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GEN1797 Mastering AI and Automation for Strategic Leaders

$200.00
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The Executive Diagnostic and Governance Toolkit

Mastering AI and Automation for Strategic 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 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 expected to lead AI adoption, but no one has given you a way to know what to adopt first—or how to prove it matters.

The situation this is built for

Every week brings a new AI tool promising to fix inefficiencies in your team’s work. You’re responsible for sorting signal from noise, yet you lack a consistent way to assess what fits your actual processes. Proposals pile up. Budget meetings loom. You need a framework that starts with your team’s real work—not someone else’s roadmap. Without it, you risk choosing tools that don’t integrate, waste engineering time, or fail to move the needle on outcomes.

Who this is for

A director or senior manager who owns a function where AI and automation are expected to deliver efficiency—such as operations, customer experience, technical program management, or product delivery. They are accountable for throughput, quality, and team productivity, and must make adoption choices under uncertainty.

Who this is not for

This is not for individual contributors implementing AI models, data scientists tuning algorithms, or executives seeking high-level trend summaries. It is for leaders who must translate AI potential into operational decisions.

What you walk away with

  • Assess your team's current AI maturity with precision
  • Prioritize AI use cases based on workflow impact and readiness
  • Create defensible adoption plans for budget and leadership reviews
  • Align cross-functional stakeholders on AI implementation order
  • Avoid costly missteps from premature or mismatched automation

How this maps to your situation

  • Assessment: Understanding current state and readiness
  • Prioritization: Identifying and selecting high-impact opportunities
  • Execution: Designing, integrating, and scaling AI adoption
  • Governance: Managing risk, alignment, and sustainability

Before vs. after

Before
You’re overwhelmed by AI options, lack a consistent way to assess fit, and struggle to justify choices in budget meetings.
After
You have a clear, evidence-based roadmap for AI adoption that aligns with your team’s actual work and decision rights.

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 parallel with ongoing work. Total time investment: 36 hours over 12 weeks if followed sequentially.

If nothing changes
Without a structured way to assess and prioritize AI, teams default to vendor-driven agendas, implement mismatched tools, and fail to demonstrate ROI—leading to wasted resources, eroded trust, and stalled digital transformation.

How this compares to the alternatives

Unlike generic AI overviews or vendor-led training, this course focuses exclusively on the leader’s role in assessing, prioritizing, and governing AI within their function. It does not teach coding or model tuning. It provides actionable frameworks for decision-making, stakeholder alignment, and sustainable integration—tools you won’t find in technical documentation or conference talks.

