How to Measure AI ROI in a Field Service Business (Before You Buy Anything)

Why Most Operators Can't Measure Their AI ROI

The process that produces unmeasurable AI ROI is consistent: the operator buys the tool, implements it, and measures success using vendor-provided metrics — jobs processed, time saved, calls handled. Those metrics are real. They don't connect to P&L. A scheduling AI that reduces drive time by 12 minutes per tech per day is useful. Whether it moved gross margin depends on what those 12 minutes were used for. Most operators don't have a way to measure that.

The more fundamental problem: without a pre-implementation baseline, there's no way to attribute post-implementation changes to the tool. If callback rate drops 2 points in the 90 days after implementing an AI callback analysis tool, was it the tool? Or was it the new branch manager who started doing weekly ride-alongs? Without a baseline and a controlled measurement period, you can't know.

Vendors know this. "Up to 30% improvement" benchmarks are built on cases where the vendor's metrics showed improvement. Whether the P&L moved is a different question with a different answer.

The 5 Metrics to Baseline Before Any AI Implementation

Pull these from your FSM and call tracking system before any AI tool goes live. 90-day trailing averages. Store them somewhere you'll find them in a year.

1. Gross margin % by technician on your top 3 job types — 90-day rolling average. Not blended GM. By tech, by job type. This is the number that tells you whether field execution is improving.
2. Callback rate by tech and job type — current rate and 90-day trend. You need the trend, not just the point-in-time number, to distinguish signal from seasonal variation.
3. CSR booking rate by rep on first-time inbound — current rate by rep. Filtered to first-time callers on your top 2 service categories. This is the number that tells you whether call capture is improving.
4. Call answer rate during peak hours — current abandonment rate by hour of day. The peak-hour abandonment rate, not the daily average. Peak abandonment is where the revenue loss is concentrated.
5. Follow-up execution rate on unsold estimates — percentage of unsold estimates that receive at least one follow-up attempt within 5 business days. Most operators don't track this. If you don't know the current rate, that's the baseline: zero.

Pull these before you implement. Pull them again at 30, 60, and 90 days. The delta between the baseline and the 90-day numbers, net of other changes in the operation, is your AI ROI.

What a Realistic AI ROI Timeline Looks Like

These are the realistic milestones, not the vendor timeline:

• 30 days: Tool is live, team is using it, vendor metrics are tracking. No P&L movement yet — too early to detect signal in field service operations where jobs cycle over weeks.
• 90 days: Measurable operational efficiency gains possible (drive time, scheduling utilization). Minimal GM impact. You should be able to see movement in the specific metric the tool targets.
• 180 days: If the tool addresses a real operational gap and was implemented correctly, you should see 1–2 point GM improvement or 10–15% reduction in the metric the tool targets. If you're not seeing movement here, the tool isn't addressing the primary driver of the gap.
• 12 months: Full cycle, seasonal comparison. If you haven't seen P&L movement by 12 months, the tool is not the primary driver of the gap it was sold to address. Either the gap was overstated or the behavioral change required to realize the tool's value hasn't happened.

Red Flags in Vendor AI ROI Claims

The claims that consistently don't hold up in field service operations:

• ROI calculated on time savings without connecting to revenue or margin. "Saves 45 minutes per tech per day" is not an ROI claim. What happened to those 45 minutes is the ROI claim.
• Comparison to industry average rather than your own baseline. If your callback rate is already better than the industry average, the tool's improvement claim based on industry average doesn't apply to your operation.
• Case studies from larger operators applied to your size without adjustment. A 200-tech platform gets different scheduling optimization ROI than a 15-tech shop. The underlying economics are different.
• "Up to X%" framing. The top of the range requires conditions that may not apply. Ask for the median outcome across comparable operators, not the ceiling case.
• Guaranteed ROI on software that requires behavioral change to deliver it. The software can be implemented perfectly and the behavior may not change. The guarantee covers implementation, not adoption.

The Measurement Framework

For each AI tool you're evaluating, map it to one of the four EBITDA levers: field GM, callbacks, CSR booking rate, or follow-up and membership. Identify the specific metric it's designed to move. Baseline that metric. Set a 90-day target based on the vendor's comparable case studies, adjusted for your operation size and current baseline. Measure at 30, 60, and 90 days. If the metric moved the target amount, calculate the dollar value of that movement against your job volume and ticket average. That's your ROI.

When AI ROI Is Real

The use cases where field service operators consistently report genuine, measurable ROI from AI tools:

• Scheduling optimization for operators running high job volume with significant geographic spread. The efficiency gain is real and measurable in utilization metrics. Most shops under 30 techs see modest gains.
• AI overflow call handling during peak hours. Captures calls that would otherwise be missed. The ROI is direct: missed call rate times average ticket times conversion rate.
• Automated follow-up sequences for low-value unsold estimates. Reduces CSR time spent on sub-threshold follow-up calls, freeing capacity for high-value work. Measurable in follow-up execution rate.

The ROI is real in these cases. It's also modest compared to the ROI from closing behavioral gaps in field GM and callback rate. A 2-point GM improvement across 50 techs running $850 average tickets at 400 jobs per month is $408,000 annually. No scheduling tool gets close to that number on its own.