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GEN1797 Leading AI Agents and Automation at Work

$197.00
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The Executive Diagnostic and Governance Toolkit

Leading 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.
You manage the work that slips between systems—routing, chasing, re-keying—because no single team owns the end-to-end flow.

The situation this is built for

Every day, your teams lose hours to manual handoffs, duplicate entries, and stalled requests. You're held accountable for outcomes, yet you don't control the tools or the data flows. The work depends on people remembering to forward emails, update trackers, or flag exceptions. AI agents now perform these tasks autonomously, but no one has mapped what that means for your role, your team, or your operating model.

Who this is for

Senior leaders accountable for cross-functional workflows that span systems and teams—such as service delivery, operations, customer success, or internal enablement—where coordination costs are high and ownership is diffuse.

Who this is not for

Individual contributors building automation scripts, technical AI developers, or vendors selling workflow platforms.

What you walk away with

  • Map the invisible coordination work in your domain
  • Evaluate where AI agents replace or reshape human tasks
  • Define ownership of AI-driven workflows
  • Anticipate shifts in team structure and accountability
  • Lead the transition without disrupting service continuity

How this maps to your situation

  • High coordination load with low system integration
  • Accountability gaps in cross-functional workflows
  • Growing reliance on manual tracking and updates
  • Emerging use of autonomous agents in daily operations

Before vs. after

Before
You inherit fragmented workflows, chase updates across systems, and bear accountability without control over the tools enabling the work.
After
You lead with clarity on where AI agents change task ownership, redefine coordination, and require updated governance—equipped with a plan to guide the transition.

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 for completion over 12 weeks with reflection and team discussion.

If nothing changes
Without deliberate oversight, AI agents will quietly absorb critical coordination tasks, leaving your team bypassed, your metrics outdated, and your influence eroded—while risks in accuracy, compliance, and continuity grow unchecked.

How this compares to the alternatives

Unlike generic automation courses, this program focuses exclusively on the leadership challenges of AI agents in cross-system workflows—addressing ownership, governance, and human coordination, not just technical implementation.

