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GEN1797 AI and Automation Leadership for Executives

$201.00
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The Executive Diagnostic and Governance Toolkit

AI and Automation Leadership for Executives

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’re responsible for AI adoption, but every option feels high-risk and hard to compare.

The situation this is built for

You’re expected to lead decisions on AI and automation, yet the tools evolve faster than your ability to assess them. You must choose what to adopt, in what order, and justify those choices to leadership who demand results but don’t understand the trade-offs. There’s no shortage of solutions, but no clear path to decide which ones belong in your function — or when. You’re making calls in isolation, without a framework to separate transformation from theatre.

Who this is for

A senior leader who owns the function responsible for integrating AI and automation into core operations, including workflow redesign, agent deployment, and system governance.

Who this is not for

This is not for technical implementers, data scientists, or solution vendors. It is not for those seeking product tutorials or deployment coding guides.

What you walk away with

  • Define a prioritization model for AI adoption specific to your operational context
  • Map current automation maturity across teams and processes
  • Build defensible business cases for agent-based workflow changes
  • Govern AI integration without becoming a bottleneck
  • Lead cross-functional alignment on AI ethics, risk, and rollout

How this maps to your situation

  • Assessing current state and gaps
  • Prioritizing transformation opportunities
  • Building approval and funding cases
  • Sustaining long-term execution and governance

Before vs. after

Before
Overwhelmed by options, lacking a clear framework to prioritize AI and automation initiatives, and struggling to justify decisions to leadership.
After
Equipped with a personalized roadmap, governance model, and communication strategy to lead AI integration with confidence and clarity.

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 45 minutes per chapter, with flexibility to move at your own pace. Total estimated engagement is 60 to 70 hours over 12 weeks.

If nothing changes
Without a structured approach, you risk automating the wrong processes, overspending on solutions that don’t scale, and losing credibility when pilots fail to deliver. The longer you wait, the more reactive your decisions become, and the harder it is to catch up when competitors embed agents deeply into their operations.

How this compares to the alternatives

Unlike vendor-led training or generic online courses, this program focuses exclusively on the leadership decisions behind AI adoption — not technical configuration. It provides no product endorsements, only structured thinking tools, decision frameworks, and templates tailored to executives responsible for outcomes.

