Readiness Assessment
40-Point Audit · EVO3 2026
AI Readiness Audit Checklist
A structured 40-point assessment to determine your organization's readiness for agentic AI deployment — and identify the gaps before they become production failures.
Sierra Napier-Leach, MPA · EVO3
Estimated time: 30–60 minutes
Scoring guide: Complete all 40 items. Count your checkmarks per domain (8 items each). 7–8 = Strong. 5–6 = Developing. 3–4 = Gap. 0–2 = Not Ready. Total score out of 40: 35+ = Ready for agentic deployment. 24–34 = Partial readiness — targeted work needed. Below 24 = Foundation work required first.
Domain 1: Data Infrastructure
Can your data support reliable AI reasoning and action?
Your core operational data is centralized in a queryable system (database, warehouse, or API layer) — not siloed in spreadsheets or disparate tools.
You have a defined data schema with consistent field names, types, and validation rules that agents can rely on without transformation.
Data quality is monitored — you know your approximate null rate, duplicate rate, and stale record percentage for key datasets.
You can trace the lineage of a data record — where it came from, when it was created, who touched it last.
Your data is accessible via API or SDK — not only through a manual BI tool or spreadsheet export.
You have a strategy (even informal) for handling PII, PHI, or other sensitive data in automated pipelines.
You have data backup and recovery procedures in place that would protect you if an agent wrote incorrect data at scale.
You have at least one person in the organization who can write a SQL query or equivalent data retrieval — someone to validate agent data reads.
Domain 2: Process Clarity
Are your workflows documented well enough for an agent to follow?
The specific workflow you want to automate is documented in writing — steps, decision points, inputs, outputs, and exception handling.
You can identify where the current workflow produces the most inconsistency or delay — the specific bottlenecks an agent would address.
You know the difference between decisions that require judgment and tasks that follow a consistent rule — and have labeled each step accordingly.
There are clear success and failure states for each process step — you will know when the agent did the right thing and when it didn't.
You have identified which process exceptions and edge cases are most common — and have a plan for how agents should handle them.
The people who currently run this process have been consulted and are aligned on what AI automation would and would not do.
You can measure the current process with at least two quantitative metrics (e.g., time, error rate, volume) to establish a baseline for improvement.
The process is stable enough that you are not planning major changes to it within the next 90 days — automating a process in flux multiplies risk.
Domain 3: Governance & Oversight Readiness
Are you organizationally prepared to oversee AI systems responsibly?
There is a named individual responsible for AI governance in your organization — even if it's a part-time or shared role.
You have defined which AI actions require human approval (synchronous HITL) and which can proceed with async notification.
You have an escalation path for when an agent produces an unexpected or harmful output — who to notify, what to do first.
Leadership is aligned that AI systems require ongoing human oversight — not a set-and-forget deployment.
You have a way to pause or halt the AI system immediately without requiring a developer or a code deployment.
You are prepared to capture and review audit logs of AI actions — both for internal quality control and external compliance purposes.
Affected staff understand that AI automation will change (not eliminate) their roles — and have been given a realistic picture of what that means.
You have considered the regulatory or compliance implications of automated AI actions in your specific industry or context.
Domain 4: Technical Infrastructure
Does your technical environment support agentic deployment?
You have a production-grade hosting environment (cloud or on-premises) capable of running long-lived API processes — not just static websites.
You have environment variable management in place — secrets are not stored in code repositories.
Your existing tools have APIs or webhooks that agents can integrate with — you don't rely exclusively on GUI-only software.
You have a mechanism for monitoring service uptime and receiving alerts when systems are down.
You have at least one developer or technical partner who can maintain and modify agent code in production.
Your network and security posture allows outbound API calls to AI model providers (OpenAI, Anthropic, etc.) without firewall conflicts.
You have a staging or testing environment where agent changes can be validated before being released to production.
You have version control in place for your codebase — no "single laptop" single points of failure for your AI logic.
Domain 5: Organizational Readiness
Is your organization culturally and strategically ready?
Leadership has articulated a clear business problem they want AI to solve — not just an interest in "doing AI."
There is budget and timeline commitment for a genuine pilot — not a one-week experiment that will be abandoned under the first obstacle.
You have identified a pilot workflow that is high-value but not mission-critical — allowing real learning without catastrophic risk.
There is a culture of iteration in your organization — people expect the first version to be imperfect and are willing to refine it.
You have considered and addressed the job impact question — impacted employees are not finding out about AI automation through the grapevine.
There is a named executive sponsor who can remove organizational blockers if the AI implementation runs into resistance.
You have thought about AI ethics and fairness in the context of your specific use case — not just generically.
You are prepared to measure and report the results of your AI deployment — both successes and failures — with genuine transparency.
Ready to interpret your scores?
We offer AI readiness audits as a structured engagement — typically a one-week sprint producing a prioritized gap analysis and 90-day roadmap.