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GEN7369 Leading AI Agents and Automation for Senior Leaders

$197.00
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The Executive Diagnostic and Governance Toolkit

Leading AI Agents and Automation for Senior Leaders

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 the routing, chasing and re-keying between systems that nobody owns.

$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 the only one who sees the whole picture — yet nothing moves without your push.

The situation this is built for

Work gets stuck between systems no one owns. Tasks fall through cracks. People re-enter data, re-send requests, re-explain context. You’re the one who notices. You’re the one blamed. You’re the one with no mandate to fix it. The work is invisible until it fails. Now AI agents promise to automate — but no one knows who decides what runs, where, and why.

Who this is for

Senior leaders responsible for cross-system workflows that require coordination, data movement, and decision-making across siloed teams and platforms.

Who this is not for

Individual contributors building AI tools, technical founders, or teams focused only on implementing vendor solutions without strategic oversight.

What you walk away with

  • Map the full lifecycle of agent-driven work across systems
  • Assess automation readiness across departments and processes
  • Define ownership models for workflows no single team controls
  • Prioritize changes based on risk, cost, and scalability
  • Lead decisions about automation without relying on technical specialists

How this maps to your situation

  • Current state assessment
  • Ownership and accountability
  • Readiness evaluation
  • Future state design

Before vs. after

Before
Tasks stall between systems. You chase updates, re-enter data, and resolve gaps no one owns. Automation feels like more noise.
After
You see the full flow, define clear ownership, and lead changes with confidence. Work moves — you don’t have to.

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 to 5 hours per module, designed to be completed over 12 weeks with reflection and application built into each chapter.

If nothing changes
Without clarity and ownership, automation will deepen fragmentation. More agents will mean more blind spots, more errors, and more pressure on you to fix what breaks — while you get blamed for lack of progress.

How this compares to the alternatives

Unlike generic process improvement courses or technical AI training, this course is built for senior leaders who own complex, cross-system work but lack direct control over teams or tools. It focuses on observation, decision-making, and governance — not coding, configuration, or vendor selection.

