AI in Incident Response: Hype vs. Reality in 2024

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:

1. AI detects the anomaly
2. AI correlates related alerts
3. AI suggests probable root cause
4. AI recommends remediation steps
5. Human approves the action
6. Automation executes

What to Evaluate

When looking at AI ops tools, ask:

1. What data do you need? (If they need 6 months of data to start, that's a red flag)
2. What's the false positive rate? (Anything > 10% will be ignored by your team)
3. Can I see the reasoning? (Black-box AI is useless for incident response)
4. Does it integrate with my existing tools? (If it requires rip-and-replace, walk away)
5. 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