Module 4 · Measurement, Delivery Pipelines & Change Enablement

Setup & Inputs

Gather necessary artefacts

Module 3 prototypes and logs.
Stakeholder feedback notes.
Baseline KPIs for the current workflow (cycle time, error rates, satisfaction scores, cost metrics).
Existing organisational change frameworks or communication templates.

Environment preparation

Set up data warehouse or local SQLite/Postgres instance for telemetry.
Install dbt-core or ensure data transformation pipeline capability.
Configure GitHub Actions or GitLab CI runners for automation pipelines.
Install n8n locally (npx n8n) or use hosted version for workflow orchestration.

Stakeholder map refresh

Update your stakeholder matrix with owners for KPIs, operations, change management, and compliance. Identify champions and skeptics to address in communication plans.

Module 4 connects metrics, telemetry, automation, experimentation, and change enablement into one delivery engine.

Learning Outcomes

Translate business outcomes into KPI trees and telemetry plans for AI-enabled workflows.
Design automated pipelines for requirements, testing, deployment, and monitoring.
Build experiment backlogs that drive continuous improvement of AI features.
Craft change management and communication strategies ensuring adoption and trust.

Concept Briefings

KPI Trees & Outcome Mapping

Align your AI initiative to measurable outcomes. Define North Star, input metrics, and leading indicators. Use historical data to set baselines and targets.

codex prompt kpi-tree "Create a KPI tree for reducing onboarding cycle time from 18 days to 7 days using AI-assisted document validation. Include leading indicators, lagging indicators, and operational metrics."

Telemetry & Automation

Instrument prototypes to capture events, errors, and user feedback. Automate CI/CD with testing gates for prompts, UX flows, and compliance checks.

yaml # .github/workflows/ai-workflow-ci.yaml name: AI Workflow CI on: [push, pull_request] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Install deps run: pip install -r requirements.txt - name: Run unit tests run: pytest - name: Prompt regression tests run: python tests/prompt_regression.py - name: UX snapshot tests run: playwright test --project=chromium

Change Enablement

Plan stakeholder journeys, communication cadences, skill enablement, and adoption metrics. Include feedback loops for continuous improvement.

gemini prompt change-plan "Design a change adoption plan for rolling out an AI-assisted underwriting assistant. Include stakeholder segmentation, key messages, training approach, and success metrics."

Guided Exercise Timeline

Step 1 · KPI Tree Workshop (90 min)

Build value alignment

Facilitate a session (synced or async) mapping strategic objectives to operational metrics. Document baseline, target, and measurement cadence.

Step 2 · Telemetry Blueprint (120 min)

Design instrumentation

Define events, logs, and alerts. Identify data flow from prototypes to analytics. Map monitoring responsibilities and alert thresholds.

Ensure PII compliance—store only necessary metadata, anonymise where possible, and document retention policies.

Step 3 · Automation Pipeline (150 min)

Implement CI/CD or orchestrations

Automate ingestion to deployment: requirements updates → prompt/test suites → deployment packaging. Use GitHub Actions, n8n, or Airflow to coordinate.

Step 4 · Experiment Backlog (60 min)

Plan continuous improvement

Document experiments with hypothesis, metrics, duration, and resource estimate. Prioritise using ICE (Impact, Confidence, Effort) or RICE.

Step 5 · Change Playbook (120 min)

Prepare adoption strategy

Develop stakeholder communications, training modules, feedback channels, and success tracking. Align with human-in-the-loop responsibilities.

Lab 04 · Operational Readiness Package

Produce the measurement and operational artefacts needed to scale your AI initiative.

Inputs

Prototype telemetry logs and user metrics.
Governance checklist, risk register, stakeholder map.
Organisation OKRs/strategic objectives.

Outputs

KPI tree + metrics dossier (artifacts/week4/kpi_tree.pdf).
Telemetry blueprint (artifacts/week4/telemetry_plan.md).
Automation pipeline configuration (.github/workflows or n8n/ export).
Experiment backlog (artifacts/week4/experiments.csv).
Change playbook (artifacts/week4/change_plan.pptx or PDF).

Collaboration

Meet with operations or change manager for feedback.
Security/compliance review of telemetry plan.
Peer review of automation pipeline for failure modes.

Execution Steps

Run KPI workshop using templates/kpi_tree_canvas.pptx. Export final tree.
Design telemetry architecture using dbt docs or diagrams.net; include data validation steps.
Implement CI/CD workflow; test with sample commits to ensure prompt regression and UX tests execute.
Populate experiment backlog spreadsheet with at least six items; include ICE scoring and owner.
Create change playbook with audience messaging, training plan, adoption metrics, and escalation contacts.

Validation Checkpoints

KPI tree references baseline data sources and measurement owners.
Telemetry plan documents event schema, storage, retention, and alert thresholds.
Automation pipeline runs end-to-end with sample data, including automated test reports.
Experiment backlog prioritises quick wins and high-impact tests; includes governance checklist tie-ins.
Change playbook maps to stakeholder personas and addresses resistance scenarios.

Reflection & Submission

Submission Checklist

artifacts/week4/kpi_tree.pdf
artifacts/week4/telemetry_plan.md
Automation pipeline artefact (workflow YAML or n8n export).
artifacts/week4/experiments.csv
artifacts/week4/change_plan.pdf
Reflection (artifacts/week4/reflection.md)—what metric or stakeholder insight changed your plan?

Assessment Rubric

Measurement Strategy (30%): Alignment to business value, clarity of metrics, data sourcing.
Automation & Quality (30%): Pipeline robustness, testing coverage, telemetry instrumentation.
Change Enablement (25%): Stakeholder alignment, communication, risk mitigation.
Experimentation (15%): Thoughtful hypotheses, prioritisation, governance linkage.

Submission Process

Push Week 4 branch and open PR summarising key changes. Upload artefacts to portal. Schedule meeting with mentor to review adoption strategy ahead of Week 5 agentic build.

Troubleshooting & FAQ

Baseline data unavailable?

Use historical approximations, industry benchmarks, or synthetic baselines documented clearly. Flag assumptions and plan validation tasks in experiment backlog.

Automation pipeline flaky?

Introduce retries, health checks, and manual approval gates. Log pipeline status to monitoring dashboard. Run pipeline under failure scenarios to test resilience.

Stakeholder resistance?

Map concerns by persona, address trust, control, and workload impacts. Showcase wins via demos and metrics. Provide optional, low-risk trials before full rollout.

Further Study & Next Steps

Recommended Resources

Lean Analytics: frameworks for metric selection.
Google SRE Workbook: alerting and incident response design.
Prosci ADKAR toolkit: change management planning.

Prepare for Module 5

Identify repetitive tasks across discovery, prototyping, testing, and adoption that could be orchestrated by AI agents. List systems/APIs available for automation and set up sandbox credentials.

Preview Module 5 →