Website form or inbound social message creates a new opportunity.
See the decision before you buy the audit.
This example shows the structure and level of specificity in the ARIIA AI Operations Audit. It uses a fictional service-business lead handoff so you can judge the deliverable without relying on vague claims.
01. Map the operating reality.
The audit starts with the actual trigger, owner, handoffs, exceptions, and evidence. In this example, the expensive gap appears before any AI model is needed.
Someone notices the message when they next open the channel.
There is no durable assignment event, timer, or exception path.
Speed, completeness, and qualification vary by person and time.
No shared record proves who followed up or what happens next.
02. Name the bottleneck.
A useful audit produces a falsifiable operating diagnosis, not an AI shopping list.
Primary finding
The workflow has no explicit acceptance event, named owner, response timer, or visible exception queue.
Why AI is not first:
A better draft does not fix an inquiry that nobody owns. Ownership and measurement come before generation.
03. Separate facts from assumptions.
The real audit replaces unknowns with supplied evidence. It does not invent savings, conversions, or revenue.
| Measure | Illustrative baseline | Proposed control |
|---|---|---|
| Monthly inquiry volume | Assumption: 40; verify from channel records | Count accepted inbound events by source |
| First-response time | Unknown; establish a seven-day baseline | Median and 90th-percentile response time |
| Owner coverage | Not recorded | Every inquiry has a named owner or visible exception |
| Follow-up completion | Not provable | Completion status and next action recorded |
04. Rank the moves.
Each recommendation is ordered by impact, effort, and dependency so the team knows what to do first and what to defer.
Create one intake record and owner-acceptance event
Normalize the channel, timestamp the inquiry, assign an owner, and make unassigned work visible.
Add a response standard and exception queue
Define acknowledgement and human follow-up windows, then escalate only when the timer is breached.
Introduce bounded AI drafting after ownership works
Draft low-risk acknowledgements from approved templates while preserving human decisions for qualification, pricing, and exceptions.
05. Define the build.
The blueprint gives an implementer a concrete operating contract instead of a broad instruction to “add AI.”
Consented inbound inquiry received
Record source, timestamp, contact route, and a minimal inquiry summary.
Named intake owner accepts the record
If nobody accepts it within the agreed window, place it in an exception queue.
Record, assign, acknowledge, and time
Use an approved acknowledgement. Do not begin unsolicited outreach or infer consent.
Qualification, pricing, promises, and exceptions
Keep material customer commitments and sensitive cases with an accountable person.
Minimize data and escalate risk
Do not request passwords, financial credentials, identity documents, or unnecessary customer data.
Response time, owner coverage, and completion
Compare the measured baseline with the post-change process; do not claim causation without evidence.
06. Sequence 30 days.
The plan makes the first implementation decision obvious while leaving room for evidence to change the recommendation.
Baseline
Confirm the owner, consent boundary, channel volume, response time, and failure modes.
Control
Create the intake record, owner assignment, acceptance event, timer, and exception view.
Assist
Add the approved acknowledgement and bounded drafting only after ownership is reliable.
Measure
Review the agreed measures, exceptions, user feedback, and next improvement decision.
Bring one real workflow.
The paid audit applies this structure to your evidence: one workflow, one priority system blueprint, and one practical 30-day plan. Scope and delivery timing are confirmed before payment.