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GEN1797 Mastering AI Agents and Automation at Work

$199.00
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The Executive Diagnostic and Governance Toolkit

Mastering AI Agents and Automation at Work

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.
The work that falls between systems is breaking your teams.

The situation this is built for

Every day your people route tickets, re-enter data, chase approvals, and reconcile outcomes across platforms that don’t talk to each other. This invisible work—unowned, undocumented, and unrewarded—consumes time, erodes morale, and creates errors. It’s not a technology gap. It’s a governance gap. And now AI agents are emerging that can perform these tasks autonomously, creating pressure to act. But without a clear assessment, you risk either ceding control or resisting change that’s already reshaping the function.

Who this is for

Senior leaders responsible for operations, service delivery, or internal enablement who manage cross-system workflows and own outcomes for processes that span departments and tools.

Who this is not for

Individual contributors looking for coding tutorials, technical AI practitioners, or procurement teams evaluating software platforms.

What you walk away with

  • Map where AI agents can own end-to-end workflows
  • Define governance for AI-driven process ownership
  • Anticipate changes in human roles and oversight
  • Evaluate process readiness for agent integration
  • Lead transformation without relying on vendor claims

How this maps to your situation

  • Work that falls between systems
  • Tasks requiring manual data re-entry
  • Processes with unclear ownership
  • High-frequency, low-complexity decision points

Before vs. after

Before
Overwhelmed by invisible coordination work, reacting to breakdowns, and unsure where AI fits in your function's future.
After
Confident in your ability to assess, govern, and lead the integration of AI agents into core workflows.

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 over 12 weeks with reflection and team input. Total time commitment: 36 hours.

If nothing changes
Continuing to rely on manual routing and re-keying will erode team capacity, increase error rates, and leave your function unable to respond as AI agents redefine expectations for speed, accuracy, and ownership in workflow execution.

How this compares to the alternatives

Unlike generic automation courses or vendor-led training, this program focuses exclusively on the strategic and operational realities of integrating AI agents into workflows where ownership is fragmented and systems don’t connect. It does not teach coding or platform configuration. It teaches leadership, assessment, and governance for the work AI agents now perform.

