OpenAI GPT 6.1 Terra

Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.

AI spending is difficult to forecast because costs depend on both technical choices and how employees and customers actually use the technology. An application that succeeds can become more expensive quickly: every additional question, document, or automated task may generate a charge, and long or complex requests often cost more. Prices and demand also change. Providers revise fees, introduce new models, and offer discounts with different conditions, while scarce computing capacity can push up infrastructure costs. Meanwhile, spending extends beyond vendor bills: preparing data, integrating systems, checking accuracy, protecting sensitive information, and meeting regulatory obligations all require people and money. To gain control, set a budget for each use case and assign an accountable owner. Track the full cost per useful outcome, such as a resolved customer enquiry, rather than simply counting subscriptions. Start with limited pilots, measure savings and quality, and expand only when the results justify the expense. Use spending alerts and limits, and review supplier contracts for pricing changes and exit costs. Choose less expensive models for routine tasks, reserve premium options for demanding work, and reduce unnecessary requests. Finally, forecast a range of scenarios rather than a single number, including rapid adoption and unexpected compliance costs, and revisit those forecasts regularly.