It doesn't really matter if licensing costs go up as long as the cost per unit of work completed stays the same or goes down. But if AI merely adds new costs on top of existing processes without improving anything else, then there's a real problem.
AI tools should not be adopted simply to boost AI adoption metrics or to check the “We use AI” box in a board presentation. They should deliver measurable business results. This could mean accelerating product development, gaining a competitive edge, or achieving the same results with a smaller team. Without measurable results, it is not an investment. It is merely an additional expense.
A good place to start is by measuring the specific aspect you actually want to improve using AI. Without a baseline, you’ll never know what you’ve gained or whether it was worth the cost.
When implementing new systems, companies should avoid jumping to conclusions too soon. Some metrics may even dip temporarily as people get used to new tools and workflows. But if the results don’t improve enough to justify the cost, then changes need to be made.
Maybe people need more guidance and support.
Maybe the tool I chose wasn't the right one.
Or maybe the AI just isn't capable enough for that task yet.
The key point is that with measured data, companies can make informed decisions. This includes the decision to stop using a tool and redirect their AI efforts to areas where the benefits are more evident.
The potential benefits are usually significant enough that companies do not need to optimize every billing model, certificate level, or pricing detail right from the start. First, demonstrate that the benefits are real. Once the value is clear and the tool is here to stay, it makes sense to optimize license tiers, billing cycles, and access models.
And of course, keep track of who is actually using the service.
Before renewing or expanding AI licenses, companies should ask:
Purchasing licenses for everyone when not everyone needs them isn’t particularly efficient. If leadership wants to say that “everyone has access to AI,” then perhaps some roles could use tools without monthly base fees and instead pay only based on actual usage.
Depending on the scale, there may even be a cost advantage to building an internal UI on top of AI APIs rather than paying fixed per-user platform fees.
At some point, companies should probably treat measuring AI ROI with the same level of seriousness as they do cloud costs, consulting expenses, or workforce planning.
