity

Tech #2 emerges as the best candidate: closest with matching skills and available capacity.

2:02:00 PM – Decision Cascade

The AI determines the optimal reassignment:

• Job #5 and Job #6 → Reassign to Tech #2 (best proximity + capacity + skills match)
• Job #7 → Keep with Tech #3 (VIP customer, protected status)

This decision respects business rules while solving the immediate scheduling problem.

2:03:00 PM – Route Recalculation

FieldCamp’s incremental solver rebuilds only the affected routes—Tech #2 and Tech #3. All other technicians’ schedules remain unchanged.

Incremental re-optimization is a targeted route recalculation approach where the AI dispatcher adjusts only the affected portion of the schedule—typically the delayed technician and nearby techs with capacity—rather than rebuilding all routes from scratch. This approach minimizes disruption to confirmed appointments while solving the immediate scheduling problem.

2:04:00 PM – Deployment

New routes are pushed to Tech #2 and Tech #3’s mobile apps. Customer ETA notifications are triggered automatically for Job #5 and Job #6 customers. The dispatcher receives a summary alert—no approval required since these are PLANNED status jobs.

Before reroute:

• Tech #3: Job #4 (current, delayed) → Job #5 (2:30 PM) → Job #6 (4:00 PM) → Job #7 (5:30 PM, VIP)

After reroute:

• Tech #3: Job #4 (now 3:00 PM) → Job #7 (4:30 PM, protected VIP)
• Tech #2: Job #5 (2:45 PM) → Job #6 (4:15 PM)

FieldCamp’s system completes this process in 3–5 minutes on average.

Protected vs. Flexible Jobs: What Moves and What Stays?

FieldCamp uses job status and business rules to determine which appointments remain fixed during rerouting.

Protected job status refers to appointments that cannot be moved or reassigned during dynamic rerouting due to specific business rules or customer commitments. In FieldCamp, protected jobs include CONFIRMED status appointments, jobs starting within 30 minutes, VIP customer appointments, and multi-technician team jobs. These jobs remain “pinned” in the schedule while the AI optimizes around them.

Job Status Hierarchy

For instance, Mrs. Johnson’s 2:00 PM HVAC repair with CONFIRMED status cannot be reassigned, but a PLANNED maintenance visit scheduled for 3:00 PM can move to another technician if needed.

How This Works in Practice?

A plumbing company has 8 jobs scheduled for the day:

• Jobs #1, #2, and #8 – CONFIRMED status (dispatcher explicitly confirmed with customers)
• Job #5 – VIP customer (Mrs. Johnson, repeat client with high lifetime value)
• Job #6 – Starts in 20 minutes (tech already en route)
• Jobs #3, #4, and #7 – PLANNED status (flexible, not yet confirmed)

When Tech #1 runs late, the AI can only reassign Jobs #3, #4, and #7. All others are protected by business rules.

Why This Matters?

In FieldCamp, approximately 60–70% of jobs remain flexible (PLANNED status) during morning hours, allowing maximum optimization during disruptions.

As the day progresses and more jobs are confirmed, the percentage of protected jobs increases—which is why early detection of delays is critical.For more on the 50+ variables the AI considers during reroute decisions, see our guide to What is an AI Dispatcher? Full 2025 Guide.

How FieldCamp Decides Who Gets Reassigned?

FieldCamp evaluates proximity, skills, capacity, and time windows simultaneously to find the optimal technician match.

1. Proximity Calculation

The AI measures drive time from each available technician’s current or next location to the unassigned job. This isn’t straight-line distance—it accounts for actual road routes and current traffic conditions.

2. Skill Matching

The technician must have the required skills to be eligible for reassignment. If Job #5 requires EPA certification for refrigerant handling, only technicians with that certification are considered.

