N11-1: The Build vs Buy Decision Framework
The definitive framework for deciding when to build AI in-house vs buying from vendors.
🎯 What You'll Learn
- ✓ Apply the differentiation test
- ✓ Calculate true TCO
- ✓ Assess vendor lock-in costs
- ✓ Build the decision matrix
Lesson 1: The Core vs Context Test
If AI is a core differentiator in your product (the reason customers choose you), build it. If it's context (a feature that enhances but doesn't define your product), buy it. Most companies get this wrong because they confuse "interesting engineering" with "strategic differentiation."
The AI capability that customers cite as the primary reason they buy.
AI capabilities that enhance the product but are not the primary value proposition.
Would the CEO describe this AI to investors as the company's competitive advantage?
List all AI capabilities in your product. Apply the Core vs Context test to each. How many are truly core?
Lesson 2: True TCO Calculation
The "build" cost is never just the ML engineer salary. True TCO includes: ML engineering team (salaries + benefits + management overhead) + GPU/cloud compute + data collection and annotation + evaluation infrastructure + monitoring and observability + ongoing retraining + opportunity cost of not building other features. The true TCO is typically 3-5x the visible engineering cost.
ML engineer salaries and cloud compute bills.
Recruiting, management, evaluation, monitoring, retraining, infrastructure.
Features NOT built because engineers are maintaining the ML system.
Calculate the full TCO of your most complex in-house AI system. Include all hidden costs. Compare to the vendor alternative.
Lesson 3: The Decision Matrix
Plot each AI capability on a 2×2 matrix: Strategic Importance (low/high) vs Internal Capability (low/high). High Strategic + High Capability = Build. Low Strategic + Low Capability = Buy. High Strategic + Low Capability = Partner (buy now, build later). Low Strategic + High Capability = Open Source.
High strategic importance AND high internal capability.
Low strategic importance AND low internal capability.
High strategic importance BUT low internal capability.
Map all your AI capabilities onto the 2×2 decision matrix. Share with your CTO and CPO for alignment.
Continue Learning: Track 11 — Economics of Build vs Buy
2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.
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Executive Dashboards
Generate deterministic, board-ready financial artifacts to justify CAPEX workflows immediately to your CFO.
Defensible Economics
Replace heuristic guesswork with hard mathematical frameworks for build-vs-buy and SLA penalty negotiations.
3-Step Playbooks
Actionable remediation templates attached to every module to neutralize friction and drive instant deployment velocity.
Engineering Intelligence Awaiting Extraction
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Module Syllabus
Lesson 1: Lesson 1: The Core vs Context Test
If AI is a core differentiator in your product (the reason customers choose you), build it. If it's context (a feature that enhances but doesn't define your product), buy it. Most companies get this wrong because they confuse "interesting engineering" with "strategic differentiation."
Lesson 2: Lesson 2: True TCO Calculation
The "build" cost is never just the ML engineer salary. True TCO includes: ML engineering team (salaries + benefits + management overhead) + GPU/cloud compute + data collection and annotation + evaluation infrastructure + monitoring and observability + ongoing retraining + opportunity cost of not building other features. The true TCO is typically 3-5x the visible engineering cost.
Lesson 3: Lesson 3: The Decision Matrix
Plot each AI capability on a 2×2 matrix: Strategic Importance (low/high) vs Internal Capability (low/high). High Strategic + High Capability = Build. Low Strategic + Low Capability = Buy. High Strategic + Low Capability = Partner (buy now, build later). Low Strategic + High Capability = Open Source.