The same architecture judgment I bring to engagements, written down. What separates an AI demo from a system that survives real users, real data, and an audit.
Framework
The serious 2025 research, from MIT to McKinsey, BCG, and EY, converges on one shape: AI adoption is outrunning the integration, governance, and operating maturity that turn it into value, and only about 5% of companies capture it. Two axes, the problem a system solves and the identity it is allowed to assume, map that gap into four quadrants and tell you what to buy, what to build, and what survives production.
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Economics
Selling AI outcomes instead of software seats means someone absorbs the compute — and 80-90% SaaS margins don't survive that. Seven places cost hides, from orchestration loops that quietly run up five-figure bills to the human review that sneaks back into "autonomous" systems. The math worth running before you commit to building or buying.
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Case study
How safety becomes an operating system in a high-trust mental-health context: a risk-detection policy, a context-aware architecture that decouples safety from the conversation, audit-ready data infrastructure, and a validation discipline.
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