W27–W28: Operational Metrics & Reporting

W27–W28: Operational Metrics & Reporting

What you’re doing

You take your AI Ticket Analyzer and turn it into something that helps you run AMS like a machine.

Right now you have:

• classification
• routing
• clustering
• evaluation

Cool. Now make it visible.
Because if nobody sees it, nobody trusts it, and it dies.

Time: 4–6 hours/week
Output: an ops report pack (weekly/monthly) + metrics tables that show what’s happening and what to fix next

The promise (what you’ll have by the end)

By the end of W28 you will have:

• A “weekly ops report” generated automatically
• A “top recurring issues” section with counts + trends
• Routing stats (what goes to dev vs data vs config)
• Model health stats (accuracy, needs_human, drift flags)
• A simple executive summary that doesn’t lie

The rule: reports must drive decisions

A report that doesn’t change a decision is just decoration.

Every section must answer:

• what happened?
• why it matters?
• what we do next?

What to include in your report (keep it tight)

1) Volume and trend

• total tickets processed
• trend vs last period
• spikes (top days)

2) Routing breakdown

• % dev required
• % data quality
• % config/customizing
• % manual client action

This shows where your team is bleeding time.

3) Recurring issues (clusters)

• top 10 clusters by count
• trend vs last week/month
• recommended actions (runbook/fix/mapping)

This is your “investment list”.

4) Data quality top pain

• top rule_id failures
• top fields with issues
• countries/sales orgs with most problems (if relevant)

This turns DQ into a targeted plan.

5) AI system health

• golden set accuracy (latest)
• needs_human rate
• invalid output rate (should be 0)
• drift flags (if label distribution changed)

This stops silent quality decay.

Step-by-step checklist

1) Define the report format

Pick one:

• markdown report generated to /reports/
• HTML page in your app
• both (markdown is enough for v1)

Start with markdown. It’s easy to ship.

2) Build a report generator

One command:

• queries DB (or reads logs)
• computes metrics
• prints a clean report file

Do not hand-write reports. Automate.

3) Add “executive summary” (but keep it honest)

Top 3 bullets:

• biggest spike
• biggest recurring issue
• best next fix to reduce volume

No corporate fluff. Just reality.

4) Make it periodic

Weekly report is enough.
If you want monthly too, make monthly a rollup.

5) Make it shareable

If someone opens the report, they should understand it without you explaining for 30 minutes.

Deliverables (you must ship these)

Deliverable A — Automated weekly report

• report file generated (markdown/html)
• stored under /reports/ with date

Deliverable B — Metrics tables

• routing breakdown
• top clusters
• top DQ failures

Deliverable C — AI health section

• accuracy + needs_human + drift flags
• regression summary (pass/fail)

Deliverable D — “Next actions” list

• top 5 actions to reduce ticket load
• each action linked to cluster/rule_id evidence

Common traps (don’t do this)

Report first. Dashboards are optional.

• Trap 1: “Dashboard before report.”

Tables + a short summary beats a rainbow dashboard.

• Trap 2: “Too many charts.”

Without actions, the report is useless.

• Trap 3: “No next actions.”

Quick self-check (2 minutes)

Answer yes/no:

• Is the report generated automatically (no manual work)?
• Does it show routing breakdown and recurring issues?
• Does it include DQ pain with rule_id evidence?
• Does it include AI health (accuracy/needs_human/drift)?
• Does it end with a clear next-action list?

If any “no” — fix it before moving on.