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 Agent-Ready Workflow
Establish a foundation for identifying workflows that are ripe for AI agent ownership based on repetition, handoff frequency, and system fragmentation.
12 chapters in this module
  1. Identifying workflows that span multiple disconnected systems
  2. Mapping the lifecycle of a cross-platform process task
  3. Recognizing patterns of manual data re-entry and translation
  4. Assessing frequency and volume of task handoffs
  5. Documenting where human intervention breaks flow
  6. Measuring time spent on coordination versus execution
  7. Classifying tasks by cognitive load and decision complexity
  8. Spotting recurring exceptions in routine processes
  9. Evaluating ownership ambiguity in multi-system workflows
  10. Tracking escalation paths for stalled process steps
  11. Calculating error rates from manual re-keying activities
  12. Defining the minimum viable process for agent takeover
Module 2. Diagnosing System Silos and Handoff Friction
Analyze how data and tasks move between systems and where breakdowns occur due to lack of ownership or integration.
12 chapters in this module
  1. Tracing the journey of a single record across platforms
  2. Identifying points where data format translation occurs
  3. Detecting delays caused by manual approval routing
  4. Mapping who initiates, owns, and closes each handoff
  5. Analyzing ticket lifecycle across departments and tools
  6. Quantifying time lost waiting for inter-system responses
  7. Documenting workarounds used to bridge system gaps
  8. Assessing consistency of data entry across teams
  9. Reviewing audit trails for missing or fragmented logs
  10. Evaluating SLA adherence across handoff boundaries
  11. Observing communication patterns during process stalls
  12. Classifying handoffs by risk and remediation cost
Module 3. Defining Process Ownership in the Age of Agents
Clarify who is accountable for outcomes when AI agents perform tasks traditionally managed by people.
12 chapters in this module
  1. Distinguishing between task performance and process ownership
  2. Establishing accountability for agent-driven decisions
  3. Defining escalation paths when agents fail to resolve
  4. Setting performance benchmarks for autonomous execution
  5. Creating ownership models for hybrid human-agent teams
  6. Documenting authority levels for agent-initiated actions
  7. Reviewing compliance requirements for agent activity
  8. Designing oversight roles for continuous monitoring
  9. Aligning agent behavior with service level agreements
  10. Handling liability for errors made by AI agents
  11. Integrating agent logs into incident review processes
  12. Balancing autonomy with organizational control
Module 4. Assessing Readiness for AI Agent Integration
Evaluate the maturity of your processes and infrastructure to determine where AI agents can be safely and effectively deployed.
12 chapters in this module
  1. Auditing process documentation for completeness and clarity
  2. Measuring variability in how tasks are executed
  3. Assessing availability of structured inputs for agents
  4. Reviewing access controls across integrated systems
  5. Evaluating error recovery mechanisms in current workflows
  6. Testing data consistency across source and target systems
  7. Determining if decisions follow explicit rules or heuristics
  8. Identifying dependencies on human judgment or context
  9. Mapping API availability and reliability for agent use
  10. Analyzing historical data quality for training agents
  11. Assessing change management capacity for agent rollout
  12. Defining exit criteria for agent-assisted processes
Module 5. Designing Human-Agent Collaboration Models
Structure effective partnerships between people and AI agents to maintain quality and adaptability.
12 chapters in this module
  1. Defining roles for humans in agent-supervised workflows
  2. Establishing thresholds for human-in-the-loop review
  3. Designing feedback loops from agents to human leads
  4. Creating protocols for agent learning from corrections
  5. Setting up joint performance dashboards for teams and agents
  6. Developing playbooks for agent-human handover moments
  7. Training staff to interpret and challenge agent output
  8. Planning for agent behavior drift over time
  9. Designing agent interfaces for quick human override
  10. Incorporating agent insights into team retrospectives
  11. Balancing efficiency gains with skill retention
  12. Measuring trust levels in agent recommendations
Module 6. Governance of Autonomous Process Execution
Build oversight frameworks that ensure AI agents operate reliably, ethically, and in alignment with business goals.
12 chapters in this module
  1. Establishing approval workflows for agent deployment
  2. Creating version control for agent logic and rules
  3. Setting audit standards for agent decision trails
  4. Defining refresh cycles for agent training data
  5. Implementing monitoring for anomalous agent behavior
  6. Documenting agent permissions and access boundaries
  7. Reviewing agent actions during compliance audits
  8. Enforcing data privacy rules in agent operations
  9. Managing agent updates without disrupting workflows
  10. Tracking agent performance over time and conditions
  11. Requiring agent explainability for critical decisions
  12. Involving legal and risk teams in agent governance
Module 7. Measuring the Impact of Agent Adoption
Define and track metrics that reflect real improvements in workflow efficiency and quality.
12 chapters in this module
  1. Selecting KPIs that reflect end-to-end process health
  2. Measuring cycle time reduction after agent integration
