AI native: process, lucidity and team delivery
AI-native team: conventions before tools, lead time/incident/review KPIs (not commits), enablement, shadow IT, lucidity. Delivery culture under AI acceleration.
AI native: useful definition
An AI-native team industrializes generate → test → review → merge loops with written conventions, shared tools and business-linked metrics. It is not everyone prompting harder.
AI changes delivery pace, not magically quality. Without conventions it accelerates chaos: monster PRs, fuzzy ownership, silent debt, divergent styles.
Skills shift: more review, architecture, specification and guardrails; less time on boilerplate. Seniors do not vanish. Their time moves.
Conventions before tools
Write how we ask the AI (short spec template), what we merge, what we reject, which paths need senior review (auth, payments, migrations, PII).
Standardize the environment: Claude Code guide or equivalent, shared CLAUDE.md (or repo instructions), secrets policy, shared definition of done.
A 2 to 4 person pilot on one repo beats mass rollout on day one. The pilot produces conventions; the rest of the team adopts via workshop.
Internal communication becomes a bottleneck: handoffs, async specs, acceptance criteria. AI does not replace a clear backlog. It makes it more urgent.
Metrics that matter
Useful (DORA-like family + lucidity): lead time, deploy frequency, change fail rate, MTTR, review time, post-merge incidents, critical-path coverage.
Dangerous as sole KPI: commits, lines generated, “% AI code”. You optimize noise and encourage merge without understanding.
More commits does not mean more value. We look at incidents and review time.
Add a lucidity metric: ownership rotation, time for an external dev to understand a module. If nobody understands generated code, it is a liability.
Shadow IT and enablement
Banning AI drives shadow IT. Mandating AI without training drives unread paste. Offer an official path (tools, conventions, guardrails) easier than workarounds.
1-day workshop, max 8 people, real repo: reflexes (spec, tests, review, secrets, compact/clear if Claude Code), not a feature tour. Deliverable: conventions + merge checklist + anti-patterns.
90-day roadmap
Days 1-30: pilot, conventions v0, tool workshop, HITL on critical paths, base metrics.
Days 31-60: second team, AI review comments-only, versioned skills/prompts, first assisted CI job.
Days 61-90: industrialize what works, kill debt, train validators, link eng and business KPIs.
If you want to structure the AI-native shift, we can scope in 20-40 minutes.
FAQ
- Does AI replace seniors?
- No. More review, architecture and guardrails. Less boilerplate. Responsibility stays human.
- Which tool first?
- Team loop first, then one standardized tool. Convention beats tool debate.
- Need an AI champion?
- Yes temporarily (pilot pair that documents). No as a permanent bottleneck.
- Exec metrics?
- Lead time, incidents, time saved on a named business flow, adoption, mitigated risks. Not commit theater.
- Link to AI code review?
- Culture is here; the review tool is in the AI code review article.
- 2-person team?
- Yes. Conventions prevent AI from becoming two-person debt.
Scope your first AI agent
20 minutes to review your tools, data and the first useful case. No jargon, no commitment.