What Should I Automate Before AI?

Automate the basics first. Greene Solutions sees a clear pattern: companies that master foundational automation before implementing AI have 3x higher success rates. Here's your pre-AI automation checklist.

1. Document Your Processes

Before automating anything, document how work actually happens:

• Current state mapping: Write down each step in your critical processes
• Identify variations: Note when processes branch or have exceptions
• Measure timing: How long does each step take currently?
• Document data flow: What information moves where?

AI cannot automate what you cannot describe. Companies with documented processes see 40% faster AI implementations.

2. Clean and Standardize Your Data

AI needs clean data. Automate these data prep steps:

• Duplicate removal: Merge duplicate customer records, products, and vendors
• Format standardization: Consistent dates, phone numbers, addresses
• Missing data handling: Fill gaps or flag incomplete records
• Data validation rules: Prevent bad data from entering your systems

67% of AI project delays stem from data quality issues. Fix this first.

3. Automate Simple Rule-Based Workflows

Start with simple "if this, then that" automations:

• Email routing: Auto-sort and assign incoming messages
• Form notifications: Alert the right person when forms are submitted
• Status updates: Automatically update project or task statuses
• Basic follow-ups: Send reminder emails based on triggers

These build automation muscle without complexity. Your team learns to trust automated systems.

4. Connect Your Systems

AI works best when data flows freely. Automate integrations:

• CRM to email: Sync customer data with communication tools
• Calendar integration: Connect scheduling with project management
• Payment to accounting: Auto-import transaction data
• Form to database: Capture leads directly into your systems

Disconnected systems force manual data entry. That manual work disappears when systems talk to each other.

5. Build Basic Analytics Dashboards

You cannot improve what you do not measure. Automate reporting:

• Key metric tracking: Daily/weekly snapshots of critical numbers
• Performance alerts: Notify when metrics exceed thresholds
• Automated reports: Scheduled summaries delivered to stakeholders
• Data visualization: Make trends and patterns visible

These dashboards become the baseline for measuring AI's impact later.

The Automation Readiness Scale

Timeline: Pre-AI Automation

Typical timeline for most businesses:

• Weeks 1-2: Document top 5 processes
• Weeks 3-4: Clean critical data
• Weeks 5-6: Implement 3-5 simple workflows
• Weeks 7-8: Connect 2-3 key systems
• Weeks 9-10: Build basic dashboards
• Week 11+: Begin AI implementation