The AI Implementation Checklist: 40 Things to Do Before You Launch

Data & Quality (Items 1–10)

• Training/test data has been audited for quality and bias
• Data consent and privacy compliance verified (DPDPA, GDPR if applicable)
• Data pipeline from source to AI system is automated and monitored
• Baseline performance metrics established before AI deployment
• Edge cases and failure scenarios documented and tested
• Data version control in place
• PII (personally identifiable information) identified and handled per policy
• Data refresh schedule defined for time-sensitive use cases
• Vendor data-sharing agreement reviewed and signed
• Data lineage documented (where does each data point come from?)

Model & System (Items 11–20)

• Model evaluated on representative test set (minimum 100 examples)
• Accuracy, precision, recall (or task-specific metrics) meet defined threshold
• Latency tested at expected peak load
• Cost per transaction modelled at 3x expected volume
• Fallback/error handling implemented (what happens when AI fails?)
• Model versioning and rollback procedure documented
• System tested for adversarial inputs (prompt injection, jailbreaking)
• Rate limiting and abuse prevention in place for user-facing APIs
• Logging of all AI inputs and outputs enabled
• Monitoring dashboard set up with key metrics

Governance & Compliance (Items 21–30)

• Use case approved by designated AI owner
• Legal review completed for regulated domains (finance, health, legal)
• Acceptable use policy updated to include new AI system
• Bias and fairness assessment completed
• Explainability requirements assessed — can decisions be explained?
• Incident response plan documented
• Human escalation path defined for AI errors
• Regular review schedule established (monthly first 3 months, then quarterly)
• Vendor SLA reviewed for uptime, data security, and support
• Audit trail mechanism in place for regulated decisions

Launch & Adoption (Items 31–40)

• Affected team trained on new AI-assisted workflow
• FAQ document prepared for employee questions
• Soft launch with limited user group before full rollout
• Feedback mechanism for users to flag AI errors
• Success metrics and review cadence communicated to stakeholders
• Rollback plan if significant issues emerge post-launch
• Communication plan for customers if AI is customer-facing
• 90-day review meeting scheduled at launch
• Budget approved for iteration and improvement post-launch
• Named owner responsible for ongoing performance accountable

Get Launch-Ready

Our workshop covers the full AI implementation process — from use case selection to post-launch monitoring.