AI DEEP LEARNING PROGRAM

Build the Skills. Ship the Artifacts. Change How Your Team Works.

A 12-week engagement that embeds AI into how your team actually works. Not a workshop. Not a course. A structured program with a dedicated Embedded Engineer working alongside your team every single day.

12 Weeks

Structured program

6 Sessions

Trainer-led, 4 hours each

3 Roles

Product, Dev, and Testing together

Daily

Embedded Engineer coaching

THE PROBLEM

Workshops Teach. Habits Require Something More.

Most AI training ends when the session ends. The DLP is built around a different premise: that changing how a team works requires daily reinforcement on real work, not slides and good intentions.

Day 1

Tools Without Habits Don't Stick

Teams get AI access and maybe a workshop. Without daily reinforcement on real work, usage stays shallow and inconsistent regardless of the tooling.

Notes ≠

Training Produces Notes, Not Artifacts

Most programs end with slides and summaries. The DLP ends every session with committed skill files in your team's actual repositories.

3 Roles

Siloed Learning Breaks the Artifact Chain

When Product, Dev, and Testing learn separately, the chain falls apart. Every DLP session brings all three roles into the room together.

THE DELIVERY MODEL

Two Roles. One Consistent Standard.

The DLP runs on a dual delivery model so techniques get introduced and habits get built at the same time, on real work. Not in theory.

ROLE 1

Track Lead

Senior trainer-coach delivering six half-day workshops. Sessions land at the start of each two-week block, setting the agenda for the Embedded Engineer's daily coaching.

ROLE 2

Embedded AI Engineer

A dedicated coach working alongside your team every day between sessions, applying concepts to real work, building habits, and extracting reusable skill files from actual project context.

THE PROGRAM

Two Loops, One Artifact Chain

Each session produces a committed artifact your team uses the next morning. Every two weeks builds on the last until the full chain is assembled.

WEEK 1

Acceptance Criteria Generation

Working AC skill file, ready to use with the Embedded Engineer the next morning

WEEK 3

Unit Test Generation

Test generation skill file built from real stories and Acceptance Criteria

WEEK 5

Test Planning

Full Acceptance Criteria to Tests to Test Plans chain assembled

WEEK 7

Prompt Evaluation

Working evaluator for the primary skill file; first iteration cycle

WEEK 9

Skills

Skill file meeting transferability standard; peer-reviewed artifact

WEEK 11

Agents & Workflows

First bounded agent or workflow with a commitment statement for continued adoption

IMPROVING AI ADOPTION MODEL

Moving Your Team from Stage 2 to Stage 4

The DLP is designed to move your team across the Autonomy Inflection Point, where teams stop needing permission for every AI action and start directing AI output instead.

2

Permissions

Human does the work. AI assists with permission on every step.

Role: Doer

3

Task

Autonomy emerges. AI acts on single tasks independently.

Inflection Point

4

Workflow

AI drafts across systems. Human reviews output.

Role: Reviewer

The jump from Stage 2 to Stage 3 requires turning permissions off, demanding new skills, governance, and trust. The DLP builds the habits to cross this threshold safely.

WHAT YOUR TEAM BUILDS