AI ROI & Cost Model

AI economics have two sides executives must hold together. On the cost side: model usage is metered per unit of text processed, bigger models cost multiples of smaller ones, and routing routine volume to small models typically halves the bill with no visible quality loss. On the value side: benefits show up as capacity, speed, quality or risk reduction — and only convert to money if the freed capacity is actually redeployed or hiring slows.

Executives should care because both sides are routinely mismanaged. Costs balloon when every task defaults to the flagship model; benefits evaporate in 'hours saved' slides that never touch a budget line. Credible cases pair a measured baseline with a metric the business already respects — cost per claim, days to close, revenue per head — and state their assumptions openly.

Concrete example: an insurer measures cost per processed claim before and after AI summarisation: down 18%, backlog cleared in six weeks, and a 15% volume rise absorbed with zero new hires. That is a case a CFO funds.

Watch the trend line too: capability per dollar keeps improving fast, so last year's economics are a floor, not a ceiling.