What AI readiness means
AI readiness is not a tool purchase. It is a decision framework: knowing what work AI can plausibly accelerate, what data it can touch, who reviews its output, and how you measure whether it actually helped.
A ready company can answer four questions in plain language: which workflows AI is allowed in, which data it is allowed to see, who signs off on its output, and how success is measured.
What to audit first
- Decision workflows that already have a clear input, output, and reviewer
- Repetitive content, summarization, translation, and triage tasks
- Customer-facing surfaces where errors must be caught before they ship
- Internal knowledge that is scattered across documents, threads, and tools
Data and privacy boundaries
Decide in writing what should never be sent to third-party AI tools: customer PII, regulated records, source code under restrictive licenses, unreleased financials, security material.
Tie those rules to the tools your team actually uses, not abstract policy documents.
Workflow opportunities
- Drafting + human edit cycles (briefs, memos, replies)
- Structured extraction (forms, invoices, transcripts)
- Search and synthesis over your own knowledge
- Routing and triage in support and ops
Human review gates
Every AI-assisted workflow that affects a customer, a payment, a contract, or a public claim needs a named human reviewer and a written approval step. Without that, AI mistakes ship as your mistakes.
Analytics and success measures
Track time saved, reviewer corrections, downstream rework, and customer outcomes — not raw token counts. If you cannot tell whether AI helped, you do not have AI readiness yet.
Where companies get AI wrong
- Buying tools before deciding workflows
- Letting AI write things no human reviews
- Sending sensitive data to consumer-grade endpoints
- Measuring activity instead of outcomes