AI Spend Planning Excel Template

AI spend management is the process of planning, tracking and controlling the costs associated with artificial intelligence tools and services. These costs can include software licenses, user seats, API consumption and other usage-based charges.

Start by identifying the AI tools and vendors your organization expects to use, then estimate their pricing, number of users and expected consumption. An AI budget should also account for how adoption and usage may change throughout the year.

An AI budget can include enterprise software licenses, user seats, API usage, consumption-based services and other recurring AI technology expenses. Some vendors may also use hybrid pricing that combines fixed licenses with variable usage costs.

An AI spend tracker can centralize vendor costs, monthly spending limits, pricing models and usage assumptions. This makes it easier to compare planned spending across AI providers and understand the total cost of your AI technology portfolio.

AI spend forecasting starts with your expected number of users, vendor pricing and anticipated usage. You can then model how those costs may change throughout the year as adoption increases, decreases or varies by quarter.

Seat-based pricing charges for each user or license, while consumption-based pricing charges according to how much of an AI service is used. Some vendors use hybrid pricing models that combine both.

AI budgeting is often a shared responsibility between Finance and IT. Finance can help manage budgeting, forecasting and financial controls, while IT can provide context around vendor selection, technology usage and organizational requirements.

Yes. The Vena AI Spend Planning Template is a free Excel template designed to help organizations structure, model and forecast their AI spending.

This template is designed for Finance, FP&A and IT teams responsible for managing AI investments and technology budgets.

It can help teams that are:

• Building an annual AI budget
• Evaluating multiple AI vendors
• Managing AI software and API costs
• Forecasting adoption and usage
• Trying to prevent unexpected AI cost overruns
• Aligning Finance and IT on AI investment decisions