Year one of AI deployment, budgets are straightforward. You are buying licenses, paying for implementation, running pilots. The costs are visible and the categories make sense.
Year two is where the budget mistakes happen, and they all follow the same pattern. The organization treats AI tools as a fixed line item rather than as infrastructure that requires ongoing investment to maintain its value.
Here is what that looks like in practice.
The licenses renew automatically. That part gets handled. But the work that actually keeps the AI useful, the prompt refinement, the document hygiene, the index maintenance, the workflow adjustments as the business changes, gets absorbed invisibly into whoever happens to be paying attention to the tools. Usually that is one or two people who care, doing it on the side of their actual job description, without any formal recognition that this work is happening or any protection for the time it requires.
When those people get busy with other priorities, which happens at some point to everyone, the AI tools quietly degrade. The knowledge base gets stale. The prompts stop being updated to reflect how the business has changed. The retrieval starts returning outdated information. Users start trusting the tools less. Adoption slips. By the time leadership notices, the problem has been building for months.