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 what is currently automated, where gaps exist, and how work flows across systems and people.
12 chapters in this module
  1. Defining the scope of your automation responsibility
  2. Mapping existing tools in your team’s workflow
  3. Identifying manual handoffs that create latency
  4. Assessing error rates in current automated processes
  5. Documenting decision points handled by humans
  6. Measuring time spent on repetitive task execution
  7. Evaluating integration points between systems
  8. Tracking incidents caused by automation failures
  9. Reviewing logs for unattended workflow bottlenecks
  10. Classifying tasks by cognitive load and repetition
  11. Benchmarking against industry workflow patterns
  12. Creating a visual map of your automation ecosystem
Module 2. Diagnosing Team Readiness for AI Adoption
Evaluate your team’s capacity to adopt and sustain AI-driven changes, including skills, trust, and change tolerance.
12 chapters in this module
  1. Assessing technical fluency across team roles
  2. Identifying team members resistant to automation
  3. Evaluating documentation completeness for AI training
  4. Measuring incident response time to AI errors
  5. Determining data access permissions and barriers
  6. Reviewing team feedback on past automation efforts
  7. Testing understanding of AI decision logic
  8. Observing how often humans override automated outputs
  9. Tracking frequency of rework after AI interventions
  10. Evaluating psychological safety in reporting AI mistakes
  11. Assessing training bandwidth for new AI tools
  12. Mapping communication patterns during AI failures
Module 3. Identifying High-Leverage AI Opportunities
Pinpoint where AI can reduce latency, errors, or effort in ways that directly improve outcomes.
12 chapters in this module
  1. Analyzing workflows for repetitive high-volume tasks
  2. Finding decision points with consistent human patterns
  3. Measuring throughput loss due to manual review
  4. Identifying escalations that could be preempted
  5. Locating tasks with high cognitive load but low variability
  6. Tracking where context switching degrades quality
  7. Evaluating tasks prone to fatigue-induced errors
  8. Assessing opportunities for real-time decision support
  9. Mapping customer journeys with abandonment points
  10. Reviewing audit trails for compliance risk hotspots
  11. Calculating cost of delay in current processes
  12. Prioritizing opportunities by operational impact
Module 4. Evaluating AI Fit for Specific Work Patterns
Determine whether an AI solution aligns with the actual structure and variability of your team’s work.
12 chapters in this module
  1. Classifying tasks by rule-based versus judgment-based logic
  2. Assessing input variability in current workflows
  3. Evaluating AI reliability under edge-case conditions
  4. Measuring time to resolve AI-generated false positives
  5. Determining whether AI can handle ambiguous inputs
  6. Reviewing historical data quality for AI training
  7. Testing AI output interpretability for non-experts
  8. Assessing alignment between AI output and workflow needs
  9. Evaluating need for human-in-the-loop oversight
  10. Mapping AI confidence levels to decision risk
  11. Determining fallback procedures for AI uncertainty
  12. Assessing retraining frequency based on drift
Module 5. Building a Defensible AI Adoption Case
Construct evidence-based proposals that withstand scrutiny in budget and leadership reviews.
12 chapters in this module
  1. Defining success metrics for AI implementation
  2. Measuring baseline performance before AI
  3. Estimating time savings from automation pilots
  4. Calculating error reduction potential
  5. Projecting headcount impact of sustained automation
  6. Documenting compliance benefits of AI oversight
  7. Creating before-and-after workflow diagrams
  8. Gathering qualitative feedback from stakeholders
  9. Benchmarking against peer team performance
  10. Aligning AI goals with organizational KPIs
  11. Building financial models for AI ROI
  12. Preparing leadership presentation with evidence
Module 6. Designing Human-AI Collaboration Models
Structure roles and responsibilities so humans and AI systems complement each other effectively.
12 chapters in this module
  1. Defining clear handoff points between AI and staff
  2. Designing escalation paths for AI uncertainty
  3. Assigning ownership for AI output validation
  4. Creating feedback loops for AI improvement
  5. Establishing routines for AI performance review
  6. Setting thresholds for human override authority
  7. Designing dashboards for AI decision transparency
  8. Developing playbooks for AI failure response
  9. Integrating AI alerts into existing communication channels
  10. Defining training requirements for AI interaction
  11. Measuring trust in AI recommendations over time
  12. Evaluating team adaptation to new collaboration patterns
Module 7. Integrating AI into Daily Operational Rhythm
Ensure AI becomes part of regular work patterns rather than a separate initiative.
12 chapters in this module
  1. Embedding AI outputs into daily stand-up reports
  2. Incorporating AI metrics into team dashboards
  3. Scheduling routine reviews of AI performance
  4. Aligning AI alerts with shift handover protocols
  5. Integrating AI suggestions into planning meetings
  6. Updating standard operating procedures with AI steps
