A license costing a few tens of euros and an AI project integrated into the management system respond to different needs. To compare them we need to look at the complete work: preparing the information, obtaining a result, verifying it and using it in the business process. The price of the model is an item on the account; the useful measure is how much it costs to complete the task well.
We can start from a concrete question: how much does it cost us to manage a case with the help of AI, including cases that require correction? Defining that result before the test makes both the estimate and the subsequent decision clearer.
A read document does not necessarily equate to an order ready for the warehouse. A generated response may still require source searching, checking and insertion into the management system. We then choose an observable end point: for example, a request correctly classified and assigned to the contact person, or an order verified and recorded according to the agreed rules.
We also determine what makes the result acceptable: required fields, permissions, controls and human intervention. Counting the answers produced, without distinguishing those that can be used, can make a process that leaves a lot of work for people appear convenient.
The start-up includes process analysis, data preparation, connection development, testing and training. Management includes licenses, consumption, infrastructure, assistance, controls and updates. Let's keep the two parts visible: the initial cost does not disappear because it is spread over a longer period.
To compare alternatives we can attribute a portion of the project to the observed period, declaring the hypothesized duration and volumes. If these change, the comparison also changes. It is useful to combine the recurring cost with the investment needed to start and the conditions for interrupting or changing the solution.
These activities depend on the organization. The list is used to formulate questions to the supplier and internal contacts, without assuming that each project has the same perimeter.
A pay-as-you-go system can also process instructions, history, and retrieved documents, as well as user-written text. Repeated requests or chains of multiple operations can increase usage. A paid license, on the other hand, may have limits, additional services or integration costs to verify.
The FinOps Foundation proposes linking AI spending to the use case outcome. To apply this principle we distinguish technical consumption from the cost of the process: a less expensive model may require more corrections; a more expensive one may be superfluous for a simple task. The comparison must be tried on the same cases.
Cost per case completed = costs attributed to the process in the period ÷ cases completed according to the agreed criteria.
We include the declared items in the numerator, without counting the same cost twice. In the denominator we put verified results, not attempts. Alongside the report we always report the volume entrusted to the system, accepted results and exceptions: the isolated number does not describe the functioning of the process.
For comparison with the current method we use an equivalent perimeter, including the treatment of cases excluded from automation. Time recovered and economic savings are different measures: the former becomes a concrete benefit when we know how it will be used.
Let's imagine a test with 500 cases: 400 achieve the agreed outcome and 100 require alternative management. Let's assume costs attributed to the period of 200 euros for licenses, 100 for consumption and infrastructure, 300 as a share of the initial work and 600 for controls and exception management.
The hypothetical total is 1,200 euros: dividing by the 400 accepted cases we obtain 3 euros per application completed with the AI path. Le 100 eccezioni restano evidenziate; il lavoro necessario per trattarle deve essere compreso nella rilevazione. Per valutare l’intero flusso registriamo anche come e quando vengono chiuse.
The numbers are invented exclusively to explain the calculation: they are not a price list, a Tredipi estimate or a customer result. A decision requires costs and times observed in the company, as well as comparison with the current process.
A fixed cost weighs differently on a hundred or a thousand cases; consumption can increase with longer documents, spikes and repeated attempts. We therefore prepare ordinary and challenging scenarios, reporting the hypotheses that change the account.
Spending alerts help detect anomalies, but an alert is not the same as a cap that automatically blocks every charge. We check which limits apply to the chosen service and which activity remains available when they are reached.
Le Microsoft guidelines on cost optimization they connect technical choices to the objectives of the work. Cutting an item without checking the effects on quality, access or reliability can shift the cost elsewhere.
We may collect this information without sharing confidential credentials or documents. For further information on the initial checks, please refer to the guide for evaluate an AI tool; for an example of a concrete process, see the article above orders and delivery notes in the management system.
At Tredipi we can support you in analyzing the adoption process, connections and alternatives. THE'AI Opportunity Assessment it is a starting point for defining what to investigate and what evidence to collect. When the topic is connecting tools and management, the system integration.
Let's talk about it starting from an activity of your company: we put together the expected result, volumes and controls necessary to evaluate the sustainability of the solution.
