Why most enterprise AI pilots never reach production
Analyst data puts enterprise AI pilot failure near 88%, yet the cause is rarely the model. It is scope chosen without a baseline, no owner for the workflow, and no plan for the cases that break the happy path.
The CFO's AI scorecard: measuring real ROI in the first 12 months
Most AI projects fail the CFO test not because they didn’t work but because nobody measured them in finance terms. Four buckets — revenue, cost, risk, capability — each with a baseline, a target, and a 30/60/90 cadence so the answer in month twelve doesn’t rest on storytelling.
The token economics of scale: keeping AI costs flat as usage 10×s
Token cost grows linearly with usage. Five well-known levers — model routing, prompt caching, response budgets, batch APIs, eval-driven downgrades — compound to flatten that curve. Most teams pull them out of order. The eval suite is the prerequisite for the biggest savings.
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