What is AI Runbook?

What is an AI Runbook?

An AI Runbook is the operational playbook for your AI systems. It documents the step-by-step procedures that anyone responsible for operating, monitoring, or troubleshooting an AI system needs to follow. Think of it as the equivalent of an operations manual for a physical plant: it tells operators what to check, when to check it, what normal looks like, what abnormal looks like, and exactly what to do when something goes wrong.

The concept of runbooks comes from IT operations, where they have long been used to standardise system management. AI Runbooks adapt this concept for the unique characteristics of AI systems, including model-specific monitoring requirements, data quality dependencies, retraining procedures, and the particular ways AI systems can fail.

Why AI Runbooks Are Essential

Reducing Key-Person Dependency

In many organisations, AI system knowledge lives in the heads of one or two people, often the data scientist who built the model. When that person is unavailable, on holiday, ill, or has left the company, the organisation is left unable to manage or troubleshoot its own AI systems. A runbook captures this knowledge in a form that any qualified team member can follow.

Ensuring Consistent Operations

Without documented procedures, different team members handle the same situations differently. One person might restart an AI service when it shows high latency, while another might investigate the root cause first. A runbook ensures consistent, proven responses regardless of who is on duty.

Enabling Faster Incident Response

When an AI system fails or produces bad outputs, response time matters. Every minute spent figuring out what to check, who to contact, or how to roll back is a minute of business impact. A runbook provides immediate, actionable guidance that eliminates the diagnosis-from-scratch approach that wastes critical time during incidents.

Supporting Compliance and Audit

Regulators and auditors increasingly expect documented operational procedures for AI systems, particularly in regulated industries like financial services and healthcare. An AI Runbook demonstrates that your organisation has structured, responsible approaches to AI system management.

Key Sections of an AI Runbook

1. System Overview

Provide a clear, non-technical description of what the AI system does, who uses it, and why it matters to the business:

• System name and purpose
• Business processes it supports
• Key stakeholders and users
• Dependencies on other systems and data sources
• Expected usage patterns including peak periods

2. Architecture and Components

Document the technical components and how they connect:

• Infrastructure components such as servers, cloud services, and databases
• Data pipeline architecture showing the flow from source to model
• Model serving infrastructure
• Integration points with other business systems
• Network and security configurations

3. Monitoring and Health Checks

Define what to monitor and what constitutes normal versus abnormal:

• Key metrics and dashboards: Where to find them and what each metric means
• Normal operating ranges: Expected values for response time, throughput, error rates, and resource utilisation
• Alert thresholds: Levels that trigger warnings versus critical alerts
• Model performance metrics: Accuracy, drift indicators, and output quality measures with expected baselines
• Data quality indicators: Pipeline health, data freshness, and quality scores

4. Routine Operations

Document standard operational procedures:

• Daily health checks: What to review each day and what to look for
• Scheduled maintenance: Regular tasks like log rotation, backup verification, and certificate renewal
• Retraining procedures: When and how to trigger model retraining, including data preparation and validation steps
• Deployment procedures: Step-by-step instructions for deploying model updates
• Scaling procedures: How to scale resources up or down based on demand

5. Incident Response Procedures

Provide clear guidance for common failure scenarios:

• Incident classification: How to determine severity levels based on business impact
• Escalation paths: Who to contact for each severity level and how to reach them
• Troubleshooting guides: Step-by-step diagnostic procedures for common issues
• Rollback procedures: How to revert to the previous model version or system state
• Communication templates: Pre-drafted messages for notifying stakeholders during incidents

Common scenarios to document include:

• Model producing incorrect or degraded outputs
• Data pipeline failure or data quality issues
• System performance degradation or outage
• Unexpected spikes in traffic or resource usage
• Security incidents affecting the AI system

6. Recovery and Post-Incident

Document what happens after an incident is resolved:

• Verification procedures: How to confirm the system is fully recovered
• Post-incident review process: Template for documenting what happened, why, and how to prevent recurrence
• Stakeholder communication: How and when to provide all-clear notifications

7. Contact Information

Maintain a current directory of:

• On-call team members and rotation schedule
• Subject matter experts for specific components
• Vendor support contacts and account details
• Internal stakeholder contacts who need to be informed during incidents

Creating an Effective AI Runbook

Involve Operators, Not Just Builders

The people who build AI systems and the people who operate them often have different perspectives. Involve both in creating the runbook. The builders know how the system works; the operators know what questions arise in daily management and what information they need during incidents.

Write for Clarity Under Pressure

Runbook procedures will often be used during stressful incidents when people need to act quickly. Write in clear, numbered steps. Avoid jargon. Use screenshots and diagrams. Make procedures unambiguous, as someone reading at 2 AM during an outage should not have to interpret what a step means.

Keep It Updated

A runbook that does not reflect the current system is worse than useless because it provides false confidence. Establish a review cycle, typically quarterly, and update the runbook whenever the system changes. Make runbook updates a required part of any deployment or system change process.

Test Your Runbook

Regularly practice key procedures, especially incident response, to verify they work and identify gaps. Tabletop exercises where the team walks through a simulated incident using the runbook are an effective way to test without risking production systems.

AI Runbooks for Southeast Asian Operations

• Multilingual documentation: If your operations teams include members who are more comfortable in local languages, consider providing critical sections in both English and the local language to ensure clarity during high-pressure situations.
• Distributed team coordination: For businesses with teams across multiple ASEAN time zones, the runbook should clearly document hand-off procedures, on-call rotations that account for time zones, and escalation paths that work across geographies.
• Vendor-specific procedures: If you use regional cloud providers or locally hosted infrastructure in specific markets, include vendor-specific troubleshooting and support contact information for each environment.
• Regulatory incident requirements: Different ASEAN countries may have specific incident reporting requirements, particularly for AI systems handling personal data. Include country-specific regulatory notification procedures in the incident response section.

Common Runbook Mistakes

• Too much detail: A 200-page runbook that nobody reads is useless. Focus on actionable procedures, not exhaustive documentation
• Too little detail: Vague instructions like "investigate the issue" are not helpful during an incident. Be specific.
• Never updated: Systems change frequently but runbooks are often written once and forgotten. Build updates into your change management process.
• Not tested: A runbook that has never been practiced in a drill may contain errors or gaps that only become apparent during a real incident
• Stored in an inaccessible location: The runbook must be immediately accessible to anyone who needs it, not buried in a document management system that requires special access