AI Readiness and Trust Training
Equip your executives, leaders, and project managers with the knowledge and tools to successfully lead AI initiatives.
This live virtual training series covers everything from AI readiness and governance to operational excellence, ethical design, strategic adoption, and AI-specific project management.
Whether you’re just starting your AI journey or managing ongoing initiatives, this curriculum provides actionable insights, frameworks, and checklists you can apply immediately.
Please take look at our classes below:
AI Readiness Essentials
Goal: Foundation For All - Understand your organization’s AI readiness across business, data, and compliance.
Key Topics:
- Business readiness: strategy, process, and people.
- Data readiness: quality, access, integration, governance.
- Compliance and regulatory overview.
- Mapping early ROI opportunities and quick wins.
Outcome: A 3-part readiness snapshot identifying gaps and opportunities.
Building Trust in Artificial Intelligence
Goal: Governance & Compliance -Establish foundational principles of AI governance, security, and trust.
Key Topics:
- Data governance vs. AI governance.
- Regulatory frameworks and compliance checkpoints.
- Security and privacy best practices for AI.
- Operationalizing trust: policies, standards, accountability.
Outcome: Checklist of governance measures for immediate application.
Responsible AI by Design
Goal: Ethics & Privacy - Embed ethics, privacy, and fairness into your AI initiatives from day one.
Key Topics:
- Bias detection and mitigation strategies.
- Privacy-by-design principles and data minimization.
- Ethical AI frameworks and governance checkpoints.
- Risk assessment for AI product deployment.
Outcome: A “responsible AI blueprint” for internal teams.
ML & LLM Observability
Goal: Operational Excellence - Learn how to monitor, measure, and optimize ML and LLM performance.
Key Topics:
- Key performance metrics for ML/LLM pipelines.
- Detecting and managing model drift.
- Integrating observability into MLOps LLMOps production workflows.
- Real-world troubleshooting and optimization examples.
Outcome: Observability framework template for AI operations.
Navigating the AI Vendor Landscape
Goal: Strategic Decision-Making -Make informed build, buy, or partner decisions for AI adoption.
Key Topics:
- AI vendor evaluation framework.
- Build vs. buy vs. partner decision matrix.
- Cost, compliance, and integration considerations.
- Risk management for third-party AI products.
Outcome: Decision matrix for evaluating AI vendors.
AI Project Manager Readiness
Goal: PM Execution Skills - Equip project managers with the skills and mindset to lead AI initiatives effectively.
Key Topics:
- Differences between AI/ML projects and traditional IT projects.
- Iterative experimentation vs. fixed-scope delivery.
- Managing data dependencies, model training cycles, and model drift.
- Stakeholder alignment for probabilistic outcomes.
- Risk management for AI-specific failures and biases.
Outcome: PM-specific AI project checklist and risk-prevention guide.
Training Price List
* Prices are per single user. * Group pricing available upon request.
Essentials Pass
$500
per pass
Live, virtual class
Class summary and notes
Q&A after lecture
Pro Readiness Pass
$950
per pass
Live, virtual class
Class summary and notes
Q&A after lecture
30 minutes of consultation anytime after class
Full AI Leadership & PM Journey
$2500
per pass
Live, virtual class
Class summary and notes
Q&A after lecture (up to 30 minutes)
60 minutes of consultation, anytime after the class