Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Building every AI use case from scratch wastes time and talent, while buying off-the-shelf SaaS for everything can strip away competitive differentiation and create severe vendor lock-in. This course provides a systematic framework for choosing the right sourcing and development strategy per use case.

Core-versus-context analysis, hybrid architectures (foundation model APIs plus proprietary RAG/agent layers), RFP criteria and contract terms are examined through real cases. Participants map their organisation's current AI requests onto a Build/Buy/Partner decision matrix and produce a sourcing roadmap.

Who Should Attend?

  • CIOs, CTOs, CDOs and enterprise architecture leaders
  • IT procurement, vendor management and legal/contract managers
  • Business unit directors and AI product owners
  • Strategy and corporate development managers

Key Takeaways

  • Classify use cases by competitive differentiation and strategic criticality
  • Compare full in-house build, turnkey SaaS, cloud AI platforms and co-development partnerships
  • Prepare technical, security and commercial RFP questionnaires for AI vendors
  • Define contract terms for IP ownership, training data restrictions and exit strategies
  • Create a multi-layered Build/Buy/Partner decision matrix for the organisation's use case portfolio

Curriculum

Strategic Positioning: Core vs. Context Analysis
  • Separating differentiating core capabilities from standard support processes
  • Balancing time-to-value against internal engineering capacity
  • Deciding which layer of the AI stack (model, data, orchestration, UI) to own
Sourcing Archetypes: Build, Buy, Partner and Hybrid Models
  • Turnkey enterprise SaaS and embedded copilot solutions (Buy)
  • In-house development on cloud AI platforms and foundation model APIs (Hybrid Build)
  • Co-development with systems integrators, boutique AI firms and academia (Partner)
Cost, Technical Debt and Vendor Lock-in Analysis
  • Three-year comparison of seat/usage licensing versus internal build and maintenance
  • Portability across prompts, data schemas and orchestration (model-agnostic design)
  • Assessing vendor viability and market consolidation risks
AI Vendor Selection, RFPs and Contract Management
  • Avoiding the demo trap: structured PoCs and benchmarks on proprietary data
  • Data privacy, zero-data-retention, KVKK and security evaluation criteria
  • Contract clauses for IP ownership, SLAs, price caps and exit strategies
Decision Matrix Workshop
  • Scoring 5–8 real organisational AI scenarios in a multi-criteria decision tool
  • Reconciling IT, business unit and procurement perspectives
  • Producing agreed Build/Buy/Partner decision cards

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