AniSri
All capabilities

The methodology

Turn evidence into a thin slice, then scale what works.

Combine interviews with workflow observation and system data, establish a baseline, deliver a narrow end-to-end improvement, and expand only if the measured constraint improves.

The methodology connects the capability assessments. It defines how evidence becomes a priority, a thin implementation slice, and a decision to scale, revise, or stop.

4
Focus areas
6
Implementation patterns
Assessment structure
Questions, evidence, and implementation patterns

What this capability should change

01

Stakeholder claims are tested against workflow, system, incident, cost, and adoption evidence.

02

The roadmap starts with the constraint and the smallest complete change that can move it.

03

Every phase ends with a measured decision to scale, revise, or stop.

Focus areas

Select an area to open its questions, evidence, target state, and implementation patterns.

Why it matters

Combine stakeholder intent with observed developer journeys, system telemetry, delivery flow, incidents, cost, and risk.

Discovery Questions

  • •Which stakeholder groups must be interviewed (leadership, platform, product, developers, reliability, security, AI, finance)?
  • •What evidence sources will we collect (dashboards, docs, runbooks, org charts, cost reports)?
  • •How will we shadow developer workflows from idea to production?
  • •Which user and developer journeys matter most, and where do they currently fail or wait?
  • •How recent is the incident and on-call data we'll review?
  • •What tooling inventories or architectural diagrams exist today?
  • •What is the current cloud spend and how is it allocated across teams?
  • •Where are AI systems or coding agents already in use, and what data, tools, or production actions can they reach?

Evidence to Collect

  • •Interview notes and transcripts.
  • •Documentation repository audit.
  • •Incident and on-call data exports.
  • •Architecture diagrams and service catalogs.
  • •Workflow recordings or observations.
  • •Cloud cost reports and tagging coverage metrics.
  • •AI tool, model, MCP, data-access, evaluation, and spend inventory.

What good looks like

A versioned baseline connects stakeholder claims to observed journeys, system and delivery telemetry, incidents, ownership, cost, risk, and AI access, while clearly marking assumptions and evidence gaps.

Implementation Patterns

Discovery Playbook

Standardize interviews, agenda, and evidence templates for repeatable engagements.

NotionConfluenceMiro
Steps
  1. Create interview guides for leadership, platform, reliability, security, AI, finance, and product teams.
  2. Set up evidence collection folders with permissions and naming conventions.
  3. Schedule shadowing sessions across platform and product delivery teams.
  4. Document baseline outcomes (flow, reliability, cognitive load, adoption, risk, and unit cost).
  5. Separate observed evidence from stakeholder assumptions and unresolved questions.

Use the method

Greenlight

Run a structured readiness assessment, capture evidence, and leave with prioritized gaps.

Platform playbook

Practical defaults, failure modes, and copyable checks

Browse the full playbook

Filter by layer, search the collection, and copy the useful part from the full playbook.