3. Capacity Check

The technician must have available time in their schedule without violating:

• Shift end time
• Overtime limits
• Scheduled breaks
• Existing confirmed appointments

4. Time Window Preservation

Any reassignment must keep the job within the customer’s promised time window. If moving Job #5 to Tech #2 would cause arrival at 3:45 PM but the customer was promised 2:30–3:00 PM, the system evaluates whether that violation is acceptable or if another option exists.

5. Workload Balancing

The AI considers fairness across the team. It avoids overloading one technician while others sit idle—unless that’s the only way to meet customer commitments.

How does FieldCamp’s AI balance workload during rerouting?

FieldCamp’s workload balancing operates on three levels during dynamic rerouting:

• Daily Balance: Distributes jobs evenly across days to avoid Monday overload and Friday slowdowns. Without AI, you might see 25 jobs on Monday and 8 on Friday. With AI balancing, the distribution smooths out to a predictable, manageable workload each day.
• Technician Balance: Ensures each tech gets a fair share of jobs without favoritism. When reassigning delayed jobs, the AI checks current workload across all available technicians—not just proximity.
• Overtime Control: Minimizes unnecessary overtime by factoring in per-technician overtime limits and cost multipliers. A technician who’s already at 7 hours won’t receive a reassigned job if another qualified tech has capacity.

The system uses configurable soft constraints to balance workload against routing efficiency. You can adjust the weight: increase it for more balanced distribution, decrease it for more route-focused optimization.

For a deeper dive into workload balancing with AI and how it impacts technician satisfaction and retention, see our detailed guide.

6. Penalty Scoring

Each potential reassignment receives a score based on:

• Drive time increase
• Time window risk
• Workload balance impact
• Skill match quality
• Customer relationship factors

A technician 15 minutes away with perfect skill match scores higher than a technician 8 minutes away who would need to work overtime to complete the job.

Real-World Example

Tech #3 is delayed. Jobs #5 and #6 need reassignment. Here’s how the AI evaluates options:

Tech #2 wins: 8 minutes away, EPA certified, available at 2:15 PM.

FieldCamp’s incremental solver recalculates affected routes in 15–30 seconds. By contrast, a full-day rebuild needs 2–5 minutes. This speed difference matters when customers are waiting for updates.To know more on AI route optimization and its route planning capabilities, see our detailed feature documentation.

Automatic Customer Updates During Reroutes

When jobs are reassigned or delayed, customers shouldn’t be left wondering. FieldCamp automatically triggers communication workflows as part of the rerouting process.

ETA Recalculation

When a job is reassigned or a technician is delayed, the system immediately calculates a new arrival time based on the updated route. This isn’t a guess—it’s based on actual drive time calculations and the technician’s current schedule.

Notification Triggers

SMS or email notifications are sent automatically when:

• ETA changes by more than the 15-minute threshold mentioned earlier
• A different technician is assigned
• The appointment is rescheduled to a different time slot

Message Templates

FieldCamp uses customizable templates that communicate clearly without alarming customers:

Delay notification:

“Your technician is running 15 minutes late. New arrival time: 3:15 PM”

Earlier arrival:

“Good news—we’re sending a technician earlier than expected. New arrival: 2:45 PM”

Technician change:

“Update: Your technician Tom (Tech #2) will arrive at 2:50 PM instead of 2:30 PM. Track his location here: [link]”

Technician Profile Updates

If a job moves to a different technician, the customer receives updated information including:

• New technician name
• Photo (if configured)
• Direct contact information
• Updated ETA

Do dispatchers need to manually notify customers? 

No. These notifications fire automatically within 60 seconds of route recalculation. The system respects customer opt-out preferences while logging communication attempts.

For workflow automation capabilities, see FieldCamp’s real-time customer service updates workflow.

Opt-Out Handling

Customers who have disabled notifications don’t receive updates. The system respects their preferences while still logging the communication attempt for the dispatcher’s reference.

Example Scenario

Mrs. Johnson’s HVAC repair was scheduled with Tech #3 at 2:30 PM. Tech #3 runs late. AI reassigns the job to Tech #2, new ETA 2:50 PM.