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 Coordination Burden
Examine the daily reality of routing, chasing, and re-keying across systems, and quantify its impact on throughput and quality.
12 chapters in this module
  1. Identifying the most time-consuming handoff points
  2. Measuring delays caused by system boundaries
  3. Tracking where human memory compensates for gaps
  4. Documenting exceptions that require manual intervention
  5. Mapping who currently owns each fragment of work
  6. Assessing the cost of rework due to misrouting
  7. Recognizing patterns in recurring coordination failures
  8. Classifying data formats moving between systems
  9. Evaluating the reliability of current tracking methods
  10. Pinpointing where oversight breaks down
  11. Understanding how service level expectations are impacted
  12. Establishing baseline metrics for coordination load
Module 2. The Role of Autonomous Agents
Define what AI agents do in practice—initiating tasks, monitoring progress, and moving data—without relying on human triggers.
12 chapters in this module
  1. Differentiating agents from traditional automation scripts
  2. Observing how agents respond to new requests
  3. Tracing how agents move data across platforms
  4. Analyzing agent decision points in real scenarios
  5. Reviewing logs of agent-initiated actions
  6. Understanding how agents detect stalled workflows
  7. Identifying when agents escalate to humans
  8. Examining how agents handle authentication
  9. Assessing agent consistency across repetitions
  10. Mapping the scope of agent permissions
  11. Evaluating how agents interpret unstructured input
  12. Documenting agent failure modes and fallbacks
Module 3. Ownership in a World of Agents
Clarify who is accountable when agents perform tasks that were previously distributed across roles.
12 chapters in this module
  1. Defining ownership of agent-generated outputs
  2. Determining who validates agent decisions
  3. Assigning responsibility for agent errors
  4. Clarifying escalation paths for agent outcomes
  5. Reconciling agent actions with team KPIs
  6. Aligning agent behavior with compliance standards
  7. Setting boundaries for agent autonomy
  8. Establishing audit trails for agent activity
  9. Reviewing contractual obligations affected by agents
  10. Updating role descriptions to include agent oversight
  11. Designing handover protocols between agents and people
  12. Creating governance forums for agent performance
Module 4. Workflow Fragmentation Analysis
Break down end-to-end processes into discrete steps and identify where fragmentation creates risk and delay.
12 chapters in this module
  1. Charting the lifecycle of a high-volume request
  2. Identifying steps requiring cross-system verification
  3. Noting where data must be reformatted manually
  4. Tracking points where work waits for human action
  5. Highlighting steps prone to miscommunication
  6. Measuring time spent on status updates
  7. Logging instances of duplicate effort
  8. Observing how priorities shift across teams
  9. Mapping dependencies between functional groups
  10. Assessing variance in processing times
  11. Documenting exceptions that bypass standard paths
  12. Evaluating how feedback loops close
Module 5. Agent Integration Patterns
Study how agents connect systems, including authentication, data transformation, and error handling.
12 chapters in this module
  1. Reviewing how agents authenticate to multiple systems
  2. Analyzing how agents transform data formats
  3. Tracing how agents retry failed operations
  4. Observing how agents handle rate limits
  5. Mapping how agents store intermediate state
  6. Understanding how agents parse email content
  7. Evaluating how agents extract structured data
  8. Assessing how agents manage concurrency
  9. Documenting how agents handle timeouts
  10. Reviewing how agents secure sensitive fields
  11. Examining how agents log actions for review
  12. Testing how agents respond to schema changes
Module 6. Human-Agent Collaboration Models
Design how people and agents share responsibility for task completion and decision-making.
12 chapters in this module
  1. Defining when agents should wait for human input
  2. Setting thresholds for agent escalation
  3. Designing interfaces for agent status visibility
  4. Creating templates for human override
  5. Establishing response time expectations for agents
  6. Mapping how agents notify stakeholders
  7. Reviewing how agents summarize progress
  8. Designing feedback mechanisms for agent learning
  9. Clarifying who trains agent decision rules
  10. Documenting how agents handle ambiguous requests
  11. Planning for agent downtime and coverage
  12. Evaluating agent explanations for decisions
Module 7. Governance of Autonomous Systems
Build oversight frameworks for monitoring, auditing, and updating agent behavior at scale.
12 chapters in this module
  1. Setting up dashboards for agent performance
  2. Scheduling regular reviews of agent logs
  3. Defining criteria for agent updates
  4. Establishing change control for agent logic
  5. Creating incident response plans for agent errors
  6. Designing access controls for agent configuration
  7. Implementing version tracking for agent rules
  8. Reviewing compliance with data regulations
  9. Auditing agent decisions for fairness
  10. Monitoring for unintended side effects
  11. Documenting agent dependencies and risks
  12. Planning for agent deprecation and retirement
Module 8. Change Management for Agent Rollout
Prepare teams for shifts in workflow, responsibility, and performance expectations when agents are introduced.
12 chapters in this module
  1. Assessing team readiness for agent collaboration
  2. Communicating changes in role expectations
  3. Training staff on interacting with agents
  4. Updating onboarding materials to include agents
  5. Revising performance metrics to reflect automation
  6. Addressing concerns about job impact
  7. Celebrating early wins with agent support
  8. Gathering feedback from frontline users
  9. Adjusting workflows based on agent performance
  10. Managing resistance to new interaction models
  11. Reinforcing accountability in hybrid workflows
  12. Tracking adoption rates across teams
Module 9. Service Level Redefinition
Reassess response times, resolution expectations, and quality standards in light of agent capabilities.
12 chapters in this module
  1. Measuring current end-to-end cycle times
  2. Identifying bottlenecks agents can resolve
  3. Setting new benchmarks for request handling
  4. Redefining SLAs with agent participation
  5. Aligning customer expectations with automation
  6. Adjusting internal service commitments
  7. Monitoring adherence to updated timelines
  8. Evaluating quality of agent-generated outputs
  9. Balancing speed with accuracy in service delivery
  10. Handling exceptions to automated timelines
  11. Reporting on service improvements due to agents
  12. Updating escalation procedures for automated flows
Module 10. Risk and Reliability Assessment
Evaluate the stability, security, and resilience of agent-driven workflows under real conditions.
12 chapters in this module
  1. Identifying single points of failure in agent design
  2. Testing agent behavior under high load
  3. Reviewing data privacy implications of agent actions
  4. Assessing exposure from third-party integrations
  5. Evaluating agent recovery from system outages
  6. Documenting fallback procedures for agent failure
  7. Monitoring for unauthorized data access
  8. Reviewing agent logging completeness
  9. Testing input validation for malicious content
  10. Assessing impact of agent errors on downstream systems
  11. Evaluating agent consistency across environments
  12. Planning for disaster recovery scenarios
Module 11. Scaling Agent Operations
Plan for expanding agent use across teams, systems, and geographies while maintaining control.
12 chapters in this module
  1. Prioritizing workflows for agent expansion
  2. Assessing infrastructure readiness for scale
  3. Designing templates for new agent deployment
  4. Standardizing naming and tagging conventions
  5. Building centralized monitoring for all agents
  6. Creating shared libraries of agent rules
  7. Establishing cross-team coordination forums
  8. Managing agent performance across regions
  9. Ensuring language and localization support
  10. Scaling training data for agent improvement
  11. Optimizing resource usage as volume grows
  12. Reviewing cost implications of agent scaling
Module 12. Leading the Transition Strategically
Integrate insights from the course into a leadership action plan for managing the shift to agent-supported work.
12 chapters in this module
  1. Synthesizing findings from workflow analysis
  2. Prioritizing high-impact agent opportunities
  3. Aligning leadership on transition goals
  4. Securing executive sponsorship for change
  5. Developing a phased implementation roadmap
  6. Allocating budget for agent operations
  7. Measuring progress against strategic objectives
  8. Adjusting governance as agents evolve
  9. Communicating vision to stakeholders
  10. Institutionalizing lessons from early pilots
  11. Planning for continuous agent refinement
  12. Defining long-term success metrics

Frequently asked

Who is this course designed for?
Senior leaders accountable for end-to-end workflows that span systems and teams, where coordination costs are high and ownership is diffuse.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover technical AI development?
No, it focuses on leadership, governance, and operational impact, not coding or model training.
Will I learn how to select AI vendors?
No, the course centers on understanding the work, not evaluating external platforms.
Is there a team discount available?
Yes, contact us directly for multi-seat licensing options.
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 for completion over 12 weeks with reflection and team discussion..

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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