AI Management - OBERHAHN (Page 10)

AI Efficiency Benchmarking Across Engineering Teams

AI efficiency benchmarking is how an organization turns AI spend from a set of isolated bills into a comparison that

Proving AI ROI to a board is a different exercise from showing that AI is being used. A chart of

AI capacity planning is the discipline of matching AI supply to AI demand before the mismatch becomes an outage or

An AI incident postmortem is the document that decides whether an agent failure becomes an isolated event or a recurring

The 2024 AI spend forecast for most organizations was wrong in the same direction: too low. The methodology that produced

Benchmarks create accountability. Without a reference point, every AI cost number is either reassuringly low or alarmingly high depending on

The pattern is consistent enough to be almost predictable. An organization discovers its AI spend has grown substantially — sometimes 2x,

Token costs are a measure of consumption. They tell you what you spent but not what you got. An AI

Most 2025 AI budgets were wrong. Not because the people who built them were incompetent — because AI spend does not

When AI costs become significant enough to warrant formal allocation, most organizations face a choice between two models: chargeback, where

Governance has a reputation problem in engineering organizations. It reads as process, and process reads as slow. But the organizations

The engineers who manage AI API costs well are not the ones who use AI less. They are the ones