At 2:04 PM, Mrs. Johnson receives SMS:

“Update: Your technician Tom (Tech #2) will arrive at 2:50 PM instead of 2:30 PM. Track his location here: [link]”

No dispatcher involvement required.

When Dispatchers Step In?

Dynamic rerouting handles most schedule adjustments automatically. But some situations require human judgment—and FieldCamp is designed to recognize when to ask for help.

Approval Workflows

Some reroutes execute automatically while others require dispatcher confirmation. The distinction is based on configurable business rules:

Automatic execution (no approval needed):

• Reassigning PLANNED status jobs
• Moving jobs between technicians in the same zone
• Adjusting ETAs within acceptable ranges
• Filling gaps from cancellations

Approval required:

• Reassigning VIP customers to different technicians
• Moving CONFIRMED jobs (if override is enabled)
• Reassigning jobs across zone boundaries
• Significant time window violations

Override Scenarios

Dispatchers can reject AI suggestions and manually assign differently. When a dispatcher overrides a recommendation, they’re not fighting the system—they’re teaching it.

Example: AI suggests moving Mrs. Peterson’s job (VIP customer) from Tech #3 to Tech #5 due to delay.

Dispatcher receives approval request:

“Reassign VIP customer Mrs. Peterson to Tech #5? Reason: Tech #3 delayed 40 min.”

Dispatcher knows Mrs. Peterson prefers Tech #3 and has complained about technician changes before. She clicks “Keep assigned.”

AI adjusts by delaying Mrs. Peterson’s start time instead, updates her ETA, and reassigns a different (non-VIP) job to Tech #5.

Feedback Loop Mechanics

When dispatchers override AI suggestions, the system learns. If a dispatcher consistently rejects reassignments for certain customer types or situations, the AI adjusts future recommendations to recognize similar patterns.

This isn’t instant—it takes multiple overrides before the pattern is established. But over time, the system becomes more aligned with your business’s specific preferences.

Notification Dashboard

Dispatchers receive alerts for significant reroutes but don’t need to approve routine adjustments. The dashboard shows:

• Summary of all reroutes in the last hour
• Pending approval requests
• Override history
• Customer notification status

Manual Intervention Rate

According to FieldCamp customer data from implementations with 5+ technicians, dispatcher approval is required for less than 15% of reroutes after the first 30 days of system learning. The remaining 85% execute automatically, freeing dispatchers to focus on exceptions and customer relationships.

Common Reroute Scenarios

Different disruptions require different responses. Here’s how FieldCamp handles the most common mid-day challenges.

Scenario 1: Technician Delay

Situation: Tech #1 runs 45 minutes late due to a commercial HVAC unit requiring unexpected compressor replacement.

AI Response:

1. Detects delay via GPS deviation and time variance
2. Evaluates Tech #1’s remaining jobs: #5, #6, #7
3. Identifies Tech #2 as available with matching skills
4. Moves Jobs #5 and #6 to Tech #2
5. Updates ETAs for affected customers
6. Tech #1 continues with Job #7 on adjusted timeline

Result: Three customers receive updated ETAs. Two jobs are completed on time by Tech #2. Tech #1 finishes their day without overtime.

Scenario 2: Early Completion

Situation: Tech #5 finishes their 11:00 AM job 30 minutes early. They’re now sitting idle until their next scheduled job at 1:00 PM.

AI Response:

1. Detects early completion via job status update
2. Checks unassigned job queue for nearby work
3. Finds Job #12 (15 minutes away, matches skills, fits time window)
4. Inserts Job #12 into Tech #5’s route
5. Updates customer for Job #12 with earlier-than-expected arrival

Result: Tech #5’s idle time is eliminated. An additional job is completed. Customer for Job #12 gets faster service.

Scenario 3: Customer Cancellation

Situation: Customer cancels their 3:00 PM appointment with 2 hours notice.

