Modeled measurement case
Customer Support AI: A Measurable Resolution and Agent-Capacity Case
A modeled measurement case for returning support capacity and shortening resolution time while keeping empathy, unusual cases, commitments, and escalation authority with people.
FIELDRUNTIME / CASE / 03
TRANSPARENCY / NOT CLIENT RESULTS
This is an evidence-ready ROI model, not a claim about a completed client deployment. Every baseline and target is an explicit example assumption. During Field Discovery, each number is replaced with the organization's verified operating data before a business case is approved.
THE OPERATING PROBLEM
Measure the work before changing it.
Support teams repeatedly gather account context, search prior incidents, reproduce issues, prepare responses, and hand cases across functions. A governed support harness can assemble evidence, propose next actions, and preserve resolution memory without removing human ownership of the customer relationship.
MODEL INPUTS
Every assumption is visible.
The starting model
- Team modeled
- 25 support agents
- Daily volume
- 15 tickets per agent
- Context and preparation
- 12 minutes per ticket
- Working year and value
- 230 days at $55/hour
OUTCOME MODEL
Targets require evidence.
What the pilot must prove
| Metric | Example baseline | Modeled target | Evidence source |
|---|---|---|---|
| Context/preparation capacity | 17,250 hours/year | Return 4,312–6,900 hours | Ticket events and activity sampling |
| Modeled capacity value | $948,750/year | $237,000–$379,500 returned | Finance-approved loaded-cost model |
| Median first response | 8 hours | 4–6 hours | Ticket-system timestamps |
| Median resolution time | 32 hours | 20–24 hours | Open, escalation, and resolution events |
| Reopened cases | 14% | 8–10% | Reopen reason and quality review |
THE VALUE MODEL
A calculation the CFO can challenge.
25 agents × 15 tickets/day × 230 days × 12 minutes ÷ 60 × $55/hour = $948,750 annual capacity exposed to context gathering and response preparation.
At 25–40% capacity recovery, the modeled annual value is approximately $237,000–$379,500 before counting retention, customer satisfaction, reduced escalations, or engineering interruption.
HUMAN AUTHORITY + CONTROLS
- People retain authority over empathy, commitments, unusual cases, escalation, and customer remedies.
- Responses cite approved sources and stop when evidence is missing or conflicting.
- Sensitive account context follows source permissions and complete access logging.
MEASUREMENT PLAN
Turn the model into evidence.
- Baseline by issue type, customer tier, channel, and escalation path.
- Shadow agent recommendations and score evidence quality before sending customer-facing text.
- Compare first response, resolution, reopen rate, escalation, and human edit distance.
- Link every promoted skill or memory change to validated downstream outcomes.
Replace assumptions with your operating data.
Map the real workflow and build the evidence case.
Bring one repetitive, expensive, or consequential workflow. We will map what stays human, what becomes reliable software, where AI creates measurable value, and what evidence will make the system improve.