AI TRAINING
Make-vs-Buy Decision Framework for AI
Leave with a repeatable framework to decide confidently when to build, configure, or buy AI capabilities.
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Run the diagnostic →What it covers
This programme equips technology and transformation leaders with a structured decision framework for evaluating AI sourcing options, custom build, vendor configuration, or off-the-shelf purchase. Participants work through real-world case studies covering total cost of ownership modelling, strategic fit assessment, and risk profiling for each pathway. The format combines facilitated workshops with hands-on TCO spreadsheet exercises and peer benchmarking. By the end, teams can apply a consistent, defensible methodology to any AI investment decision.
What you'll be able to do
- Apply a structured decision matrix to classify any AI initiative as build, configure, or buy within your organisation's context
- Build a defensible TCO model comparing custom AI development against SaaS vendor and open-source alternatives over a 3-year horizon
- Identify the strategic differentiation threshold above which custom AI development is justified
- Assess vendor lock-in risk, data portability, and integration complexity for a shortlisted AI vendor
- Produce a sourcing recommendation document with risk register that can be presented to a board or investment committee
Topics covered
- Build vs configure vs buy decision matrix
- Total cost of ownership (TCO) modelling for AI
- Strategic fit and competitive differentiation analysis
- Vendor evaluation and due diligence for AI products
- Risk profiling: data dependency, lock-in, and scalability
- Integration complexity and internal capability assessment
- Governance and compliance constraints on sourcing choices
- Roadmap sequencing and reversibility planning
Delivery
Typically delivered over two to three days in-person or across four live virtual half-day sessions. Approximately 60% of time is hands-on: participants bring one real AI initiative and work it through the framework during sessions. Materials include a pre-built TCO model template, a decision matrix scorecard, and a vendor due diligence checklist. A facilitated peer-review session allows cross-organisation benchmarking. Remote delivery uses Miro for collaborative framework workshops and shared spreadsheet environments.
What makes it work
- Anchoring the decision in a clearly articulated competitive differentiation thesis before evaluating options
- Using a shared TCO model that finance, engineering, and procurement all co-own
- Building a lightweight governance gate that requires a sourcing rationale document for any AI spend above a defined threshold
- Scheduling a structured re-evaluation trigger (e.g., 18-month review) for every major sourcing decision made
Common mistakes
- Defaulting to custom build because it feels more strategic, without modelling the true long-term maintenance and talent costs
- Evaluating vendors on feature lists alone, ignoring data residency, model transparency, and exit clauses
- Treating the sourcing decision as a one-time event rather than a revisable position as the AI market evolves
- Excluding legal, security, and compliance stakeholders from the decision, leading to late-stage blockers
When NOT to take this
This training is not the right fit for organisations that have already committed budget and signed a vendor contract, at that stage, adoption and change management support is more valuable than a sourcing framework.
Providers to consider
Sources
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