Turner Industries — AI Enablement Curriculum Overview

Engagement: AI-Enabled Development Acceleration Client: Turner Industries (Donnell Jenkins) Consultant: Nubitz, LLC (Steve Schaneville) Duration: 10 weeks total — 5 weeks active engagement (Phases 1 & 2) plus 4 weeks white-glove support (Phase 3)

The Learning Bed Approach

This curriculum is built around a single focal product — the learning bed — selected by Turner in the kickoff session. Every subsequent session advances the same artifact: discovery on the product, governance shaped around the product, CLAUDE.md and skills authored for the product, the product's Azure DevOps pipeline gaining AI-augmented steps, and a real backlog item from the product taken end-to-end in the capstone.

By the end of the engagement, the learning bed product is fully wrapped in AI-assisted development practices. Turner's team owns the work of replicating that wrapping across the rest of their portfolio — and they have a complete working example to copy from.

Curriculum Shape

Seven sessions span the active engagement: a kickoff (Session 0), two strategy sessions (Sessions 1-2), and four hands-on enablement sessions (Sessions 3-6). Sessions are designed for Turner's hands-on development team.

# Session Phase Week Duration Focus Area
0 Kickoff & Learning Bed Selection 1 1 1.5 hrs Engagement framing + product selection
1 Discovery & Current-State Assessment 1 1 2 hrs Production-Ready Tooling Assessment
2 Strategy & Governance Workshop 1 2 2 hrs AI Context & Governance Strategy
3 AI-Assisted Development Foundations 2 3 2.5 hrs Hands-on AI tooling
4 Context Management & Custom Skills 2 3-4 2.5 hrs AI Context & Governance Strategy
5 Azure DevOps Integration 2 4 2.5 hrs DevOps Pipeline Integration
6 Speed vs. Risk: Capstone & Best Practices 2 5 2.5 hrs Speed vs. Risk Optimization

Total active facilitation: ~15.5 hours across 5 weeks. Plus 10 hours of Phase 3 white-glove support delivered via scheduled calls.

How the Learning Bed Threads Through the Sessions

  • Session 0 — Turner selects the learning bed product from its internal portfolio.
  • Session 1 — Discovery and audit performed on the learning bed product: its workflows, pipelines, security posture, and pain points.
  • Session 2 — Governance framework and context-management strategy designed against the learning bed's realities; configuration-asset specs target this product first.
  • Session 3 — Foundational exercises (explain, refactor, test, review) run against real code in the learning bed.
  • Session 4 — CLAUDE.md, AGENTS.md, and SKILL.md authored directly in the learning bed's repos.
  • Session 5 — AI-augmented pipeline steps wired into the learning bed's Azure DevOps pipeline.
  • Session 6 — A real backlog item from the learning bed taken end-to-end with the full toolchain in the capstone.

Mapping to SOW Focus Areas

The four core areas from the SOW are addressed across the curriculum as follows:

  • AI Context & Governance Strategy is anchored in Sessions 2 and 4 — strategy/framework design then hands-on CLAUDE.md, AGENTS.md, and SKILL.md authoring in the learning bed's repos.
  • Production-Ready Tooling Assessment runs through Sessions 1, 3, and 5 — current-state audit, foundational tool training, and tooling integration into pipelines.
  • Speed vs. Risk Optimization is framed in Session 2 (risk analysis) and operationalized in Session 6 (patterns, guardrails, capstone exercise).
  • DevOps Pipeline Integration is the explicit focus of Session 5 and reinforced in Session 6.

Audience

Primary audience: Turner's developers — engineers who will use Claude and AI workflows day-to-day, including the named developer(s) for the learning bed product. The engagement sponsor and technical leadership are expected at Session 0 (selection), and encouraged at Sessions 2 and 6 (governance and wrap-up). DevOps/platform engineers should be present for Session 5. The learning bed's domain expert should attend Sessions 1, 4, and 6.

Phase 3: White-Glove Support (Weeks 6-10)

Following Session 6, up to 10 one-hour calls are available to Turner's team for ad-hoc guidance, troubleshooting, and refinement as they apply the new workflows independently — especially as they begin replicating the pattern from the learning bed onto other products in their portfolio.

Deliverables Aligned to Sessions

  • Phase 1 deliverables (driven by Sessions 0-2): AI Development Strategy Document; recommendations and specifications for AI configuration assets — both tailored to the selected learning bed product.
  • Phase 2 deliverables (driven by Sessions 3-6): Guidance for integrating AI workflows into Turner's environment and Azure DevOps pipelines; Developer Reference Guide for Turner-specific AI-assisted development practices, using the learning bed product as the worked example throughout.

Folder Layout

This folder set contains:

  • 1 - High-Level Outlines/ — Session-by-session summaries with objectives, topics, and deliverables (this folder).
  • 2 - Detailed Agendas/ — Time-blocked agendas with demos, exercises, and durations for each session.