AI Budgeting Mistakes Businesses Make and How to Avoid Them

AI Investments on the Rise

Businesses are increasingly investing in Artificial Intelligence (AI). As indicated by a Gartner survey, the majority of finance leaders are raising their technology budgets for 2026, with financial services firms leading the way with a 15% increase.

AI Cost Forecasting Challenges

The decision to invest in AI has been made, but a challenging question remains: how do you predict a cost that varies based on the usage of the tool? Many companies struggle to answer this, which can impact how they build their AI budget.

Common Mistakes in AI Budgeting

Companies often make two specific errors when budgeting for AI. It’s important to understand these before finalizing your AI budget.

Mistake one: Budgeting AI as a Fixed Cost

Unlike traditional software expenses that are consistent each month, AI costs are consumption-based, not headcount-based. This means you can’t set a fixed annual budget, as AI usage increases as employees find more uses for the tool.

Mistake two: Ignoring Hidden AI Cost

The vendor’s invoice isn’t the only cost associated with AI. There are additional expenses, such as reviewing output, training staff, and maintaining governance, that don’t appear on the bill but are still part of the total cost.

Budgeting for Honest AI Returns

Finance leaders should base their funding on measured outcomes, rather than vendor promises, as less than 1% of executives report AI returns of 20% or greater. Meanwhile, a significant number of AI projects are abandoned after the proof of concept stage.

Building an Effective AI Budget

To build a realistic AI budget, start with a pilot program to understand the cost per task. Use this data to forecast costs based on realistic adoption rates. Then, add in the hidden costs associated with AI. Revisit and revise your budget regularly to accommodate changing prices and usage.