How to Use This Checklist

This checklist guides you through the entire AI automation implementation process, from initial assessment to ongoing optimization. Use it as a project management tool to ensure nothing is missed.

Phase 1: Assessment & Planning

1.1 Current State Analysis

•  List all repetitive tasks performed by your team
•  Document time spent on each task (hours/week)
•  Identify tasks with high error rates
•  Map data flow between systems
•  Note current tools and software in use
•  Calculate fully-loaded labor costs for manual work

1.2 Opportunity Identification

•  Score each task on automation potential (1-5)
•  Estimate ROI for top candidates
•  Identify quick wins (high impact, low complexity)
•  Consider dependencies between processes
•  Assess data quality and availability
•  Evaluate integration requirements

1.3 Stakeholder Alignment

•  Present business case to decision makers
•  Get budget approval for pilot project
•  Identify project sponsor/champion
•  Communicate vision to affected team members
•  Address concerns about job displacement
•  Set expectations for timeline and results

Phase 2: Solution Design

2.1 Process Documentation

•  Create detailed process flowcharts
•  Document all decision points and business rules
•  Identify exceptions and edge cases
•  Note approval requirements and checkpoints
•  Define success criteria and KPIs
•  Document current pain points to address

2.2 Tool Selection

•  Research automation platforms
•  Verify integration capabilities with existing tools
•  Check security and compliance requirements
•  Compare pricing models (per-user, per-task, flat)
•  Request demos and trials
•  Check vendor reputation and support quality

2.3 Architecture Planning

•  Design data flow between systems
•  Plan authentication and access controls
•  Define error handling procedures
•  Create logging and audit requirements
•  Plan for scalability
•  Document rollback procedures

Phase 3: Build & Configure

3.1 Environment Setup

•  Set up development/test environment
•  Configure necessary API connections
•  Set up authentication and credentials (secure storage)
•  Create test accounts and sample data
•  Establish version control for configurations
•  Set up monitoring and alerting

3.2 Build Automation

•  Create workflows for each process step
•  Configure triggers and schedules
•  Build error handling and retry logic
•  Implement logging and audit trails
•  Create notification rules
•  Set up approval workflows where needed

3.3 Integration Testing

•  Test each component individually
•  Test end-to-end workflows
•  Verify data accuracy at each step
•  Test error scenarios and edge cases
•  Validate security controls
•  Performance test under expected load

Phase 4: Pilot & Validation

4.1 Pilot Planning

•  Select pilot scope (subset of data/users)
•  Define pilot duration (typically 2-4 weeks)
•  Identify pilot participants
•  Create feedback collection mechanism
•  Define success metrics
•  Plan parallel running with manual process

4.2 Pilot Execution

•  Launch pilot with selected group
•  Monitor closely for first 48-72 hours
•  Collect daily feedback from participants
•  Track all errors and exceptions
•  Compare automated vs. manual results
•  Document any required adjustments

4.3 Pilot Review

•  Analyze pilot performance data
•  Review all errors and their causes
•  Gather participant feedback and suggestions
•  Calculate actual vs. projected time savings
•  Identify needed improvements
•  Make go/no-go decision for full rollout

Phase 5: Rollout

5.1 Pre-Launch Preparation

•  Apply learnings from pilot
•  Update documentation with final processes
•  Create user training materials
•  Prepare FAQ and troubleshooting guide
•  Set up support channel for questions
•  Schedule launch communication

5.2 Training

•  Train all affected team members
•  Conduct hands-on practice sessions
•  Provide written reference guides
•  Identify power users for peer support
•  Train on exception handling procedures
•  Communicate escalation path

5.3 Go-Live

•  Announce launch to organization
•  Activate automation in production
•  Monitor system performance
•  Be available for immediate support
•  Track adoption and usage
•  Celebrate quick wins

Phase 6: Optimize & Scale

6.1 Performance Monitoring

•  Set up regular performance reporting
•  Track KPIs vs. baseline and targets
•  Monitor error rates and types
•  Collect ongoing user feedback
•  Review system logs for issues
•  Track time and cost savings

6.2 Continuous Improvement

•  Analyze performance data monthly
•  Identify optimization opportunities
•  Implement incremental improvements
•  Update training as processes evolve
•  Share success stories internally
•  Document lessons learned

6.3 Scaling

•  Identify next processes to automate
•  Apply learnings to new implementations
•  Build internal automation capabilities
•  Create automation governance framework
•  Plan for system maintenance and updates
•  Evaluate ROI and adjust strategy

Common Pitfalls to Avoid

Quick Reference: Decision Framework

Use this framework to evaluate automation candidates:

Score 20+: High priority. Score 15-19: Good candidate. Score below 15: Consider alternatives.