In the wake of the rapid development of artificial intelligence and its unstoppable spread, more and more companies and institutions are deciding to support their employees\u2019 work with some form of AI tool. At first glance, it might seem like the obvious solution to grant every member of the organization access to these tools\u2014but corporate AI subscription fees are significant, so this can impose disproportionately high costs on organizations, especially those with larger workforces. <\/p>\n\n
Therefore, leaders typically make decisions based on prior assessment: they strive to determine which employees would derive the greatest value from the tool and allocate subscriptions accordingly, in limited numbers.<\/p>\n\n
The need, therefore, is already established at the moment of implementation: most organizations view artificial intelligence as a valuable resource and, accordingly, strive to optimize its use.<\/p>\n\n
Despite this effort, we often see that the use of allocated subscriptions within an organization is extremely uneven. For some, the tool becomes an integral part of their daily work, while others\u2014even though the organization specifically grants them access\u2014use it only occasionally or not at all. In certain areas\u2014such as writing, analysis, development, and translation\u2014dozens of meaningful interactions per day are realistic, while in other areas, the introduced tool may remain unused for months on end because it is less suited to daily tasks or colleagues\u2019 established work methods. <\/p>\n\n
This quiet asymmetry is one of the often-overlooked challenges of AI adoption in the corporate world. It has no obvious symptoms and is not associated with any specific incidents, so it rarely makes it onto the agenda of executive meetings; yet it is constantly present: the organization is spending money on a tool without having a clear picture of how it is actually being used. <\/p>\n\n
Utilization analyses most often focus on quantitative metrics. However, there is a rarely discussed yet significant cost factor that stems not from the extent of usage but from the manner of usage: specifically, which model colleagues use to perform a given task. <\/p>\n\n
The use of language models is billed based on the number of tokens used, which depends, among other things, on the volume of content processed, the complexity of the task, and the expected output. There are significant differences in the unit prices of individual models, so performing the same task can cost several times more depending on the model chosen. <\/p>\n\n
This is not solely up to the user: to foster informed model usage, it is essential that implementation be supported by a well-thought-out strategy, a regulated framework, and targeted education.<\/p>\n\n
The costs of corporate AI subscriptions are precisely documented and easy to track: the charges are itemized on invoices, so the organization can see and know exactly how much it is spending to support its processes with artificial intelligence. However, the organization typically cannot measure the value created. Managers cannot see how intensively a given subscription is used, for what tasks, or with what results. Since the cost cannot be tied to a specific task or process, it remains unclear which subscriptions are not generating sufficient value and where it would be justified to expand the budget. <\/p>\n\n
In a value-based allocation model, unused budgets are adjusted to actual needs, and the organization can realize significantly greater benefits from the same expenditure at no additional cost.<\/p>\n\n
A prerequisite for responsible management is measurability, and a prerequisite for measurability is transparency: the organization\u2019s use of AI must be presented in a clear, aggregatable format.<\/p>\n\n
As long as this layer is missing, the organization is not managing its subscriptions\u2014it is merely paying for them. The difference is by no means just a matter of semantics. Management means that expenditure is aligned with actual value; mere payment means that resources leave the organization in the hope that they will end up in the right place. <\/p>\n\n
Once it becomes clear across the board who is using the tools, with what intensity, using which model, and for what purpose, resource allocation can become a data-driven management decision: access can be aligned with actual needs, budgets with actual utilization, and tasks with the appropriate models.<\/p>\n\n
Over the course of the summer, we\u2019ll be presenting, step by step, an approach that creates transparency and enables responsible management of the costs associated with corporate AI usage. Be sure to follow us. <\/em><\/p>\n\n <\/p> Do you have any question? Are you interested in this solution? Get in touch with our colleagues! <\/h3>\n\n
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