A tailored course, built for your situation
Authority to Shape Generative AI Standards in Your Current Role
Build influence and decision ownership without changing titles or teams
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Feeling like you execute others’ AI governance decisions rather than shaping them
The situation this course is for
You're technically ahead of the curve, but still need approval to propose architecture changes or block risky deployments. Your insights land second, not first.
Who this is for
Senior individual contributor in AI/ML engineering shaping internal tools, frameworks, or platform decisions without formal authority over policy
Who this is not for
Managers looking to delegate AI oversight, or engineers focused only on model accuracy or inference speed without governance involvement
What you walk away with
- Final call on AI pattern approvals without escalation
- First review on incoming AI use cases across teams
- Priority input on internal AI tooling investments
- Named ownership of internal AI standards documents
- Ability to decline implementations misaligned with core principles
The 12 modules (with all 144 chapters)
Module 1. Claiming Ownership of Pattern Decisions
Establish your role as the default decision-maker for generative AI patterns by aligning technical choices with business risk appetite.
12 chapters in this module
- Defining pattern ownership
- Mapping team dependencies
- Identifying decision thresholds
- Setting review gates
- Versioning frameworks
- Logging rationale
- Naming authority zones
- Blocking unsafe patterns
- Routing exceptions
- Updating guardrails
- Tracking adoption
- Measuring impact
Module 2. Building Internal Credibility Without Titles
Grow influence through consistency, clarity, and early wins that position you as the go-to expert.
12 chapters in this module
- Spotting leverage moments
- Documenting first principles
- Sharing templates early
- Running peer reviews
- Capturing feedback
- Measuring alignment
- Creating reference cases
- Highlighting efficiency gains
- Attributing outcomes
- Positioning updates
- Gaining silent buy-in
- Avoiding overreach
Module 3. Structuring Repeatable Governance Templates
Design reusable decision frameworks that compound your influence across projects.
12 chapters in this module
- Defining template scope
- Choosing approval paths
- Embedding risk tiers
- Linking to data policies
- Adding audit trails
- Standardizing naming
- Integrating with CI/CD
- Automating checks
- Updating versions
- Version rollback rules
- Documenting exceptions
- Measuring compliance
Module 4. Owning Tooling and Infrastructure Choices
Gain decision rights over which platforms, libraries, and monitoring systems your team adopts.
12 chapters in this module
- Assessing integration fit
- Evaluating cost impact
- Benchmarking performance
- Testing security posture
- Reviewing vendor terms
- Setting deprecation rules
- Creating migration paths
- Tracking usage metrics
- Budgeting for scale
- Prioritizing upgrades
- Documenting trade-offs
- Gaining executive sign-off
Module 5. Setting Precedent Through First Reviews
Ensure your function sees AI use cases first by designing intake workflows others adopt.
12 chapters in this module
- Mapping request sources
- Designing intake forms
- Setting SLAs
- Routing for feedback
- Flagging risks early
- Prioritizing reviews
- Tracking volume trends
- Optimizing handoffs
- Reducing rework
- Improving time-to-approval
- Measuring adoption
- Scaling review capacity
Module 6. Declining Misaligned Implementations
Build the confidence and documentation to say no to AI deployments that don’t meet standards.
12 chapters in this module
- Defining clear boundaries
- Creating rejection templates
- Logging rationale
- Offering alternatives
- Tracking override rates
- Escalating patterns
- Protecting brand risk
- Maintaining neutrality
- Avoiding bottlenecks
- Supporting remediation
- Updating policies
- Measuring compliance rate
Module 7. Shaping Investment Priorities
Influence where money goes by linking technical needs to business value and risk reduction.
12 chapters in this module
- Mapping spend to use cases
- Identifying cost levers
- Building business cases
- Presenting to finance
- Tracking ROI post-deployment
- Forecasting needs
- Aligning with roadmap
- Prioritizing requests
- Negotiating budgets
- Documenting decisions
- Updating forecasts
- Measuring impact
Module 8. Defining Risk Thresholds and Guardrails
Set the rules for acceptable AI behavior and ensure they’re enforced across teams.
12 chapters in this module
- Classifying risk levels
- Setting probability thresholds
- Defining harm types
- Mapping mitigation paths
- Automating alerts
- Setting response protocols
- Training reviewers
- Updating frameworks
- Running drills
- Documenting incidents
- Reviewing post-mortems
- Improving thresholds
Module 9. Creating Cross-Functional Influence
Work effectively with legal, security, and product teams to embed your standards enterprise-wide.
12 chapters in this module
- Mapping stakeholder needs
- Aligning on definitions
- Scheduling syncs
- Building shared templates
- Resolving conflicts
- Attributing wins
- Scaling coordination
- Tracking adoption
- Improving feedback loops
- Reducing friction
- Measuring reach
- Growing collaboration
Module 10. Institutionalizing Your Framework
Turn personal expertise into organizationally recognized standards.
12 chapters in this module
- Gaining official endorsement
- Publishing documentation
- Training new hires
- Embedding in onboarding
- Linking to HR systems
- Tracking certification
- Updating annually
- Measuring compliance
- Celebrating adoption
- Rewarding alignment
- Scaling globally
- Evolving with tech
Module 11. Measuring and Demonstrating Impact
Quantify your contribution to risk reduction, speed, and consistency.
12 chapters in this module
- Choosing KPIs
- Tracking adoption rate
- Measuring rework reduction
- Calculating risk avoidance
- Benchmarking speed
- Surveying peers
- Reporting to leadership
- Adjusting focus
- Improving frameworks
- Highlighting wins
- Linking to goals
- Scaling insights
Module 12. Expanding Scope Without Promotion
Identify the next layer of influence to pursue while staying in your current role.
12 chapters in this module
- Auditing decision rights
- Spotting expansion gaps
- Planning next moves
- Building coalitions
- Testing new areas
- Measuring reach
- Refining messaging
- Gaining recognition
- Avoiding burnout
- Sustaining momentum
- Scaling impact
- Evolving your role
How this maps to your situation
- When rolling out a new AI service
- When reviewing third-party integrations
- When onboarding new team members
- When proposing infrastructure upgrades
Before vs. after
Before
You contribute to AI standards but don't control them. Decisions require escalation. Your influence is informal and inconsistent.
After
You lead the design and adoption of AI standards. You own key thresholds, tooling choices, and governance rules in your domain.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3-4 hours per week over 6 weeks, designed to fit around active projects.
If nothing changes
Without structured influence, your expertise remains reactive. Others will define standards that you'll have to follow, reducing your ability to shape safe, scalable AI systems.
How this compares to the alternatives
Unlike generic AI governance courses, this program focuses on earned authority in technical roles, how to gain real decision rights without a promotion. Most curricula assume management authority; this one assumes influence must be built.
Frequently asked
Who is this course for?
Senior individual contributors in AI/ML engineering who want to own standards and decisions in their current role.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this require management approval?
No. The strategies are designed for ICs to build influence organically, without formal authority.
$199 one-time. Approximately 3-4 hours per week over 6 weeks, designed to fit around active projects..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours