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GEN1797 Mastering AI and Automation Leadership

$199.00
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The Executive Diagnostic and Governance Toolkit

Mastering AI and Automation Leadership

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing deciding what to adopt, in what order, and defending that choice when the budget round asks why this and not that.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You are responsible for AI and automation progress — but every choice feels contested, underfunded, or premature.

The situation this is built for

Every week brings a new AI tool promising transformation. Your peers point to flashy demos. Executives ask why you haven’t adopted certain capabilities yet. But you know the real work: aligning stakeholders, proving ROI, managing change, and avoiding technical debt. You need a way to assess what matters, act decisively, and defend your roadmap — without relying on vendor claims or speculative pilots.

Who this is for

A senior leader accountable for AI and automation outcomes, typically in operations, digital transformation, or technology strategy. Owns the roadmap, vendor evaluations, and cross-functional execution. Faces budget scrutiny and must justify sequencing and scope.

Who this is not for

Individual contributors not responsible for strategy, technical implementers without decision authority, or leaders seeking only vendor comparisons or technical deep dives.

What you walk away with

  • Assess your current AI and automation maturity with precision
  • Build a prioritized, evidence-based initiative roadmap
  • Lead stakeholder conversations with structured decision frameworks
  • Design agent-first workflows that scale without disruption
  • Govern model deployment and handoff decisions effectively

How this maps to your situation

  • Understanding the current automation landscape
  • Shifting to agent-based process design
  • Evaluating readiness for AI initiatives
  • Sustaining long-term AI transformation

Before vs. after

Before
Overwhelmed by options, reacting to trends, struggling to justify choices in budget reviews.
After
Confidently leading with a clear, evidence-based roadmap for AI and automation progress.

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 hours per module, designed to be completed in weekly increments with team application.

If nothing changes
Without a structured approach, AI efforts will remain fragmented, underfunded, and vulnerable to reversal during budget scrutiny. Leaders who cannot articulate a defensible roadmap risk losing control of the function to external vendors or siloed teams.

How this compares to the alternatives

Unlike generic AI courses or vendor-led training, this course focuses exclusively on the leadership decisions, governance structures, and prioritization frameworks needed to own AI and automation transformation. It does not teach coding or promote specific tools, but provides actionable structure for executives who must deliver results.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Mapping the Current State of Automation
Establish a baseline of existing tools, workflows, and pain points to ground future decisions.
12 chapters in this module
  1. Identifying all active automation scripts in production
  2. Documenting handoff points between systems and people
  3. Cataloging decision rules in current workflow logic
  4. Measuring cycle time before and after automation steps
  5. Assessing error rates in automated versus manual tasks
  6. Mapping ownership of each automation component
  7. Evaluating integration depth with core systems
  8. Tracking change frequency in automated processes
  9. Classifying automations by business criticality
  10. Quantifying maintenance effort per automation
  11. Reviewing incident logs tied to automation failures
  12. Benchmarking against peer organization patterns
Module 2. Defining the Agent-First Operating Model
Shift from task automation to autonomous agent coordination within business functions.
12 chapters in this module
  1. Distinguishing agents from scripts and bots
  2. Defining agent autonomy levels in decision making
  3. Setting boundaries for agent escalation behavior
  4. Designing agent identity and role permissions
  5. Mapping agent interactions across business units
  6. Establishing agent handoff protocols with humans
  7. Specifying agent memory and context retention rules
  8. Creating agent performance monitoring dashboards
  9. Defining agent lifecycle management procedures
  10. Integrating agent actions into audit trails
  11. Aligning agent goals with business KPIs
  12. Planning agent training and revalidation cycles
Module 3. Assessing AI Initiative Readiness
Evaluate potential AI projects using operational, data, and governance criteria.
12 chapters in this module
  1. Scoring data availability for model training
  2. Validating data labeling consistency across teams
  3. Assessing feature engineering pipeline maturity
  4. Evaluating model inference latency requirements
  5. Checking for regulatory constraints on AI use
  6. Reviewing model explainability needs by stakeholder
  7. Auditing access controls for model endpoints
  8. Estimating cost of model drift detection
  9. Mapping model dependencies on external APIs
  10. Assessing team readiness to maintain AI systems
  11. Reviewing incident response plans for AI failures
  12. Benchmarking model performance against baselines
Module 4. Prioritizing Use Cases by Strategic Fit
Rank AI and automation initiatives using business impact and implementation complexity.
12 chapters in this module
  1. Defining strategic goals for AI adoption
  2. Scoring use cases by revenue protection impact
  3. Evaluating cost reduction potential per initiative
  4. Assessing customer experience improvement magnitude
  5. Measuring alignment with core business differentiators
  6. Estimating implementation timeline for each use case
  7. Identifying cross-functional dependencies early
  8. Calculating resource requirements for deployment
  9. Evaluating change management effort needed
  10. Prioritizing use cases with quick validation paths
  11. Balancing high-effort and low-effort initiatives
  12. Building a weighted scoring model for decisions
Module 5. Building the AI Investment Business Case
Create defensible justifications for AI spending using operational metrics.
12 chapters in this module
  1. Defining baseline performance for comparison
  2. Estimating full lifecycle cost of AI deployment
  3. Projecting headcount impact of automation
  4. Calculating risk exposure reduction value
  5. Quantifying time savings across roles
  6. Estimating error reduction financial benefit
  7. Modeling avoided cost from faster resolution
  8. Assigning dollar values to SLA improvements
  9. Factoring in training and change costs
  10. Building sensitivity analysis for assumptions
  11. Presenting trade-offs between build and buy
  12. Aligning ROI calculation with finance norms
Module 6. Designing Human-Agent Collaboration
Structure workflows where people and agents share responsibilities effectively.
12 chapters in this module
  1. Defining tasks suitable for full automation
  2. Identifying decisions requiring human oversight
  3. Setting thresholds for agent escalation
  4. Designing feedback loops from humans to agents
  5. Creating shared context between roles
  6. Standardizing handoff documentation format
  7. Implementing real-time collaboration channels
  8. Training staff on agent interaction protocols
  9. Establishing agent performance review cycles
  10. Documenting fallback procedures for agent errors
  11. Measuring trust levels in agent recommendations
  12. Updating playbooks as agent capabilities evolve
Module 7. Governance for Autonomous Systems
Implement controls to ensure safety, compliance, and accountability in AI operations.
12 chapters in this module
  1. Defining model approval board responsibilities
  2. Setting thresholds for model retraining
  3. Establishing audit logging requirements
  4. Implementing model versioning standards
  5. Creating model rollback procedures
  6. Enforcing data privacy in model inputs
  7. Validating model fairness across segments
  8. Monitoring for unintended model behavior
  9. Requiring model documentation packages
  10. Scheduling regular model health reviews
  11. Enforcing secure model deployment practices
  12. Tracking model lineage from training to production
Module 8. Scaling AI Across Business Functions
Expand AI initiatives beyond pilot scope while maintaining control and coherence.
12 chapters in this module
  1. Identifying transferable automation patterns
  2. Adapting models for new domains safely
  3. Standardizing data pipeline interfaces
  4. Creating shared service models for reuse
  5. Establishing center of excellence roles
  6. Defining onboarding process for new teams
  7. Building cross-functional automation standards
  8. Creating internal knowledge base for patterns
  9. Measuring adoption rate across units
  10. Tracking consistency in implementation
  11. Managing technical debt in scaling efforts
  12. Optimizing infrastructure for multi-team use
Module 9. Managing AI Vendor Evaluations
Evaluate external tools based on integration, transparency, and long-term fit.
12 chapters in this module
  1. Defining required capabilities for RFP
  2. Assessing vendor API documentation quality
  3. Testing vendor system observability features
  4. Evaluating model explainability offerings
  5. Reviewing vendor update and deprecation policy
  6. Assessing support response time commitments
  7. Validating security compliance certifications
  8. Testing integration with identity systems
  9. Reviewing data ownership terms in contracts
  10. Evaluating exit strategy and data portability
  11. Assessing training materials for usability
  12. Benchmarking performance under load conditions
Module 10. Leading Change in AI Adoption
Drive organizational alignment and adoption through structured communication.
12 chapters in this module
  1. Mapping stakeholder influence and concerns
  2. Communicating AI goals in business terms
  3. Addressing job impact fears proactively
  4. Creating role-specific training plans
  5. Demonstrating early wins visibly
  6. Establishing feedback channels for concerns
  7. Celebrating team adaptation successes
  8. Updating performance metrics post-AI
  9. Revising career paths to include AI skills
  10. Measuring change adoption through surveys
  11. Tracking workflow disruption recovery time
  12. Adjusting messaging based on team feedback
Module 11. Measuring AI Initiative Performance
Track progress using metrics that reflect real business outcomes and sustainability.
12 chapters in this module
  1. Defining primary success metrics per project
  2. Setting baseline measurements before launch
  3. Tracking model accuracy over time
  4. Monitoring operational cost changes
  5. Measuring user satisfaction with AI tools
  6. Assessing reduction in manual effort
  7. Evaluating error rate trends post-automation
  8. Calculating time-to-resolution improvements
  9. Auditing compliance with governance rules
  10. Reviewing incident frequency for AI systems
  11. Measuring model drift detection effectiveness
  12. Reporting progress to executive stakeholders
Module 12. Sustaining AI and Automation Momentum
Maintain progress through continuous evaluation and strategic refresh.
12 chapters in this module
  1. Scheduling regular roadmap review meetings
  2. Updating initiative priorities quarterly
  3. Reassessing AI maturity annually
  4. Refreshing skills inventory for AI roles
  5. Evaluating new AI capabilities responsibly
  6. Updating governance policies as needed
  7. Rotating team members through AI roles
  8. Sharing lessons across business units
  9. Revising training materials regularly
  10. Archiving deprecated automation systems
  11. Celebrating long-term AI achievements
  12. Planning for next-generation technology shifts

Frequently asked

Who is this course designed for?
Senior leaders accountable for AI and automation outcomes, including those in operations, digital transformation, and technology strategy roles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover technical implementation details?
No. It focuses on leadership decisions, prioritization, governance, and stakeholder alignment, not hands-on coding or system configuration.
Will I receive practical tools with the course?
Yes. Each module includes downloadable templates and worked examples, plus a hand-built implementation playbook delivered at enrollment.
Can I share this course with my team?
Each enrollment is for individual use. Team licensing is available upon request.
Is there a refund policy?
Yes. We offer a 30-day money-back guarantee if the course does not meet your expectations.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed in weekly increments with team application..

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·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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