Where teams stand today on hands-on AI use, in their own words. Real outcomes, the operating patterns we've found unlock the most value, and the playbook we're using to onboard the next set of teams and roles.
Two distinct strategies for bringing a person up to speed with AI — different shapes for cost, time-to-impact, and depth of learning. Both reference the same five-level AI Adoption Spectrum.
A five-level model of how teams progress with AI assistance — from passive code suggestions through to a fully-resourced workspace built around the AI-DLC methodology.
Walk people left-to-right through the spectrum: Vibe Coding → Guided Dev → Spec Driven → Workspace. Each level introduces new concepts, best practices, and techniques for improving quality and time-to-build.
Drop the person into a workspace that's already set up with the context, skills, and guardrails for the work they actually do. The system carries the expertise; the person becomes productive on day one and absorbs the foundational knowledge over time.
Voices and outcomes from people doing the work. Tabs below switch between teams.
Alex is using AI to read our existing MS SQL vendor load procedures and translate them into Python code that reads from S3 parquet files into our MS SQL data warehouse databases. The goal is to replace the SQL Load procedures so a Custom RDS MS SQL Instance isn't needed for L&S.
Converted Oasis Table & Everi CDM SQL loads to Python with data validations, running in a local environment.
PlayerDayMeasure_Slot records were being arbitrarily deleted. Claude found the issue in about a minute.
Instead of relying on memory (or pinging Mark), had Claude scan every repo for places the ID was used and update them accordingly.
Three-table update driven from a customer email. Claude reads the email and translates it into the data updates. ~15–30 minutes per project — stacks across every implementation.
Previously his biggest day-to-day frustration. Now seamless — Claude handles the mechanics.
Repeatable workspace setup. Lots of progress over the AI Pilot Project time period — created a repeatable method for workspace setup that enables other IA TC's to work in it. Red and Lexi were the first beneficiaries.
AI-assisted onboarding. Just joined IA — using Claude heavily to ramp by asking questions directly against the repo (code + documentation).
VP Product, hands-on in the workflow. Working through the AI-DLC method end-to-end on Insight Cash — proof the methodology is approachable for non-engineering leaders.
From first PR (2026-05-11) through end of week one (2026-05-17) — authored on GitHub as CCTBrandy.
The common patterns that allow for maximum potential and speed to productivity. These showed up consistently across every story in Section 2.
Everyone working from the same folder + file + MCP layout, synced to the appropriate data sources. No team should have to invent it from scratch.
Codify the flow and the specific tasks people repeat into reusable skills, so the team isn't re-explaining the same thing to Claude every session.
Leave a trail of .md files so Claude has the "memory" of what's been done — and patterns surface naturally for the next person to pick up.
Resist the urge to step outside the session for a "quick" manual fix — even when you think you'd be faster, you're starving the system of context.
Keep capturing the things that aren't documented or only live in someone's head. Tribal knowledge becomes a renewable asset, not a single point of failure.
Make sure the skills and structure we're building are the right ones, established correctly — review gates, naming conventions, and shared standards.
The steps for creating the best workspace for onboarding a new team or a new role.
Repos, websites, documentation, OneDrive — everywhere the work currently lives.
A Markdown or Notion page with the instructions for Claude to materialize a local workspace populated with the sources from step 1. Repeatable for every new team member.
The work the team does often becomes a skill, not a fresh prompt every time. This is where the compounding starts.
Prioritize artifacts that deliver one or more of:
Review gates, naming conventions, and shared standards — so what gets built fits the structure and stays consistent across teams.
Where to point investment over the coming quarter — converting individual wins into team- and org-level compounding gains.
| Time-to-impact | ~1 week |
|---|---|
| Teams engaged | 3 |
| Practitioners with documented results | 6 |
| Unlock principles codified | 6 |
| Onboarding playbook steps | 5 |
| Sentiment signal | "Best week of work in a while" |