Open · Vendor-neutral · Community-maintained · CC BY-SA 4.0

The open framework for adopting AI across software delivery.

ScaledAIOps restructures the SDLC, CI/CD and DevOps practices you already run so AI can take part safely, measurably and at scale, from business concept to production and retirement. Rigorous enough for a bank, practical enough for a startup.

AI is already in your delivery process

The question is whether it is governed, measured and owned.

Adoption

Tools arrive faster than rules

Assistants and agents spread team by team. Few organisations can say which tools touch which code, data or systems.

Accountability

Ownership gets blurred

When AI drafts the change, the spec or the incident summary, someone still has to own the outcome.

Evidence

Value is asserted, not measured

Output volume rises. Whether cycle time, quality and cost improve is rarely shown.

From business concept to retirement

AI takes part in every stage you already run. The stages stay; the controls change.

  1. Conceivebusiness case
  2. Planbacklog
  3. Specifyspecs, ADRs
  4. Generatecode, IaC
  5. Verifyreview, evals
  6. Releasetiered gates
  7. Operaterun, respond
  8. Learnadjust
  9. Retiredecommission

Every element along the way, from business case to agent credential, has an owner, a risk tier, provenance and a planned retirement. See the lifecycle →

The framework at a glance

Six disciplines, held together by four control pillars, guided by eight principles and carried by eight roles.

Disciplines

What an organisation must be good at

AI-Augmented Delivery

AI in every lifecycle stage, with an AI-aware pipeline.

Human–AI Workflow Design

Delegation, approval gates, handoffs and escalation.

Tooling & Context Engineering

Approved tools, integrations and context as versioned assets.

Governance & Guardrails

The four control pillars applied to AI use.

Skills & Roles

Competency shift, redefined roles, enablement.

Value & Measurement

Cycle time, rework, quality and cost. Never vanity metrics.

Delegate by risk, not by convenience

Four tiers decide how much AI may do and which approvals a change needs. Same model for a startup and a bank, at different settings.

TierTypical workGate
LowDocs drafts, test scaffolding, reversible internal changesAuto-approve after automated checks
MediumProduction code and configuration with standard rollbackHuman review
HighSecurity, customer data, financial logic, wide blast radiusTwo reviewers incl. a specialist
CriticalIrreversible or regulatory-significant actions, decisions about peopleHuman only; AI may inform, never decide

How tiers are assigned →

Who it is for

Engineering leaders

Adopt without a parallel process

Change the SDLC, pipelines and team roles you already run, one reversible step at a time.

Risk & compliance

Assure it like any other control

Risk tiers, an audit trail from existing systems of record, and mappings to the EU AI Act and ISO/IEC 42001.

Platform teams

Build the paved road

Approved tools, governed integrations, non-human identities and an AI-aware pipeline that enforces the rules.

Built by practitioners, in the open

No vendor lock-in. Every page is a Git-tracked, CC BY-SA document, and AI-assisted edits are reviewed before they merge. Fix a sentence or propose a practice.

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