  3. Tracking error rate changes in agent-handled tasks
  4. Calculating headcount hours freed by agent automation
  5. Assessing customer satisfaction with agent-managed flows
  6. Monitoring first-contact resolution with agent support
  7. Evaluating agent consistency across similar cases
  8. Benchmarking cost per transaction pre and post agent
  9. Analyzing rework rates in agent-closed cases
  10. Measuring team capacity for higher-value work post agent
  11. Comparing escalation frequency before and after
  12. Validating data integrity in agent-updated records
Module 8. Planning for Organizational Adaptation
Prepare teams and structures for shifts in responsibility and skill requirements caused by AI agent integration.
12 chapters in this module
  1. Identifying roles most affected by agent deployment
  2. Redesigning job descriptions to include agent oversight
  3. Developing training plans for managing AI agents
  4. Planning career paths in an agent-augmented environment
  5. Communicating changes to teams without causing alarm
  6. Establishing centers of excellence for agent operations
  7. Encouraging experimentation with agent capabilities
  8. Creating forums for sharing agent performance insights
  9. Recognizing contributions to agent improvement efforts
  10. Managing resistance to change in process ownership
  11. Aligning incentives with agent-supported outcomes
  12. Supporting leadership adaptation to new workflows
Module 9. Integrating Agents Across Service Delivery Chains
Extend AI agent capabilities across interconnected services and customer journeys.
12 chapters in this module
  1. Mapping customer journey touchpoints with system ownership
  2. Identifying service chain segments with handoff delays
  3. Designing agent handovers between service stages
  4. Ensuring data continuity across service lifecycle phases
  5. Synchronizing agent behavior with customer expectations
  6. Coordinating agent actions across internal departments
  7. Managing SLAs across agent-handled service segments
  8. Aligning agent logic with customer communication tone
  9. Tracking resolution ownership across service transitions
  10. Designing fallback paths when agents cannot proceed
  11. Incorporating customer feedback into agent tuning
  12. Validating end-to-end service quality with agents involved
Module 10. Building Resilience in Agent-Driven Workflows
Ensure reliability and continuity when AI agents are central to process execution.
12 chapters in this module
  1. Designing redundancy for critical agent functions
  2. Creating manual override procedures for agent failures
  3. Testing agent behavior under high-volume conditions
  4. Simulating system outages affecting agent operations
  5. Documenting fallback workflows for agent downtime
  6. Monitoring agent performance degradation over time
  7. Establishing alert thresholds for abnormal patterns
  8. Planning for agent retraining after process changes
  9. Securing backup data sources for agent inputs
  10. Reviewing agent decisions during incident post-mortems
  11. Validating agent responses to edge-case scenarios
  12. Ensuring business continuity with agent dependencies
Module 11. Scaling Agent Use Across Functions
Expand successful agent implementations to other areas while maintaining control and consistency.
12 chapters in this module
  1. Identifying transferable patterns from pilot workflows
  2. Assessing readiness of new functions for agents
  3. Standardizing agent interaction templates across teams
  4. Creating shared libraries of agent decision rules
  5. Developing onboarding checklists for new agent uses
  6. Establishing cross-functional agent governance boards
  7. Measuring consistency of agent behavior by domain
  8. Aligning agent language and tone across functions
  9. Managing version drift in replicated agent logic
  10. Tracking resource demands as agent use scales
  11. Evaluating central vs local agent management models
  12. Documenting lessons from early agent adopters
Module 12. Leading the Transition to Agent-Augmented Operations
Provide strategic direction and leadership to guide your organization through the shift to AI agent integration.
12 chapters in this module
  1. Articulating a vision for human-agent collaboration
  2. Setting strategic priorities for agent adoption
  3. Balancing innovation with operational stability
  4. Engaging executives in agent governance discussions
  5. Communicating progress and setbacks transparently
  6. Incorporating agent impact into strategic planning
  7. Leading by example in agent-augmented workflows
  8. Sponsoring cross-functional agent initiatives
  9. Fostering a culture of responsible automation
  10. Reframing success metrics for agent-enabled teams
  11. Preparing the organization for future agent evolution
  12. Reviewing and refining the agent strategy annually

Frequently asked

Who is this course designed for?
Senior leaders responsible for processes that span systems and teams, where manual coordination creates delays and errors.
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 own outcomes, not for engineers or developers.
Will I learn to build AI agents?
No. You will learn to assess where agents should operate, how to govern them, and how to lead teams through the transition.
What deliverables come with the course?
A complete implementation playbook, templates for process assessment, and frameworks for governance and measurement.
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 over 12 weeks with reflection and team input. Total time commitment: 36 hours..

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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