AI Automation Readiness Checklist
Before investing in AI-powered automation, assess whether your business has the foundation for success. Use this 15-point checklist to identify gaps and prioritize next steps.
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How to Use This Checklist
For each item, mark whether you have it in place. At the end, count your checkmarks to determine your readiness level.
Ready to implement AI automation now
Close—address gaps first, then pilot
Build foundation before automating
1Data & Infrastructure Readiness
You have structured data in accessible systems
Your lead data, customer records, or operational data exists in databases/CRMs (not just spreadsheets or email threads).
Why it matters: AI automation needs clean, queryable data. If everything is in Gmail or scattered spreadsheets, you'll spend 80% of time on data prep.
Your data quality is “good enough” (≥80% accuracy)
Most records are complete, duplicates are minimal, and formats are consistent.
Red flag: If you can't trust your CRM contact data or analytics numbers, AI will amplify those errors.
You have API access to your core tools (CRM, email, analytics)
Your key systems (Salesforce, HubSpot, Stripe, etc.) offer APIs or native integrations.
Why it matters: Automation relies on API connectivity. If your tools don't integrate, you're stuck with manual copy-paste.
You can afford $300-1,000/month for automation tools + AI APIs
Budget allocated for n8n/Zapier, OpenAI/Anthropic API usage, and vector database hosting.
Reality check: Pilot costs: ~$300-500/month. Production at scale: $500-1,500/month depending on volume.
2Process Clarity & Documentation
Your target workflow is documented (even if informal)
Someone can describe the step-by-step process you want to automate (e.g., “Lead submits form → Qualify → Route to sales”).
Why it matters: You can't automate what you can't explain. Start with a simple flowchart.
The workflow is rule-based or pattern-based (not purely creative)
Decisions follow logic: “If lead score ≥ 8, assign to enterprise team.” Not: “Use your judgment and intuition.”
Good candidates: Lead routing, email triage, data entry, report generation. Bad candidates: Artistic direction, strategic planning.
The workflow is repetitive (happens ≥10 times/week)
High volume justifies automation investment. Automating a once-per-month task rarely pays off.
ROI threshold: If automation saves 30 minutes per occurrence and runs 10×/week → 5 hours/week saved = justifiable.
3Team Buy-In & Technical Readiness
Stakeholders support automation (not fearful of being replaced)
Your team sees automation as removing drudgery, not eliminating jobs. Leadership is aligned.
Resistance signals: “We like doing it manually,” “What if the AI makes mistakes?” → Address with pilot mindset.
Someone on your team can validate outputs (quality control)
You have a subject matter expert who can review AI decisions (at least during the pilot phase).
Example: Automating lead scoring? Your sales team should review classifications for the first 100 leads.
You have (or can hire) technical implementation support
Either internal dev/ops, a technical founder, or budget to hire a consultant for implementation.
DIY threshold: No-code tools (n8n, Zapier) can be learned by non-technical founders. Custom RAG systems need a developer.
4Risk Management & Compliance
You understand what data the AI will access and why
Clear on what customer/business data goes to LLM APIs (OpenAI, Anthropic) and your data handling policies.
Compliance note: GDPR/HIPAA require explicit consent and data processing agreements with AI providers.
You have a rollback plan if automation breaks
Your business can temporarily revert to manual processes if the automated workflow fails or needs debugging.
Best practice: Keep manual process documented for first 3 months of automation.
Mistakes are recoverable (not catastrophic)
If the AI misroutes a lead or misclassifies a support ticket, it's fixable within minutes/hours—not a business-ending error.
High-risk workflows: Financial transactions, medical decisions, legal advice—start with human-in-the-loop approval.
5Measurement & Success Criteria
You know your baseline metrics (time spent, error rate, cost)
Can answer: “How long does this take manually?” “What's our current accuracy?” “What does it cost in labor?”
Example: “Lead qualification takes 10 min/lead, 50 leads/week = 8.3 hours/week = $400/week in TA time.”
You have clear success criteria (what “good” looks like)
Defined goals like: “Reduce lead response time from 2 hours to 15 minutes” or “Automate 80% of support ticket routing.”
Avoid vague goals: “Make things faster” → Too ambiguous. “Save 5 hours/week” → Measurable.
You're willing to iterate (version 1 won't be perfect)
Expectation: Launch at 70% accuracy, monitor feedback, refine over 4-8 weeks to reach 90%+.
Mindset shift: Treat automation as a product—ship, measure, improve.
How Did You Score?
Book a strategy call to discuss implementation. We'll map out a 4-6 week pilot plan.
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