Course Objective & Scope
Many organisations struggle to move promising AI pilots into enterprise-wide production due to unexpected cost spikes, integration bottlenecks, security blocks and weak adoption outside the pilot group. This course teaches leaders how to make disciplined post-pilot scaling decisions through a structured investment and readiness gate review.
It covers validating whether pilot results generalise, simulating full-volume unit economics (tokens, APIs, infrastructure, support), passing architecture and security gates, and establishing an L1/L2/LLMOps support model. Participants assemble a scaling decision dossier and rollout plan for a live or upcoming pilot.
Who Should Attend?
- Executives and business sponsors making post-pilot investment and rollout decisions
- AI programme managers, AI CoE leads and product owners
- Enterprise architecture, IT operations, information security and finance teams
- Operations and change management leads responsible for enterprise rollout
Key Takeaways
- Challenge whether pilot evidence generalises to enterprise-wide volumes and user groups
- Model full-scale unit economics across token usage, infrastructure, licensing and support
- Audit pre-production security, privacy, governance and SLA readiness gates
- Make objective Scale, Pivot or Retire decisions using a structured scorecard
- Build a phased rollout, operational support and benefit-realisation plan
Curriculum
Challenging Pilot Evidence and Generalisability
- Accounting for pilot enthusiasm (Hawthorne effect) and cohort selection bias
- Reviewing actual achievement across business, operational and quality KPIs
- Surfacing unresolved edge cases and technical debt accumulated during the pilot
Full-Scale Unit Economics and Revised ROI
- Simulating token, inference, vector database and API costs at 10x–100x volume
- Cost optimisation levers: model routing, semantic caching and smaller models
- Updating TCO and payback period projections with real pilot data
Enterprise Architecture, Security and Governance Gate
- Authentication, role-based access control (RBAC), audit logging and DLP checks
- High availability, API rate limits, latency SLAs and fallback resilience
- Finalising legal, privacy and responsible AI sign-offs
Operational Handover, Support and LLMOps / MLOps Readiness
- Transitioning from project team to production support (L1, L2, L3 ownership)
- Continuous quality monitoring, drift detection and recurring eval pipelines
- Managing upstream model deprecations and version upgrades
Scale, Pivot or Retire Decision Framework and Rollout Plan
- Investment committee decision scorecard and conditional sign-off triggers
- Phased rollout plan across business units, regions or customer segments
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