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. Understanding Your Current Automation Baseline
Establish a clear picture of where your organization stands in AI and automation adoption today.
12 chapters in this module
  1. Identifying all active automation initiatives across departments
  2. Mapping the current state of workflow digitization
  3. Classifying processes by automation readiness level
  4. Documenting legacy system dependencies and constraints
  5. Assessing team capacity for managing AI agents
  6. Evaluating data quality for agent-driven decision making
  7. Reviewing existing governance policies for AI use
  8. Tracking incident reports related to automated failures
  9. Benchmarking against industry-specific automation norms
  10. Cataloging approved and shadow automation tools
  11. Measuring the cost of manual work still in place
  12. Defining the scope of your decision-making authority
Module 2. Defining the Role of Agents in Your Operations
Clarify how autonomous agents fit into your business processes and where they add real value.
12 chapters in this module
  1. Differentiating between rules-based bots and AI agents
  2. Identifying high-friction handoffs suitable for agents
  3. Assessing decision complexity in current workflows
  4. Determining agent ownership and accountability lines
  5. Evaluating agent explainability requirements
  6. Designing fallback protocols for agent errors
  7. Setting thresholds for human-in-the-loop intervention
  8. Integrating agents into service-level agreements
  9. Measuring agent performance beyond uptime
  10. Aligning agent behavior with compliance standards
  11. Planning for agent versioning and updates
  12. Documenting agent interactions in audit trails
Module 3. Prioritizing Work for Agent-First Transformation
Apply a consistent framework to decide which processes should be rebuilt around agents first.
12 chapters in this module
  1. Ranking workflows by strategic impact and volume
  2. Estimating time-to-value for agent integration
  3. Evaluating error tolerance in candidate processes
  4. Assessing stakeholder readiness for change
  5. Calculating opportunity cost of delaying automation
  6. Identifying processes with high rework rates
  7. Mapping customer journey pain points for automation
  8. Prioritizing based on data availability and structure
  9. Balancing speed of deployment with risk exposure
  10. Using pilot results to inform scaling decisions
  11. Avoiding automation of broken or obsolete workflows
  12. Aligning transformation roadmap with budget cycles
Module 4. Building Business Cases That Win Approval
Create compelling justifications for AI investments that speak to finance and operations.
12 chapters in this module
  1. Structuring financial models for agent deployment
  2. Quantifying time saved in human effort terms
  3. Estimating reduction in process variance and errors
  4. Projecting downstream savings from early automation
  5. Including hidden costs like training and monitoring
  6. Framing automation benefits in risk mitigation terms
  7. Aligning project scope with capital allocation rules
  8. Presenting trade-offs between build and buy options
  9. Using scenario planning to show range of outcomes
  10. Incorporating change management costs upfront
  11. Tying automation KPIs to executive scorecards
  12. Anticipating audit and compliance verification needs
Module 5. Designing Governance for AI Integration
Establish oversight structures that enable progress without sacrificing control.
12 chapters in this module
  1. Creating an AI review board with clear mandates
  2. Defining approval thresholds by automation impact
  3. Setting policies for agent access to sensitive data
  4. Documenting ethical considerations in agent design
  5. Implementing version control for agent logic
  6. Establishing audit requirements for agent decisions
  7. Requiring transparency in agent training data
  8. Enforcing documentation standards for agent behavior
  9. Monitoring for unintended agent consequences
  10. Setting sunset policies for underperforming agents
  11. Requiring periodic reassessment of agent necessity
  12. Integrating AI governance into enterprise risk frameworks
Module 6. Integrating Agents with Human Teams
Ensure smooth collaboration between people and AI agents in daily operations.
12 chapters in this module
  1. Redefining roles after agent implementation
  2. Designing handoff points between agents and staff
  3. Training teams to supervise and correct agents
  4. Communicating agent limitations to end users
  5. Adjusting performance metrics for hybrid workflows
  6. Managing resistance to agent-driven change
  7. Creating feedback loops from staff to agent design
  8. Planning for workload redistribution post-automation
  9. Recognizing agent-caused stress in teams
  10. Establishing escalation paths for agent confusion
  11. Measuring team adaptation over time
  12. Rebalancing staffing as automation scales
Module 7. Scaling Automation Beyond Pilots
Move from isolated experiments to organization-wide AI integration.
12 chapters in this module
  1. Evaluating pilot success using operational metrics
  2. Identifying common failure modes in early deployments
  3. Standardizing agent configuration patterns
  4. Building reusable automation components
  5. Creating templates for agent deployment workflows
  6. Developing internal certification for automation teams
  7. Establishing centers of excellence for AI practices
  8. Sharing lessons learned across business units
  9. Scaling infrastructure to support agent growth
  10. Managing dependencies between automated systems
  11. Avoiding technical debt in agent architecture
  12. Planning for multi-year automation runway
Module 8. Managing Risk in AI-Driven Processes
Proactively identify and mitigate risks inherent in agent-based systems.
12 chapters in this module
  1. Conducting risk assessments for agent deployment
  2. Mapping potential failure points in agent logic
  3. Assessing data drift and model degradation risks
  4. Planning for agent behavior in edge cases
  5. Implementing real-time monitoring for anomalies
  6. Designing circuit breakers for rogue agents
  7. Evaluating third-party agent dependencies
  8. Testing agent resilience under stress conditions
  9. Reviewing legal exposure from agent decisions
  10. Securing agent communication channels
  11. Auditing agent access logs regularly
  12. Preparing incident response plans for AI failures
Module 9. Measuring Impact and Demonstrating Value
Track and report the true impact of AI and automation initiatives.
12 chapters in this module
  1. Defining KPIs for agent performance and reliability
  2. Measuring end-to-end cycle time improvements
  3. Tracking reduction in manual intervention rates
  4. Calculating cost avoidance from automation
  5. Assessing customer satisfaction with agent interactions
  6. Evaluating agent accuracy over time
  7. Monitoring for unintended process side effects
  8. Reporting automation ROI to executive leadership
  9. Comparing actual outcomes to forecasted benefits
  10. Adjusting metrics as automation matures
  11. Using data to justify further investment
  12. Demonstrating value beyond cost-cutting narratives
Module 10. Aligning AI Strategy with Business Goals
Ensure automation efforts support broader organizational objectives.
12 chapters in this module
  1. Linking automation initiatives to strategic priorities
  2. Evaluating alignment with customer experience goals
  3. Connecting agent outcomes to sustainability targets
  4. Balancing innovation speed with operational stability
  5. Incorporating automation into long-range planning
  6. Adjusting AI roadmap based on market shifts
  7. Engaging executives in automation prioritization
  8. Translating technical progress into business terms
  9. Revisiting automation strategy quarterly
  10. Synchronizing AI goals with budget cycles
  11. Planning for workforce transitions due to automation
  12. Communicating strategic automation wins company-wide
Module 11. Leading Change in an Automated Environment
Drive cultural adoption and organizational learning around AI integration.
12 chapters in this module
  1. Articulating a vision for human-agent collaboration
  2. Building trust in agent recommendations
  3. Managing fear of job displacement proactively
  4. Celebrating early automation successes visibly
  5. Creating forums for sharing automation experiences
  6. Incorporating automation literacy into training
  7. Recognizing teams that adapt well to agents
  8. Addressing ethical concerns about automation
  9. Fostering psychological safety in hybrid teams
  10. Encouraging experimentation within guardrails
  11. Developing leadership skills for AI oversight
  12. Sustaining momentum after initial rollout
Module 12. Sustaining and Evolving Your Automation Function
Build a lasting capability for managing AI and automation over time.
12 chapters in this module
  1. Evaluating the maturity of your automation practice
  2. Planning for ongoing agent maintenance and updates
  3. Rotating staff through automation roles for depth
  4. Investing in internal automation talent development
  5. Refreshing automation strategy with new insights
  6. Reassessing vendor relationships annually
  7. Updating governance policies with experience
  8. Incorporating lessons from automation failures
  9. Benchmarking against evolving industry standards
  10. Planning for next-generation agent capabilities
  11. Ensuring automation adapts to regulatory changes
  12. Handing off ownership to successors effectively

