A tailored course, built for your situation
Tailored Operating Model for AI Integration
A structured path to embed AI into your operating model with clarity and control
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI initiatives stall not because of technology, but because operating models aren’t designed to support them.
The situation this course is for
Leaders launch AI pilots with excitement, but without a clear operating model, they fizzle out. Roles are unclear, decisions stall, and value never scales. The result? Fragmented efforts, wasted resources, and skepticism from teams who’ve seen the cycle before.
Who this is for
A strategic operator who values structure, clarity, and execution. You’ve worked with TOMs before and now face pressure to integrate AI without chaos. You don’t want hype, you want a system that works.
Who this is not for
This is not for those looking for technical AI training or data science upskilling. It’s also not for leaders seeking quick keynote inspiration without follow-through.
What you walk away with
- Define clear roles and decision rights for AI governance
- Align AI initiatives with business outcomes and operating rhythm
- Build a scalable AI operating model that evolves with maturity
- Integrate AI into existing processes without disruption
- Create visibility and accountability across teams and functions
The 12 modules (with all 144 chapters)
Module 1. Diagnose AI Readiness
Assess organizational maturity across leadership, data, and process dimensions to identify gaps and readiness for AI integration.
12 chapters in this module
- Map current AI awareness levels
- Evaluate data infrastructure readiness
- Identify decision-making bottlenecks
- Assess change tolerance capacity
- Benchmark against peer patterns
- Define operating model scope
- Clarify executive sponsorship
- Audit existing digital initiatives
- Pinpoint integration risks
- Determine pace of adoption
- Classify AI use case maturity
- Set baseline metrics
Module 2. Define AI Governance
Establish clear governance structures that balance speed, risk, and accountability across AI initiatives.
12 chapters in this module
- Design governance tiers
- Assign decision rights
- Create escalation paths
- Define ethics guardrails
- Set approval workflows
- Introduce review cadence
- Link to compliance standards
- Clarify ownership model
- Embed auditability
- Standardize documentation
- Balance central and local control
- Enable cross-functional input
Module 3. Align Roles and Teams
Clarify responsibilities across business, IT, and data functions to ensure seamless collaboration in AI execution.
12 chapters in this module
- Define core AI roles
- Map team interdependencies
- Clarify RACI for AI projects
- Integrate product management
- Align with IT strategy
- Bridge business and tech
- Establish center of excellence
- Design team onboarding
- Create handoff protocols
- Enable feedback loops
- Scale team structures
- Manage external partners
Module 4. Integrate AI into Processes
Embed AI capabilities into existing workflows without disruption, ensuring adoption and measurable impact.
12 chapters in this module
- Identify integration points
- Map current process flows
- Pinpoint automation triggers
- Design human-AI handoffs
- Test integration logic
- Adjust process ownership
- Update training materials
- Monitor performance shifts
- Refine escalation rules
- Track adoption metrics
- Optimize for usability
- Scale successful pilots
Module 5. Design Operating Rhythm
Create a cadence for reviewing, refining, and scaling AI initiatives across the organization.
12 chapters in this module
- Set review meeting structure
- Define KPIs and dashboards
- Align with planning cycles
- Integrate into leadership forums
- Track initiative health
- Surface risks early
- Celebrate progress visibly
- Adjust priorities dynamically
- Link to budget cycles
- Report to stakeholders
- Maintain momentum
- Adapt based on feedback
Module 6. Build Change Capability
Develop internal capacity to manage AI-driven change at scale, reducing resistance and increasing adoption.
12 chapters in this module
- Assess change readiness
- Identify change champions
- Design communication plan
- Create feedback channels
- Run pilot adoption cycles
- Measure sentiment shifts
- Address misinformation
- Enable two-way dialogue
- Scale learning loops
- Reinforce new behaviors
- Adjust messaging tone
- Sustain engagement
Module 7. Scale AI Use Cases
Develop a repeatable method to identify, prioritize, and scale AI use cases across functions.
12 chapters in this module
- Source use case ideas
- Apply business impact filter
- Assess technical feasibility
- Prioritize with scoring model
- Validate with stakeholders
- Design pilot scope
- Define success criteria
- Track value realization
- Document lessons learned
- Replicate proven patterns
- Adjust for context
- Retire underperformers
Module 8. Embed Data Governance
Ensure data quality, access, and compliance support AI initiatives without creating bottlenecks.
12 chapters in this module
- Define data ownership
- Classify data sensitivity
- Set access controls
- Map data lineage
- Ensure quality standards
- Monitor usage patterns
- Integrate metadata
- Enforce retention rules
- Align with privacy laws
- Enable self-service safely
- Audit data flows
- Update policies regularly
Module 9. Manage AI Risks
Proactively identify and mitigate operational, ethical, and reputational risks in AI deployment.
12 chapters in this module
- Identify bias risks
- Assess model explainability
- Test for fairness
- Monitor drift over time
- Create fallback plans
- Define incident response
- Train on risk scenarios
- Audit model decisions
- Update risk register
- Engage legal early
- Communicate transparently
- Review third-party models
Module 10. Finance AI Initiatives
Develop funding models that support experimentation while ensuring accountability and ROI tracking.
12 chapters in this module
- Define funding mechanisms
- Set budget allocation rules
- Track cost per use case
- Measure value realization
- Apply stage-gate funding
- Link to business outcomes
- Report financial impact
- Optimize resource spend
- Balance exploration and delivery
- Adjust funding dynamically
- Retire low-value projects
- Scale high-impact bets
Module 11. Measure AI Performance
Build a balanced scorecard to track AI impact across operational, financial, and strategic dimensions.
12 chapters in this module
- Define success metrics
- Track adoption rates
- Measure efficiency gains
- Assess quality improvements
- Monitor cost savings
- Evaluate customer impact
- Gather user feedback
- Benchmark against goals
- Update KPIs regularly
- Visualize performance
- Report to leadership
- Adjust targets
Module 12. Evolve the Operating Model
Create a feedback-driven process to continuously refine the AI operating model as capabilities mature.
12 chapters in this module
- Collect operating insights
- Review model effectiveness
- Identify improvement areas
- Test structural changes
- Update governance rules
- Adjust team roles
- Refine decision rights
- Scale what works
- Retire outdated rules
- Update documentation
- Communicate changes
- Sustain evolution
How this maps to your situation
- You’re launching AI pilots but lack governance
- Teams are siloed and misaligned on AI roles
- AI initiatives start strong but stall mid-way
- Leadership demands clarity on AI ROI and control
Before vs. after
Before
AI feels chaotic, initiatives start without clear ownership, governance, or path to scale. Teams are misaligned, and leadership questions value.
After
AI runs like clockwork, governed, measured, and embedded. Every team knows their role, decisions are clear, and value compounds over time.
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 week over 12 weeks, designed for busy professionals who need structure without overload.
If nothing changes
Without a tailored operating model, AI efforts will remain fragmented, underfunded, and disconnected from business outcomes, wasting time, money, and momentum.
How this compares to the alternatives
Unlike generic AI courses, this program is built around operating model design, connecting governance, roles, and process to ensure AI delivers real, sustained value.
Frequently asked
Is this course technical?
No. It’s designed for leaders and operators. No coding or data science required.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for my industry?
Yes. The operating model principles are sector-agnostic and adaptable to any context.
$199 one-time. Approximately 3 hours per week over 12 weeks, designed for busy professionals who need structure without overload..
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·
144 chapters·
Hand-built playbook included·
Account access within 24 hours