You CAN Manage, Forecast, and Evaluate AI Costs

This article provides CFO guidance on managing AI costs from a financial perspective, emphasizing that companies can control expenses and measure ROI despite rapid AI evolution.

• Choose appropriately-sized models for tasks; 95% of enterprises overspend by using expensive models for simple work
• Implement caching to reduce redundant processing by 40%-60% for repeated queries
• Optimize prompts as code to achieve 30%-50% cost reductions without sacrificing quality
• Separate real-time and batch workloads to lower per-inference costs
• Phase AI investment: fixed foundation costs first, then variable inference costs with declining unit economics
• Measure business outcomes (time saved, errors avoided) rather than token costs
• Use consumption-based pricing for flexibility as usage patterns evolve

Organizations that succeed with AI connect spending directly to business results and maintain flexibility to scale investments based on demonstrated ROI.