AI Response:

1. Detects cancellation via status update
2. Identifies gap in Tech #4’s schedule (3:00–4:30 PM now open)
3. Pulls unassigned Job #9 from queue (nearby, fits time window, matches skills)
4. Inserts Job #9 into the gap
5. Recalculates drive time, updates Tech #4’s route

Result: No lost productivity from cancellation. Job #9 customer gets same-day service.

Scenario 4: Emergency Insertion

Situation: Emergency HVAC call at 2:17 PM—customer has no cooling, outdoor temperature is 95°F.

AI Response:

1. Receives emergency job with URGENT priority flag
2. Scans all technicians: Tech #5 is closest (12 min away), has EPA certification, just finished early
3. Evaluates impact: inserting emergency won’t break Tech #5’s 4:00 PM time window for next job
4. Inserts emergency job immediately
5. Updates customer: “Technician arriving in 15 minutes”

Result: Emergency handled within 30 minutes. Existing schedule preserved. Customer crisis resolved.

FieldCamp’s system completes emergency job insertions in an average of 8–12 seconds from request to technician assignment.

Scenario 5: Traffic Spike

Situation: Real-time traffic shows 25-minute delay on I-85 due to accident. Tech #2 is en route to Job #7 (scheduled 3:00 PM).

AI Response:

1. Receives traffic alert from mapping integration
2. Recalculates Tech #2’s ETA: now 3:22 PM (violates 3:00–3:30 PM time window)
3. Identifies Tech #6 on different route, can arrive at 2:55 PM
4. Reassigns Job #7 to Tech #6
5. Updates customer with new technician information

Result: Customer commitment honored despite traffic. Tech #2 is rerouted to a different job that doesn’t require the blocked highway.

How FieldCamp Handles This?

FieldCamp’s dynamic rerouting isn’t a single feature—it’s a combination of capabilities working together in real-time.

Core Capabilities

• Real-time GPS tracking with 1–5 minute update intervals
• Deviation detection with configurable thresholds (default: >0.5 miles + 10 min no movement)
• Incremental solver that recalculates only affected routes in 15–30 seconds
• Automatic customer ETA updates via SMS/email within 60 seconds
• Protected job hierarchy (CONFIRMED, VIP, <30 min start time, multi-tech)
• Dispatcher approval workflows for VIP/zone reassignments
• Machine learning feedback loop from dispatcher overrides

What Makes FieldCamp Different?

Most Field Service Management (FSM) systems require manual dispatcher intervention for every schedule change. When a technician runs late, the dispatcher has to:

1. Notice the delay (often minutes later)
2. Mentally evaluate other technicians’ schedules
3. Check skills and certifications
4. Calculate drive times
5. Call or text affected customers
6. Update the schedule manually

FieldCamp’s incremental approach means faster recalculation (15–30 seconds vs. 2–5 minutes for full rebuild). Protected job logic prevents customer service issues from “over-optimization.” Automatic customer notifications eliminate dispatcher workload.

If you’re evaluating field service automation software for your business, dynamic rerouting is one of the key capabilities that separates AI-powered solutions from traditional dispatch tools.

Incremental vs. Full Re-Optimization

When disruptions occur, FieldCamp doesn’t always rebuild the entire day’s schedule. Understanding when the system uses incremental versus full re-optimization helps explain why some reroutes are faster than others.

Incremental Approach (Default)

For most disruptions, FieldCamp recalculates only the “affected subgraph”—the delayed technician plus nearby technicians with available capacity.

What gets recalculated:

• The delayed technician’s remaining route
• 2–4 nearby technicians who could absorb reassigned jobs
• Customer notifications for affected appointments only

What stays unchanged:

• All other technicians’ schedules
• Confirmed appointments for unaffected techs
• Jobs in different zones or business units

Stability benefit: Confirmed appointments for unaffected techs remain unchanged. No unnecessary notifications go out. Technicians who aren’t involved don’t see schedule changes.

