When AI Dispatcher analyzes unassigned jobs and generates recommendations, each suggestion appears as a detailed card on your calendar. These cards aren't simple notifications — they're decision-support tools packed with context to help you quickly evaluate whether to accept, reject, or modify each assignment.

In this guide:

When you hover over an AI suggestion block on the calendar, the detail card reveals multiple layers of information organized into distinct sections:

The card header displays:

Accepted suggestions show a green "ACCEPTED" status instead.

Every suggestion displays two critical time windows:

🤖 AI Scheduled Time
When the AI recommends scheduling based on optimal routing, technician availability, and workload balancing. You'll see:

👤 Customer Preferred Time
The original time window requested when booking. The AI may suggest scheduling outside this window if it detects conflicts or identifies a more efficient alternative — but the customer's preference is always visible so you can make informed decisions.

⚠️ Key Signal: Large gaps between AI scheduled time and customer preferred time (e.g., scheduling at 2 PM when customer requested 9 AM) usually indicate the AI detected constraints. These warrant closer review and often require a customer callback.

The "Assigned To" row displays the technician the AI selected (with profile avatar and name). This technician was chosen based on:

To understand why this specific technician was selected, expand the reasoning panel (covered below).

The location row shows the job site address. This is particularly important when reviewing travel time estimates — you can quickly verify whether the suggested routing makes geographic sense.

Every AI suggestion includes a confidence score — a percentage from 0-100% that represents how optimal the assignment is. This isn't a guess — it's a weighted calculation across multiple decision factors.

Click the "Why this assignment?" panel to expand the detailed confidence breakdown.

The confidence score is built from five weighted factors:

Does the technician have all required certifications and skills for this job type?

Example: Emergency furnace repair requires EPA 608 certification and HVAC diagnostics experience. A technician with both scores 100%. One with EPA 608 but limited furnace experience might score 75%.

How far is the technician from the job site? This considers actual driving time, not straight-line distance.

Example: If your electrical tech is wrapping up a panel upgrade in Downtown and the next job is a circuit breaker replacement 8 minutes away in the same neighborhood, proximity scores 95%+.

Does the suggested time slot align with both technician availability and customer preferred time?

Example: Customer requested 9-11 AM for a water heater installation. Technician is available 9-11 AM with no conflicts = 100%. If the only available slot is 2-4 PM, score drops to ~65%.

How busy is the technician already? Displays current job count and total hours scheduled for the day.

Example: Technician has 4 jobs totaling 6 hours in an 8-hour shift = 75% utilization. Adding a 2-hour job would push them to 100%, so workload scores around 60%.

Does this job fit logically into the technician's daily route, or does it force backtracking?

Example: Your plumber's route goes: North End (8 AM) → Midtown (10 AM) → South Side (1 PM). A new drain cleaning job on the South Side at 2:30 PM scores high. A job back in the North End at 2:30 PM scores low (forces backtracking).

💡 Pro Tip: Each factor displays a color-coded status (excellent/good/moderate/poor) and a progress bar. The overall confidence is the weighted sum of all factors.

Below the confidence score breakdown, the reasoning panel displays two critical sections:

This section lists the primary reasons why this assignment is recommended. You'll see statements like:

Each strength is marked with a green checkmark. These are the positive factors that drove the AI's decision.

If the AI detected any potential issues or trade-offs, they appear here with orange warning icons. Common concerns include:

Important: Concerns don't necessarily mean the assignment is bad — they're flags for you to review with your operational context. For example, scheduling outside the customer preference might be necessary if all technicians are booked during the requested window, but you'll want to call the customer for approval.

Scenario: Emergency AC repair during a heatwave. Customer requested 2-4 PM, but all certified HVAC techs are booked.

AI Suggestion:

Key Strengths:

Considerations:

Your Decision: Accept the suggestion but call the customer immediately to explain the delay. Emergency AC during heatwave = they'll likely accept same-day service even if it's evening.

