I knew something was off when a rep Slacked me, “My queue’s empty but my calendar’s not any freer.” Scores looked healthy. Tasks had the right shapes. And yet—silence. By lunch we found it: an API token had expired over the weekend, the webhook retried itself into a corner, and our “most active accounts” list was a museum exhibit.

Quiet Failures

AI SDRs don’t explode. They fade. A field name changes upstream, decay rules keep aging leads like fine wine, a vendor limits requests and the system responds by being very polite and very still. No alarms. Just fewer tasks today than yesterday, explained away as “seasonality” or “inbox fatigue.” The cost of neglect is rarely a fire; it’s a slow leak.

Bandwidth Is Real

RevOps isn’t lazy. RevOps is busy. Commission runs, QBR decks, CRM cleanups, renewals, new territories—each one defensible, each one urgent. The AI SDR sits in the corner labeled “project” instead of on the shelf labeled “product.” That label decides whether it gets a maintenance window or a wish.

The uncomfortable truth: an unattended AI SDR will underperform a mediocre human process that someone actually owns. Ownership beats brilliance.

What Breaks

When no one’s watching, small seams open:

The data sync “succeeds” but stops updating two columns that your scoring model depends on. Decay rules, written when your average cycle was 21 days, keep demoting deals even though sales motions now run 35. Trigger logic depends on a job-post keyword that vendors quietly renamed last month. A can’t-fail enrichment service returns 200 OK…with empty payloads.

Pipeline doesn’t die in one place; it drifts across many. You feel it before you can prove it.

Make It Boring

The pivot for us was simple: treat the AI SDR like payroll. Unsexy, scheduled, auditable.

We assigned one owner (not a committee). We wrote runbooks a tired person could follow. We set SLOs for freshness (task age < 24 hours), coverage (X% of ICP in active scoring), and uptime (no silent gaps). We added canary accounts—fake but realistic records that should always trigger. If a canary doesn’t sing, we look before reps notice.

Daily: a 15-minute “greenlight” check—queue size, last successful sync timestamp, canary triggers.
Bi-weekly: a 45-minute audit—sample 20 records end-to-end, confirm field mappings, spot-check decay outcomes.
Monthly: a 90-minute rules review—does the scoring still match win patterns; are thresholds creating the right distribution.
Quarterly: a 2-hour architecture review—vendors, rate limits, schema diffs, failover notes. Then we actually test the incident playbook.

It feels like overkill until it doesn’t.

Capacity Math

Here’s the part everyone avoids putting in writing. The upkeep time isn’t huge, but it is real:

• Steady state (post-rollout): ~3–4 hours/week for one owner to do checks, fixes, and small rule changes.

• Spikes (new motion, vendor change, schema shifts): +2–4 hours that week.

• First 90 days after launch: assume ~0.3 FTE; after stabilization: ~0.15–0.2 FTE.

If you run a team of 10–15 reps, that budget usually lives inside RevOps. At 30+ reps or multiple regions, split the load: RevOps owns platform + dashboards; a Sales Ops lead owns decay rules + trigger hygiene by region. Automation helps, but you still need a person to read the gauges.

What did we see when we finally staffed it like this? Queue freshness dropped from 8.4 hours to 56 minutes within two weeks. “Silent failures” (our catch-all for zero-payload/OK responses) fell by 70% after we added canaries and alerts. Reply-to-task conversion ticked back to baseline, then up 12% as decay rules started reflecting reality instead of nostalgia.

Not glamorous. Very effective. The AI SDR didn’t get smarter. We just stopped letting it drift.

Thanks for reading—if you want the RevOps audit checklist we use for these runs, reply and I’ll share it.