What does AI readiness actually mean for a dev team?

AI readiness isn't whether you've bought the tools. It's three things: your codebase and docs are navigable for agents, your guardrails (review, scoped permissions, observability) are strong enough that seniors trust agents on real work, and your developers can operate fleets instead of fearing them. A team can have every seat and be unready, and a messy stack can be closer than it looks. Readiness is the system around the model, not the model.

0106not a tool checklist

Not a tool checklist

Owning Claude Code or Copilot seats is the floor, not readiness. Readiness is whether agents can do real work in your environment without your seniors routing around them.

0206the three things that make a team ready

The three things that make a team ready

A codebase agents can navigate (durable context), guardrails seniors trust (review gates, scoped permissions, observability), and developers trained to operate — set goals, review at altitude, intervene. Missing any one caps the value.

0306measure it honestly

Measure it honestly

The useful question isn't 'do we use AI' but 'can our devs run agents in production on our standards, today.' A short, honest assessment beats a vendor maturity score.

0406common questions

Straight answers.

Do we need to fix our codebase first?: Not perfectly. You need a navigable source of truth agents can use, not a rewrite. The context layer is usually the highest-impact fix.
Is readiness about the team or the tech?: Both, but the team is the harder half. Developers who can operate fleets matter more than any tool, and that capability is taught, not licensed.
How do we assess readiness?: A scoped assessment of your env, guardrails, and how your devs actually use AI today — honest gaps, not a maturity badge. That's the entry door we offer.