When this workflow is a good AI candidate
The workflow becomes attractive when it happens often, the required inputs already exist digitally, the desired output is clear, and a mistake can be caught before an irreversible action.
Signal 01Repeated oftenEnough weekly volume to create meaningful leverage.
Signal 02Evidence availableThe model can work from approved source information.
Signal 03Exceptions separableUncertain cases can be routed to a person.
Current workflow
BEFOREMANUAL BASELINE
01Ticket arrivesCurrent
02Agent readsCurrent
03Search knowledgeCurrent
04Choose queueCurrent
05Draft responseCurrent
A controlled AI-assisted workflow
AFTERPROPOSED BOUNDARY
01Ticket arrivesControlled
02AI classifiesAI-assisted
03Approved context retrievedAI-assisted
04Route or draftControlled
05Human handles exceptionControlled
The goal is a reliable handoff: AI prepares or classifies the repeatable middle, while controls govern what is written, sent, approved or escalated.
Controls to design before launch
- Set confidence thresholds for auto-routing.
- Use approved knowledge and customer context only.
- Escalate billing, security, legal and emotionally sensitive cases.
- Measure reopen and correction rates, not just deflection.
How to measure whether it worked
- First-touch handling time
- Routing accuracy
- Escalation rate
- Reopen rate
- Customer satisfaction / quality review
Replace estimates with observed results.
Measure at least one quality metric alongside time saved. Faster bad work is not an improvement.