Frequently asked

Who is this course designed for?
Senior leaders who own the function responsible for integrating AI and automation into core operations, including workflow redesign, agent deployment, and system governance.
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, strategy, governance, and decision-making — not coding, infrastructure, or product configuration.
Will I receive support during the course?
Yes. You will have access to a dedicated support channel for questions about applying the frameworks to your context.
Can I share the course materials with my team?
The course is licensed per individual learner. Team licensing is available upon request.
Is there a certificate of completion?
Yes. Upon finishing all modules, you will receive a digital credential confirming completion.
What if the course isn’t right for me?
We offer a 30-day money-back guarantee if you find the content does not meet your needs.
How soon can I start?
Access to the learning environment is provisioned within 24 hours of purchase.
Are the templates customizable?
Yes. All downloadable templates are provided in editable formats for adaptation to your organization.
Do I need prior AI experience?
No. The course is designed for leaders navigating AI adoption, regardless of technical background.
What is the hand-built implementation playbook?
A custom-built guide delivered alongside course access, tailored to help you apply the frameworks to your specific automation challenges.
How long do I have access to the course?
Lifetime access is included, with updates to core content as practices evolve.
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 45 minutes per chapter, with flexibility to move at your own pace. Total estimated engagement is 60 to 70 hours over 12 weeks..

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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