Readiness Signals

Three things: (1) business processes that someone can describe (they don't need to be documented, just describable), (2) decision logic that a human currently executes (pricing, approvals, routing, triage), and (3) organizational willingness to put AI in production, not just run another pilot. You don't need perfect data, an AI team, or a transformation roadmap.

No. 'Clean your data first' is the number one stall tactic in the industry. You need accessible data, not perfect data. AI agents can handle messy, incomplete data, especially when they have structured context via Business-as-Code. Start with what you have and improve iteratively.

If you can answer yes to two of these three questions, you're ready: (1) Can someone on your team describe how a key business process works? (2) Do you have repetitive tasks that require judgment, not just rules? (3) Are you willing to put AI in production within 30 days, not 12 months? Most companies clear this bar, the gap is methodology, not readiness.

Fear of imperfection. Companies stall because they think they need perfect data, a complete AI strategy, and executive buy-in across the org. They don't. They need a structured methodology (Business-as-Code), an embed partner who knows how to operate AI in production, and the willingness to start with one process and expand from there.

Less than you'd think. Mid-market companies (100-1,000 employees) are often more AI-ready than enterprises because they have less bureaucracy, faster decision cycles, and more describable processes. The sweet spot is companies with $20M-$500M revenue where operational efficiency directly impacts profitability.