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The Product Developer's Course on Governing Generative AI When Product Roadmaps Shift

$199.00
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A focused course, tailored for you

The Product Developer's Course on Governing Generative AI When Product Roadmaps Shift

Build a repeatable governance process for AI features so your roadmap stays stable and regulatory risk stays low.

Stop rebuilding the same AI risk register every sprint while audit deadlines keep slipping.

$199 one-time
Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

You are juggling multiple telematics data pipelines, model updates, and feature releases while the compliance team pushes new audit requests. Every sprint you waste time reconciling undocumented model versioning, manual risk sign-offs, and ad-hoc stakeholder approvals. The lack of a single source of truth forces you to rerun experiments and re-document results, delaying releases and eroding confidence from senior leadership.

At the same time, the insurer’s audit calendar is tightening and the product council expects a clear governance artefact for each AI capability. Your current spreadsheets are scattered across Slack threads, JIRA tickets hold only high-level status, and the evidence pack you need for the next compliance review is missing. Missed deadlines mean product stalls, budget cuts, and a reputation hit for your team.

What you walk away with

  • Create a documented AI governance framework that satisfies audit requirements.
  • Produce a reusable risk-assessment checklist for every model iteration.
  • Generate a live evidence dashboard that updates automatically with each deployment.
  • Establish a clear decision-making RACI for AI feature sign-off.
  • Accelerate release cycles by 30% through streamlined governance steps.

The 12 modules

Module 1. Mapping AI Product Lifecycle to Governance Milestones
Define the exact points where governance actions are required within your development flow.
Module 2. Building a Model Version Registry
Set up a centralized register that captures version, data lineage, and performance metrics.
Module 3. Risk Scoring for Generative Outputs
Apply a quantitative risk matrix to evaluate potential harms of each AI release.
Module 4. Evidence Collection Automation
Create automated scripts that pull logs, test results, and validation reports into a single evidence pack.
Module 5. Stakeholder RACI and Decision Matrix
Formalize who approves what and when using a clear RACI table and decision matrix.
Module 6. Compliance Checklist Integration
Embed regulatory checklist items directly into sprint planning and Definition of Done.
Module 7. Dashboarding for Ongoing Oversight
Design a live governance dashboard that surfaces risk scores and evidence status.
Module 8. Running a Governance Review Workshop
Facilitate a structured review session that walks stakeholders through the evidence pack.
Module 9. Handling Model Drift and Retraining Requests
Implement a repeatable process for detecting drift and documenting retraining decisions.
Module 10. Incident Response Playbook for AI Failures
Prepare a concise runbook that outlines steps when an AI feature misbehaves in production.
Module 11. Continuous Improvement Loop
Set up metrics and retrospectives to evolve the governance process over time.
Module 12. Executive Reporting and Narrative Building
Craft a concise governance narrative that senior leaders can use in board updates.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers Mapping AI Product Lifecycle to Governance Milestones , exactly the missing checkpoint you need when feature planning stalls due to unclear sign-off responsibilities.
Module 4 covers Evidence Collection Automation , precisely the friction you feel when audit evidence is scattered across notebooks and emails.
Module 7 covers Dashboarding for Ongoing Oversight , the exact tool you need to replace manual spreadsheet updates that delay release decisions.

What you get with this course

  • A populated model version registry template.
  • A risk scoring matrix with pre-filled AI hazard categories.
  • An automated evidence collection script guide.
  • A RACI table for AI governance sign-off.
  • A compliance checklist embedded in sprint backlog items.
  • A live governance dashboard mock-up.
  • A step-by-step governance review workshop walkthrough.
  • An incident response runbook for AI failures.
  • A continuous improvement retrospective template.
  • An executive reporting narrative guide.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, model registry template pre-populated for your environment, risk matrix ready for immediate use.

Week 1: first version of the governance dashboard live, initial evidence pack compiled for upcoming audit.

Month 1: recurring sprint governance cadence established, executive-grade evidence pack ready for quarterly board review.

Before and after

Before

Your AI work is scattered across JIRA tickets, Slack files, and ad-hoc spreadsheets. Model versions live in separate notebooks, risk assessments are informal, and audit evidence is assembled last minute, causing release delays and frequent rework.

After

All model versions, risk scores, and evidence are captured in a single registry and live dashboard. Governance steps are baked into each sprint, decision-making is transparent, and you can present a complete evidence pack to leadership on demand.

What happens if you do not address this

If you ignore this, the next audit cycle will arrive with incomplete evidence, forcing a remediation plan that delays feature launches. Your product roadmap will continue to be reshuffled, risking budget cuts and a credibility loss with senior leadership.

Who it is for

A product developer who owns the end-to-end lifecycle of telematics AI features, works in cross-functional squads, and must align engineering, data science, and compliance without a dedicated governance specialist.

Who this is NOT for. This is not for someone who needs a basic introduction to AI fundamentals rather than a governance method.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 40-60 hours of internal scaffolding effort.

Why $199 is the right number

A half-day consultant would charge $2K-$5K for a similar governance setup, generic AI compliance courses run $800-$2K, and building it yourself typically consumes 60+ hours of scattered effort. At $199 you get a complete, ready-to-use toolkit with a custom playbook.

FAQ

Do I need a legal background to use this toolkit?
No, the course translates legal requirements into practical steps you can apply directly.
Will this replace my existing compliance tools?
It complements them by providing a structured process and ready-made artefacts.
Can I apply this to non-AI features as well?
Yes, the governance framework is generic enough for any data-driven product.
What if my team already has a risk register?
The modules help you integrate and enrich it with AI-specific risk scoring and evidence links.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

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