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 the Current State of Workflows
Document the reality of how tasks, data, and decisions move today — including the invisible labor that keeps things running.
12 chapters in this module
  1. Identifying recurring tasks that require manual intervention
  2. Mapping data movement across disconnected systems
  3. Recognizing where employees re-enter the same information
  4. Tracking how requests get lost between departments
  5. Observing patterns in follow-up and escalation
  6. Documenting workarounds used to bypass system gaps
  7. Noting where context is lost during handoffs
  8. Analyzing time spent on coordination versus execution
  9. Pinpointing where approvals create bottlenecks
  10. Evaluating reliance on email and messaging for task routing
  11. Assessing consistency of data across platforms
  12. Creating a timeline of a single work item from start to close
Module 2. Defining Ownership in Distributed Work
Clarify who is accountable for each stage of a process when no single team owns the end-to-end flow.
12 chapters in this module
  1. Distinguishing between functional and system ownership
  2. Identifying where responsibility is shared or undefined
  3. Analyzing escalation paths when things go wrong
  4. Defining decision rights for cross-team workflows
  5. Mapping stakeholders by influence and authority
  6. Assessing how handoffs are documented or assumed
  7. Clarifying roles in hybrid human-agent processes
  8. Establishing accountability for data accuracy
  9. Determining who owns resolution when automation fails
  10. Reviewing existing service-level agreements
  11. Creating a RACI model for multi-system workflows
  12. Setting expectations for response times and outcomes
Module 3. Assessing Automation Readiness Across Functions
Evaluate which parts of your workflow are ready for automation and which require stabilization first.
12 chapters in this module
  1. Evaluating process maturity before automation
  2. Measuring variability in task execution
  3. Identifying repeatable decisions suitable for delegation
  4. Assessing data quality and availability
  5. Determining system accessibility for integration
  6. Reviewing error rates in current manual steps
  7. Analyzing frequency and volume of tasks
  8. Classifying tasks by risk and compliance impact
  9. Determining human oversight requirements
  10. Evaluating team capacity to support change
  11. Assessing tolerance for automation failures
  12. Creating a readiness scorecard for each workflow
Module 4. Mapping the Role of AI Agents in Daily Work
See how AI agents already operate in your environment — as assistants, routers, or decision-makers.
12 chapters in this module
  1. Identifying existing AI-driven notifications and alerts
  2. Tracking automated suggestions in business software
  3. Observing bot participation in communication channels
  4. Documenting AI-generated summaries of meetings or emails
  5. Noting instances of predictive routing in ticketing systems
  6. Analyzing AI-influenced prioritization of tasks
  7. Reviewing automated report generation and distribution
  8. Identifying AI-based data validation steps
  9. Mapping AI agents that escalate or reassign work
  10. Assessing reliance on AI for content creation
  11. Evaluating AI interpretation of unstructured input
  12. Cataloging AI agents operating without formal oversight
Module 5. Evaluating Data Movement and System Interfaces
Understand how data flows between systems and where AI agents depend on unstable connections.
12 chapters in this module
  1. Listing all systems involved in core workflows
  2. Identifying data fields transferred between platforms
  3. Mapping transformation rules during data transfer
  4. Noting where manual reconciliation is required
  5. Assessing frequency and timing of data syncs
  6. Documenting API usage and limitations
  7. Identifying duplicate data entry points
  8. Analyzing data loss during system transitions
  9. Reviewing error handling in failed transfers
  10. Evaluating schema mismatches across systems
  11. Tracking latency in cross-system updates
  12. Assessing security controls on data movement
Module 6. Identifying Hidden Dependencies in Workflows
Uncover the informal connections and assumptions that keep processes running — and create risk when broken.
12 chapters in this module
  1. Noting individuals who act as human routers
  2. Identifying shadow systems like spreadsheets and emails
  3. Mapping undocumented approval chains
  4. Tracking reliance on personal knowledge
  5. Observing workarounds for access restrictions
  6. Documenting tribal knowledge in task execution
  7. Identifying single points of failure in coordination
  8. Analyzing informal escalation paths
  9. Reviewing unofficial data sources
  10. Assessing reliance on temporary fixes
  11. Noting undocumented integrations
  12. Evaluating ad hoc communication channels
Module 7. Assessing Risk in Agent-Driven Processes
Determine where automation introduces new failure modes or compliance exposure.
12 chapters in this module
  1. Identifying decisions made without audit trail
  2. Assessing transparency of AI reasoning
  3. Evaluating bias in automated recommendations
  4. Reviewing compliance with data handling rules
  5. Mapping fallback procedures during outages
  6. Assessing change management for agent updates
  7. Identifying lack of version control in workflows
  8. Evaluating access controls for AI agents
  9. Reviewing logging and monitoring capabilities
  10. Assessing impact of incorrect automation on downstream steps
  11. Evaluating rework costs when agents fail
  12. Determining accountability for automated errors
Module 8. Designing Human-Agent Collaboration Models
Define how people and AI agents will share responsibility in future workflows.
12 chapters in this module
  1. Defining handoff points between humans and agents
  2. Establishing rules for agent autonomy levels
  3. Setting thresholds for human review
  4. Designing feedback loops for agent learning
  5. Creating escalation protocols for edge cases
  6. Determining training requirements for staff
  7. Designing dashboards for agent performance
  8. Setting expectations for agent response times
  9. Defining roles in mixed-initiative workflows
  10. Establishing review cycles for agent output
  11. Creating documentation standards for agent actions
  12. Designing onboarding for new agent capabilities
Module 9. Building Cross-System Workflow Integrity
Ensure reliability and consistency in processes that span multiple platforms and teams.
12 chapters in this module
  1. Defining end-to-end success criteria for workflows
  2. Establishing data validation rules at each stage
  3. Creating unique identifiers for work items
  4. Implementing tracking across systems
  5. Designing alerts for stalled or missing steps
  6. Standardizing status definitions across teams
  7. Implementing audit trails for all changes
  8. Creating reconciliation procedures for discrepancies
  9. Enforcing data ownership at each stage
  10. Designing rollback procedures for failed automation
  11. Establishing metrics for workflow health
  12. Implementing periodic integrity checks
Module 10. Prioritizing Automation Initiatives Strategically
Choose where to act first based on impact, feasibility, and organizational readiness.
12 chapters in this module
  1. Assessing potential time savings per process
  2. Estimating error reduction from automation
  3. Evaluating cost of delay for improvements
  4. Assessing team capacity for change adoption
  5. Identifying quick wins with high visibility
  6. Mapping dependencies between automation opportunities
  7. Prioritizing based on risk exposure
  8. Aligning initiatives with strategic goals
  9. Evaluating cross-functional benefits
  10. Assessing vendor independence of solutions
  11. Creating a sequencing plan for implementation
  12. Building a business case for leadership approval
Module 11. Leading Governance for Distributed Automation
Establish oversight that ensures safety, consistency, and accountability without slowing innovation.
12 chapters in this module
  1. Defining roles in automation governance
  2. Creating a register of active AI agents
  3. Establishing change approval processes
  4. Setting standards for agent documentation
  5. Implementing monitoring for unintended behavior
  6. Creating incident response plans
  7. Establishing review cycles for agent performance
  8. Defining decommissioning procedures
  9. Enforcing security and access policies
  10. Creating transparency for stakeholders
  11. Establishing feedback mechanisms from users
  12. Reviewing ethical implications of automation
Module 12. Creating an Implementation Roadmap
Turn assessment and decisions into a clear plan for action with milestones and accountability.
12 chapters in this module
  1. Defining success metrics for each initiative
  2. Setting timelines for pilot and rollout
  3. Assigning owners for each phase
  4. Identifying required resources and support
  5. Creating communication plans for teams
  6. Developing training materials for new workflows
  7. Establishing feedback collection during testing
  8. Designing phased deployment schedules
  9. Building monitoring into early stages
  10. Creating documentation for future maintenance
  11. Scheduling post-implementation reviews
  12. Planning for continuous improvement cycles

Frequently asked

Who is this course for?
Senior leaders responsible for workflows that span multiple systems and teams, especially where coordination breaks down and no single person owns the end-to-end process.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course require technical knowledge?
No. It is designed for leaders who need to understand and guide automation, not build or code it.
Will I learn about specific AI tools or platforms?
No. The course focuses on the work, not the vendors or technology brands behind it.
What will I have at the end?
A clear assessment of your function’s automation state, a decision framework, and a customized implementation roadmap.
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 to 5 hours per module, designed to be completed over 12 weeks with reflection and application built into each chapter..

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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