The Art of CTO AI Adoption Strategy Framework assesses AI maturity, evaluates build-vs-integrate decisions per use case, models token economics and infrastructure costs, and generates governance gap analyses for engineering organizations.
Frequently Asked Questions
Where should engineering teams start with AI adoption?
Start with AI-assisted developer productivity tools (code completion, code review, test generation) — they have immediate ROI, low risk, and build organizational comfort with AI. Next, identify 2-3 product use cases where AI adds clear customer value (content generation, search/recommendations, anomaly detection). Evaluate build vs integrate for each: use APIs for non-differentiating features, invest in custom models only for core competitive advantages. Avoid the trap of building AI infrastructure before you have validated use cases.
How do you model AI infrastructure costs?
AI costs break into three categories: API costs (token-based pricing — model input/output volume at current rates with 20% growth buffer), compute costs (GPU instances for self-hosted models — typically $2-5/hour for inference, $10-30/hour for training), and operational costs (monitoring, evaluation, data pipeline maintenance — typically 30-50% of direct compute costs). The break-even point between API and self-hosted typically falls around 1-5M tokens/day depending on the model. Factor in engineering time for self-hosted maintenance — it is often the hidden cost that makes APIs cheaper longer than expected.