Some AI suggestions may display a conflict indicator in the footer. When conflicts are detected, the system shows detailed breakdowns:

The suggested job overlaps with an existing appointment.

Example: AI suggests scheduling a 2-hour drain cleaning from 10 AM - 12 PM, but technician already has a pipe repair from 11 AM - 1 PM. Conflict: 1-hour overlap (11 AM - 12 PM).

The technician cannot physically travel from their previous job to this one in the available time.

Example: Previous job ends at 2 PM in North County. Next job starts at 2:15 PM in South County. Actual drive time: 35 minutes. Technician would arrive 20 minutes late.

The suggested schedule falls outside the customer's preferred window.

Example: Customer requested 9-11 AM. AI scheduled 1-3 PM. Deviation: 4 hours. Call customer for approval.

🚨 Critical Rule: Never accept CRITICAL (red) conflicts without resolving the underlying issue. These indicate scheduling impossibilities that will cause real problems in the field.

Not confident about the suggested assignment? Click the "Reassign" button to see alternative technician options ranked by confidence score.

Each alternative displays:

The top alternative is marked "Recommended" and represents the second-best option.

Job: Pool pump installation requiring electrical and plumbing skills

Primary Suggestion:

Alternative #1 (Recommended):

Alternative #2:

Best Choice: Carlos (Alternative #1) — Higher confidence due to perfect skill match, even though travel is longer. Pool pump installations are complex and benefit from dual expertise.

💡 Remember: Alternatives are ranked by weighted confidence, not just proximity. A technician 20 minutes away with perfect skills often ranks higher than one 5 minutes away without required certification.

After reviewing a suggestion card, you have three primary actions:

Confirms the AI assignment and schedules the job with the suggested technician at the suggested time. The job moves from "AI Suggestion" status to "Accepted" and syncs to FieldCamp on the next batch operation.

When to accept:

Dismisses this specific assignment suggestion. The system prompts for a rejection reason:

Why feedback matters: Your rejection reasons train the AI to avoid similar mistakes in future suggestions.

Opens the alternatives dialog where you can select a different technician from the ranked list. Choosing an alternative updates the assignment while keeping the job in your active schedule.

When to reassign:

When AI Dispatcher processes multiple unassigned jobs, you'll see multiple suggestion cards. Use the selection checkboxes to review in bulk:

Scenario: 15 new jobs came in over the weekend. AI Dispatcher generated suggestions for all.

Efficient Review Process:

Total time: 12 minutes to process 15 jobs. Without AI: 45+ minutes of manual scheduling.

💡 Pro Tip: Bulk actions are particularly useful during peak scheduling periods when you need to process dozens of assignments quickly while maintaining quality control.

To maximize the value of AI suggestions while maintaining dispatch quality:

Review the overall percentage before diving into details. High-confidence suggestions (85%+) rarely need deep inspection.

If the "Considerations" section lists items, read them carefully. These flags often surface issues that aren't obvious from the confidence score alone.

When AI scheduled time differs significantly from customer preferred time, this usually requires a customer callback for approval.

Quick rule:

Before accepting, check the technician's daily schedule to ensure the new job doesn't push them into overtime or create unrealistic back-to-back commitments.

Healthy workload distribution:

When rejecting suggestions, choose accurate rejection reasons. This data trains the AI to avoid similar mistakes in future assignments.

Good rejection feedback example:

This teaches the AI to distinguish between residential HVAC and commercial refrigeration in future suggestions.

For high-value customers, emergency calls, or complex jobs, always review suggestions carefully even if confidence is 90%+.

Extra scrutiny for:

🎯 Remember: AI suggestions are decision-support tools, not mandates. The AI handles the computational heavy lifting — analyzing hundreds of factors per second — but your operational judgment and customer relationships remain irreplaceable.

Need help? Contact support or check the FieldCamp AI Dispatcher documentation.