01Build the argument around operating evidence
The paper separates adoption from enterprise impact, technical activity from verified outcomes, and released capacity from realized cash savings. Externally sourced claims are mapped to a source ledger and publication limitations.
02Convert the thesis into three engines
AI TCO covers implementation, recurring operations, human review, rework, infrastructure, governance, and risk. Verified outcomes separate first-pass, reviewed, corrected, failed, and unresolved work. The business-case engine adds baseline comparison, ROI, payback, NPV, break-even, and capacity.
03Expose every assumption
The calculator labels evidence quality and returns “not comparable” or “not calculable” instead of zero. Capacity never becomes cash by default, expected loss is protected from double counting, and the tool never recommends headcount reduction.
04Make the system maintainable
Dense factual graphics become semantic HTML. Formula versions, dated pricing snapshots, pure calculation modules, and exported inputs make every result reproducible.