Course Objective & Scope
AI projects do not follow the linear path of deterministic software delivery; data surprises, experimental iterations and shifting model behaviour in production demand a distinct management discipline. This course demystifies the end-to-end lifecycle of both predictive machine learning (CRISP-DM / CPMAI) and generative AI or agent projects for business and project leaders.
It examines the inputs, deliverables, team roles and stage-gates of every phase from discovery through production deployment and ongoing monitoring. In a case study adapted to the organisation, participants build an AI project governance plan with stage-gate checklists and MLOps/LLMOps handover criteria.
Who Should Attend?
- Project managers, Scrum Masters, Agile coaches and PMO leaders
- Product managers overseeing AI-powered products and internal tools
- Department leads coordinating across data science, software engineering and business units
- IT and digital transformation portfolio managers
Key Takeaways
- Manage the differences between deterministic software delivery and probabilistic AI lifecycles
- Establish clear stage-gates across discovery, PoC, pilot, deployment and ongoing operations
- Plan iteration and evaluation cycles for generative AI, RAG and fine-tuning projects
- Verify pre-production readiness across security, compliance, unit cost and support
- Connect post-launch drift monitoring, quality regressions and model updates to an operating model
Curriculum
How AI Differs from Traditional Software: Probabilistic and Experimental Delivery
- Comparing CRISP-DM, CPMAI and modern GenAI/LLMOps lifecycles
- Planning with timeboxed discovery spikes instead of rigid upfront specifications
- Orchestrating hand-offs between business, data, engineering and compliance teams
Discovery, Data Preparation and Iterative Experimentation
- Scheduling problem framing, data access and security clearances realistically
- Iterative loops across prompt engineering, RAG pipelines, model selection and fine-tuning
- Offline evaluation (evals) and structured error analysis
Validation, System Integration and Production Readiness
- API integration with enterprise systems, access permissions and load testing
- Legal, privacy, information security and responsible AI review gates
- User acceptance testing (UAT) and operations team enablement
Live Operations, MLOps / LLMOps Monitoring and Lifecycle Maintenance
- Monitoring data drift, provider model updates and quality regressions
- Tracking token/infrastructure spend, user feedback and incident response
- Triggering retraining, prompt updates or system decommissioning
Stage-Gate Checklists and Project Governance Blueprint
- Mandatory artefacts and sign-off matrices at each lifecycle gate
- Building a tailored lifecycle checklist for an internal AI initiative
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