  7. Tracking adoption through usage analytics
  8. Measuring time to first meaningful AI interaction
  9. Evaluating consistency of AI use across team members
  10. Identifying workarounds that bypass AI tools
  11. Assessing impact of AI on meeting agendas
  12. Reviewing incident reports for AI-related patterns
Module 8. Managing AI Risk and Compliance Exposure
Anticipate and mitigate risks related to bias, errors, and regulatory scrutiny in AI use.
12 chapters in this module
  1. Auditing AI decisions for demographic fairness
  2. Documenting data provenance for AI inputs
  3. Establishing retention policies for AI logs
  4. Reviewing AI outputs for regulatory compliance
  5. Assessing liability exposure from AI errors
  6. Creating audit trails for AI-driven actions
  7. Evaluating need for AI explainability features
  8. Mapping AI use to data privacy regulations
  9. Testing for model drift over operational time
  10. Conducting tabletop exercises for AI failure
  11. Designing opt-out mechanisms for AI processing
  12. Reviewing third-party dependencies in AI stack
Module 9. Scaling AI Adoption Across Teams
Extend successful AI pilots to broader functions while managing complexity and resistance.
12 chapters in this module
  1. Identifying transferable AI components across units
  2. Assessing readiness of adjacent teams for AI
  3. Creating shared definitions for AI success
  4. Establishing cross-team AI governance forums
  5. Developing templates for AI implementation
  6. Building centralized monitoring for AI performance
  7. Standardizing data formats for AI interoperability
  8. Managing version control for AI models
  9. Coordinating training rollouts across departments
  10. Tracking adoption variance between teams
  11. Resolving conflicting priorities in AI rollout
  12. Evaluating centralization versus autonomy trade-offs
Module 10. Optimizing AI Performance Over Time
Continuously improve AI effectiveness through feedback, iteration, and recalibration.
12 chapters in this module
  1. Setting up automated monitoring for AI accuracy
  2. Creating routines for manual AI validation
  3. Collecting structured feedback on AI outputs
  4. Measuring rework rates after AI intervention
  5. Tracking changes in AI confidence over time
  6. Scheduling periodic model retraining
  7. Evaluating impact of data drift on AI decisions
  8. Updating training data based on edge cases
  9. Benchmarking AI against human performance
  10. Identifying opportunities for AI feature expansion
  11. Assessing cost of maintaining AI infrastructure
  12. Planning for AI model lifecycle retirement
Module 11. Leading Stakeholder Alignment on AI Priorities
Facilitate decision-making across product, engineering, and operations to ensure coherent AI adoption.
12 chapters in this module
  1. Conducting joint workshops to prioritize AI use cases
  2. Mapping AI initiatives to product roadmap timelines
  3. Aligning engineering capacity with AI rollout plans
  4. Creating shared dashboards for AI performance
  5. Facilitating trade-off discussions between speed and safety
  6. Documenting assumptions behind AI investment choices
  7. Establishing escalation paths for AI disputes
  8. Reviewing AI progress in cross-functional forums
  9. Balancing innovation goals with operational stability
  10. Communicating AI benefits to non-technical leaders
  11. Managing expectations around AI capability limits
  12. Building consensus on AI deprecation criteria
Module 12. Sustaining AI Adoption Through Organizational Change
Ensure AI integration endures beyond initial rollout through culture, incentives, and measurement.
12 chapters in this module
  1. Recognizing team members who champion AI use
  2. Incorporating AI metrics into performance reviews
  3. Updating onboarding materials to include AI workflows
  4. Celebrating milestones in AI adoption journey
  5. Measuring changes in team efficiency over time
  6. Revising incentives to reward AI collaboration
  7. Conducting retrospectives on AI implementation
  8. Sharing lessons learned across organizational units
  9. Planning for leadership transitions in AI programs
  10. Evaluating cultural resistance to AI decisions
  11. Assessing long-term engagement with AI tools
  12. Creating a roadmap for next-generation AI capabilities

Frequently asked

Who is this course designed for?
This course is for leaders who own functions where AI and automation are expected to improve outcomes—such as operations, customer experience, or technical program management—and must make adoption decisions under uncertainty.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover technical AI implementation?
No. This course focuses on leadership decisions, assessment frameworks, and operational integration—not coding, model training, or infrastructure setup.
Will I receive support in applying the course to my team?
Yes. The hand-built implementation playbook is tailored to help you apply the course directly to your team’s workflow and decision context.
Can I access the course materials after completion?
Yes. You retain indefinite access to all course content and downloadable resources.
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 parallel with ongoing work. Total time investment: 36 hours over 12 weeks if followed sequentially..

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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