AI Implementation Practitioner

Course Modules

• 1
  Implementation Methodology OverviewThe Agentive 5-phase AI implementation framework from discovery through optimization.
• 2
  Requirements and ScopingTranslating business goals into clear AI system requirements and scope boundaries.
• 3
  Data Assessment and PreparationEvaluating data quality, identifying gaps, and preparing datasets for AI use.
• 4
  Vendor Selection and ManagementRFP development, vendor evaluation, contract negotiation, and ongoing vendor management.
• 5
  Pilot Design and ExecutionRunning time-boxed, hypothesis-driven AI pilots that generate real learning.
• 6
  Change ManagementStakeholder mapping, communication planning, training, and adoption measurement.
• 7
  Integration ArchitectureAPI design, system integration patterns, and avoiding common integration pitfalls.
• 8
  Testing and Quality AssuranceAI-specific testing approaches — performance, bias, adversarial, and user acceptance testing.
• 9
  Launch and MonitoringProduction deployment, monitoring dashboards, and alerting for AI system health.
• 10
  Optimization and Continuous ImprovementFeedback loops, model retraining triggers, and continuous improvement frameworks.