AIの2年目に多くの企業が犯す予算の誤り

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 The retrieval starts returning outdated information.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. The budget mistake is treating the technology cost as the whole cost and ignoring the labor cost of keeping the technology valuable. The fix is not complicated but it requires an explicit decision. Someone needs to own AI infrastructure in the same way someone owns IT infrastructure. That ownership needs a job description, not just goodwill. The time required needs to be in that person's workload, not in addition to it. And the budget conversation in year two needs to include a line item for maintenance labor, not just license renewal. How much time is actually required depends on the scale of the deployment. For a 100-person company with a few AI tools that are genuinely embedded in workflows, I typically see meaningful AI infrastructure maintenance running about 10 to 15 hours per week across whoever is doing it. That is a real cost. In most companies I have looked at, nobody has formally accounted for it. The organizations that sustain AI value over multiple years are the ones that recognized early that they were building infrastructure, not buying software. Infrastructure requires ongoing investment to remain useful. The ones that did not recognize this have a graveyard of AI tools that worked well in year one and quietly became unreliable in year two. 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. The budget mistake is treating the technology cost as the whole cost and ignoring the labor cost of keeping the technology valuable. The fix is not complicated but it requires an explicit decision. Someone needs to own AI infrastructure in the same way someone owns IT infrastructure. That ownership needs a job description, not just goodwill. The time required needs to be in that person's workload, not in addition to it. And the budget conversation in year two needs to include a line item for maintenance labor, not just license renewal. How much time is actually required depends on the scale of the deployment. For a 100-person company with a few AI tools that are genuinely embedded in workflows, I typically see meaningful AI infrastructure maintenance running about 10 to 15 hours per week across whoever is doing it. That is a real cost. In most companies I have looked at, nobody has formally accounted for it. The organizations that sustain AI value over multiple years are the ones that recognized early that they were building infrastructure, not buying software. Infrastructure requires ongoing investment to remain useful. The ones that did not recognize this have a graveyard of AI tools that worked well in year one and quietly became unreliable in year two.