2.3.2. Build, Buy, or Extend Decisions for AI Components
Every AI component in the solution requires a sourcing decision. The architect must evaluate whether to build custom, buy a third-party solution, or extend an existing Microsoft capability. The exam frames this as "Analyze whether to build, buy, or extend AI components for business solutions."
Decision Framework:
Evaluation Criteria:
| Factor | Build | Buy (Prebuilt/3rd-Party) | Extend |
|---|---|---|---|
| Time to value | Longest (months) | Shortest (days-weeks) | Medium (weeks) |
| Customization | Unlimited | Limited to configuration | Moderate |
| Maintenance | Full responsibility | Vendor responsibility | Shared |
| IP ownership | Full ownership | Licensed | Shared/Licensed |
| Risk | Highest (unproven) | Lowest (proven solution) | Medium |
| Long-term cost | High initially, may decrease | Ongoing licensing | Moderate |
| Talent needed | AI engineers, data scientists | Admins, functional consultants | Developers familiar with platform |
When to Build:
- The requirement is truly unique to the organization
- Competitive advantage depends on proprietary AI capabilities
- No existing solution provides adequate functionality even with extension
- The organization has the talent and infrastructure to sustain custom AI
When to Buy/Use Prebuilt:
- The business process aligns with a standard pattern (customer service, finance reconciliation, demand forecasting)
- Time-to-value is critical
- The organization lacks specialized AI development talent
- Maintenance and updates should be the vendor's responsibility
When to Extend:
- A prebuilt solution covers 60-80% of requirements
- The gaps can be filled through configuration, plugins, or connectors
- The extension points are well-documented and supported
- The total cost of extension is lower than building from scratch
Exam Trap: The exam frequently presents scenarios where the "build custom" option sounds impressive but is unnecessary. A common pattern: the question describes a standard customer service use case and offers "build a custom NLP model" as a distractor, when "configure Copilot Studio with business-specific topics" is the correct answer. Always check whether the requirement can be met through configuration or extension before recommending custom development.
Reflection Question: A retail company wants AI-powered product recommendations. They have a small data science team (3 people) and want the solution live in 8 weeks. They're evaluating: (a) building a custom recommendation model, (b) using D365 Commerce AI features, or (c) integrating a third-party recommendation API. Which do you recommend and why?