Full Re-Optimization Triggers

Sometimes the disruption is significant enough that incremental adjustments aren’t sufficient:

• Multiple simultaneous delays (3+ technicians affected at once)
• Major traffic event affecting more than 50% of the team
• Dispatcher manually requests full rebuild
• Significant capacity change (technician calls in sick, new emergency jobs flood in)

Trade-Offs

FieldCamp Default Behavior

• Single disruption: Incremental (evaluates delayed tech + 2–4 nearby)
• Multiple disruptions (3+ techs): Full re-optimization
• Dispatcher override: Can force either approach

Example Comparison

• Single delay scenario:
  Tech #3 runs late. Incremental solver evaluates Tech #3 + Tech #2 + Tech #5 (nearby with capacity). Recalculates in 22 seconds. Techs #1, #4, #6, #7, #8 see no changes.

• Multiple delay scenario:
  Techs #2, #3, and #6 all delayed due to a major accident on I-285. The system triggers full re-optimization, rebuilds all routes in 3 minutes, and finds globally optimal solutions.

Based on FieldCamp’s analysis of 50+ customer deployments in 2024, incremental re-optimization handles 92% of mid-day disruptions.

Multi-Technician Cascade Effects

When Tech #1 runs 30 minutes late, the delay ripples through 2–3 downstream assignments unless the system intervenes.

How Cascades Happen

• Initial delay: Tech #1 runs 30 minutes late on Job #2.
• First ripple: Tech #1’s Job #4 needs reassignment to meet time window. Moves to Tech #2.
• Second ripple: Tech #2 now has an extra job. Their Job #8 is delayed by 20 minutes.
• Third ripple: Job #8 customer receives updated ETA notification.

Without intelligent rerouting, this cascade can continue—each adjustment creating new conflicts that require more adjustments.

Ripple Minimization

FieldCamp’s AI limits cascades to 2–3 “hops” by choosing reassignments that don’t create new conflicts. The system prioritizes:

• SLA protection: Keeping time-window commitments over minimizing total drive time
• Stable assignments: Preferring solutions that don’t require moving jobs from the receiving technician
• Workload balance: Avoiding solutions that overload one tech and create future problems

Team Coordination

Multi-technician jobs (like a 3-person tree removal crew) require special handling. If one crew member is delayed, the entire crew’s schedule adjusts together.

Example: Tree service company has a 3-person crew (Techs #1, #2, #3) scheduled for oak tree removal at 10:00 AM. Tech #1 has a truck breakdown at 9:30 AM.

AI Response:

1. Detects multi-tech dependency (all three techs share visitGroupId)
2. Cannot split the crew—job requires all three
3. Delays entire crew’s start time to 10:45 AM (when Tech #1 arrives with replacement vehicle)
4. Updates customer with new arrival time
5. Evaluates crew’s 2:00 PM job—reassigns to different crew to prevent afternoon cascade

Communication Coordination

When cascades affect multiple customers, all notifications are sent simultaneously—not in sequence. This prevents the awkward situation where Customer A gets an update, calls to complain, and Customer B still doesn’t know about their delay.

According to FieldCamp’s internal testing data, the system’s multi-tech coordination prevents most cascade failures by treating crew schedules as single units.

Conclusion

Reactive dispatching responds to delays after they cascade. Dynamic rerouting prevents the cascade from starting.

FieldCamp detects delays within 10 minutes, recalculates routes in 15–30 seconds, and deploys updates before the next appointment is affected. Wanna know the technicalities in detail? Let’s catch up on a quick 30 mins call and we assure you won’t regret.

Trust us, with FieldCamp’s AI dispatching, dispatchers will spend less time firefighting and more time on strategic decisions. Customers will receive proactive updates instead of wondering where their technician is. Technicians will get clear, updated routes instead of confusing mid-day changes.

So, what are you waiting for?