Day 1: Assessing Enterprise AI Opportunities and Readiness

Value of Enterprise AI Applications

• Define problem and beneficiary first
• Assess benefits costs operational impacts
• Compare expected value with alternatives
• Define measurable outcomes for review

Enterprise AI Data Readiness

• Establish practical current-state baseline
• Validate assumptions about existing capabilities
• Distinguish critical gaps from enhancements
• Develop focused readiness improvement plan

Selecting Problems for AI Applications

• Frame problem before choosing technology
• Clarify users decisions and outcomes
• Check feasibility using data constraints
• Rank use-cases by measurable value

Day 2: Engineering Use Cases, Models, and Data Inputs

Designing AI and ML Use Cases

• Differentiate ideas from real needs
• Define target decision or task
• Estimate data effort and complexity
• Choose use-cases for responsible testing

Enterprise AI Model and Tool Selection

• Define problem selected tool solves
• Compare features with required capabilities
• Consider integration security maintainability together
• Recommend option using transparent criteria

Enterprise AI Data and Input Preparation

• Identify data needed for analysis
• Check completeness consistency and timeliness
• Address quality issues before use
• Build data-quality checklist for dataset

Day 3: Validating Models and Integrating AI into Operations

Enterprise AI Output Validation

• Define acceptance criteria before review
• Check consistency across repeated outputs
• Flag limitations requiring further validation
• Apply validation checklist before acceptance

Enterprise AI Operational Integration

• Define technical operational integration requirements
• Assign ownership for system dependencies
• Test data movement and handoffs
• Build concise example for review

Enterprise AI Human Oversight

• Identify decisions needing human judgment
• Structure clear human system handoffs
• Limit reliance on automated recommendations
• Document effective interaction workflow

Day 4: Managing AI Risk, Security, and Performance

Enterprise AI Ethics Bias and Transparency

• Identify possible workflow bias sources
• Assess impacts across user groups
• Record assumptions behind automated recommendations
• Refer sensitive uses for review

Enterprise AI Data Privacy and Security

• Assess privacy impacts before implementation
• Distinguish public internal restricted information
• Apply retention and deletion requirements
• Review access to sensitive information

Measuring AI Application Value

• Select measures linked to outcomes
• Define baselines and meaningful targets
• Review variation before final conclusions
• Use indicators to prompt action

Day 5: Scaling Machine Learning across the Organization

Enterprise AI Adoption and Trust

• Map adoption risks by stakeholder
• Equip leaders to model behaviors
• Build support for early adopters
• Monitor meaningful adoption signals

Enterprise AI Pilot and Responsible Scale

• Test concept with representative users
• Record failures and operating exceptions
• Refine controls using pilot lessons
• Plan scaling through controlled waves

Enterprise AI Application Roadmap

• Sequence application actions into stages
• Assign application owners and deadlines
• Link dependencies with required decisions
• Define evidence confirming stage completion

• Evidence-based discussion of value of enterprise AI applications in realistic settings
• Structured exercises preparing data and developing inputs for participant work contexts
• Decision simulations testing data security and usage privacy against defined criteria
• A final workshop consolidating learning into an enterprise AI roadmap with pilots and scaling stages