Every Vendor Claims AI Magic
Open any monitoring vendor's website and you'll see: "AI-powered incident detection!" "ML-driven root cause analysis!" "Intelligent alerting!"
After evaluating a dozen AI ops tools and running three in production, here's what actually works and what's snake oil.
What Works: Anomaly Detection
ML-based anomaly detection genuinely helps with metrics that have predictable patterns:
Good candidates for ML anomaly detection:
- Request rate (daily/weekly seasonality)
- CPU usage (follows traffic patterns)
- Database connections (predictable daily cycles)
- Error counts (should be near-zero baseline)
Bad candidates:
- Deployment metrics (irregular by nature)
- Batch job durations (vary by data volume)
- Cache hit rates (depends on traffic mix)
- Anything with frequent step changes
The key is training on enough data. You need at least 2-3 weeks of data for daily patterns, and 6-8 weeks for weekly seasonality.
What Works: Alert Correlation
This is where AI delivers real value. When 15 alerts fire simultaneously, AI can group them and identify the probable root cause:
Raw alerts (what the human sees):
01:15:03 CRITICAL api-server p99 latency > 2s
01:15:07 WARNING postgres connection pool 90%
01:15:12 CRITICAL checkout error rate > 5%
01:15:15 WARNING redis response time > 100ms
01:15:18 CRITICAL payment-service timeout
01:15:22 WARNING cart-service p99 > 1s
AI-correlated (what the human should see):
01:15:03 INCIDENT Database connection pool exhaustion
Impact: checkout, payment, cart services degraded
Probable cause: postgres connection pool at 90%
Related alerts: 6 (grouped)
Suggested action: Check for connection leaks, consider pool size increase
This is the difference between a 45-minute investigation and a 5-minute fix.
What Doesn't Work (Yet): Autonomous Remediation
Vendors love to demo "AI automatically fixed the issue!" In reality:
- Auto-scaling works great (but that's not really AI)
- Auto-rollback works great (also not really AI)
- Actual autonomous root cause analysis and fix? Not reliable enough for production.
I tested three autonomous remediation products. Results:
Correct diagnosis: 72%
Correct remediation: 45%
Made things worse: 8%
Did nothing useful: 47%
A 45% success rate isn't good enough for production systems. But it IS good enough for suggesting actions to a human.
The AI-Assisted Sweet Spot
Human only: AI suggests + Human decides: AI autonomous:
─────────── ──────────────────────────── ──────────────
Slow, error-prone Fast, accurate Fast, risky
at 3am at 3am at 3am
← THIS IS WHERE WE SHOULD BE
The best approach today:
- AI detects the anomaly
- AI correlates related alerts
- AI suggests probable root cause
- AI recommends remediation steps
- Human approves the action
- Automation executes
What to Evaluate
When looking at AI ops tools, ask:
- What data do you need? (If they need 6 months of data to start, that's a red flag)
- What's the false positive rate? (Anything > 10% will be ignored by your team)
- Can I see the reasoning? (Black-box AI is useless for incident response)
- Does it integrate with my existing tools? (If it requires rip-and-replace, walk away)
- What happens when the AI is wrong? (Good tools show confidence scores)
My Prediction
In 2-3 years, AI will handle 80% of incidents autonomously. The remaining 20% — novel failures, complex cascading issues — will still need human judgment. But that's fine. Those are the interesting problems.
If you want to see how AI-assisted incident response actually works in practice, check out what we're building at Nova AI Ops.
Written by Dr. Samson Tanimawo
BSc · MSc · MBA · PhD
Founder & CEO, Nova AI Ops. https://novaaiops.com
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