Course Objective & Scope
In many organisations an AI Center of Excellence (AI CoE) either tries to build every project itself and becomes a bottleneck, or turns into an ivory tower producing policy documents nobody reads. This course teaches leaders how to build and run an enabling AI CoE that accelerates business units through shared assets, standards and hands-on coaching.
The AI CoE charter, service catalogue, reusable prompt/agent/eval template library, internal consulting model and first 100-day plan are worked through with practical examples. Participants draft the AI CoE charter, staffing plan and service model for their own organisation.
Who Should Attend?
- Managers appointed or nominated to lead an AI Center of Excellence
- CDOs, CIOs, CTOs and digital transformation directors
- Data science, AI engineering and analytics team managers
- Leaders responsible for enterprise architecture and delivery standards
Key Takeaways
- Write an AI CoE charter defining mission, mandate boundaries and success criteria
- Balance delivery, standard-setting and enablement modes to avoid becoming a bottleneck
- Design a shared asset library of reusable prompts, RAG blueprints, guardrails, evals and project templates
- Define the core AI CoE roles (AI architect, product lead, responsible AI specialist, adoption lead)
- Establish KPIs that measure CoE impact through reuse rate, time-to-production and business satisfaction
Curriculum
Why an AI CoE? Anti-Patterns and Success Factors
- Avoiding the ivory tower, delivery bottleneck and shadow-IT traps
- Shifting from a centralized delivery factory to an enablement engine
- Executive sponsorship and organisational placement of the AI CoE
AI CoE Charter and Service Catalogue
- Mission, in-scope/out-of-scope boundaries and engagement rules with business units
- Four core services: opportunity discovery, architectural accelerators, quality/risk review, capability building
- Engagement modes: co-build, coaching and self-service assurance
Shared Asset Library and Technical Accelerators
- Approved models, prompt templates, MCP/tool connectors and reference RAG/agent blueprints
- Standard eval suites, security checklists and PoC starter kits
- Publishing assets through an internal portal or reusable repository
Team Structure, Roles and Internal Community Links
- Core CoE roles: CoE Lead, Solution Architect, LLMOps Engineer, Responsible AI Specialist, Adoption Lead
- Operating cadence between the CoE and business-unit AI Champions
- Managing external vendors and implementation partners through the CoE
First 100-Day Plan and Measuring CoE Performance
- Credibility-building deliverables for the first 100 days
- CoE KPIs: PoC-to-production lead time, asset reuse rate, unit cost reduction
- Workshop: drafting the organisation's AI CoE charter and service catalogue
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