AI Enablement Engagement

Session 0 — Kickoff & Learning Bed Selection

Phase: 1 — Assessment & Strategy Target Week: Week 1 (precedes Session 1) Duration: 1.5 hours Format: Remote working session Audience: Engagement sponsor (Donnell Jenkins), dev team leads, architect(s), anyone with broad knowledge of Turner's internal product portfolio

Objective

Kick off the engagement, walk the team through what they're about to receive over the next ten weeks, and — most importantly — select the learning bed product: a single internal Turner product that will be the running case study across every subsequent session. By the time the engagement ends, that product will be fully wrapped in AI-assisted development practices, and Turner's team will have the pattern they need to replicate that wrapping across the rest of their portfolio.

Why a learning bed product

A single focal product turns abstract training into concrete craft. Instead of demonstrating techniques on disconnected examples, every session in Phases 1 and 2 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.

The team owns the work of wrapping the rest of their portfolio after the engagement; this engagement equips them with a complete, working example to copy from.

Key Topics

  • Engagement overview: the three phases, the four SOW focus areas, what Turner gets and what's expected of them
  • Inventory of Turner's internal products that the team currently maintains
  • Discussion of pain points and AI ambitions for each candidate product
  • Selection criteria for the learning bed (see below)
  • Selection of the learning bed product and confirmation of access/availability
  • Logistics: cadence, scheduling, communication channel, who attends which sessions

Learning Bed Selection Criteria

The selected product should be:

  • Representative of how Turner builds and maintains software internally — picking the weirdest outlier teaches less
  • Accessible — repos, pipelines, and environments that the engagement team can reach without prolonged access requests
  • Staffed — at least one developer and one domain expert who will be present across the engagement
  • Active — has a real backlog Turner will draw the capstone work item from
  • Right-sized — complex enough to exercise the full toolchain, but not so sprawling that progress is invisible
  • Low-blast-radius — pilot pipeline changes won't disrupt production

Outcomes

  • Shared understanding of the engagement shape and expectations
  • Selected learning bed product with sign-off from the sponsor
  • Named developer and domain expert who will attend the engagement consistently
  • Confirmed access path (repos, Azure DevOps, pipelines) for the learning bed
  • Anchor for everything Nubitz prepares before Session 1

Inputs Required from Turner

  • Sponsor (Donnell) present
  • Someone who can speak to the product portfolio at a high level
  • Decision authority to commit a product as the learning bed by end of session

Deliverable Contribution

Sets the scope for the AI Development Strategy Document and the AI Configuration Assets deliverables — both will be tailored to the selected learning bed product as the primary worked example.