Budget AI in the company: token governance and ROI

From tokenmaxxing to token rationing: what's happening

In June 2026, TechCrunch has documented a rapidly spreading phenomenon. Companies are running out of their AI budgets much sooner than expected. The reason? Employees are using generative models for low-value tasks: rephrasing emails, summarizing short documents, generating a few lines of text. Every API call consumes tokens. Every token has a cost. Multiplied by hundreds of users, the result is an out-of-control expenditure.

This scenario has a precise name: Tokenmaxxing. In practice, the massive and often unconscious use of language model capabilities. However, the euphoric phase seems to be over. We are entering the opposite era: the token rationing, or the conscious rationalization of AI consumption.

Therefore, the question that marketing and digital managers must ask themselves is no longer «which AI tool to adopt.» The question is: «How do we govern AI consumption to maximize ROI?»

The numbers reshaping CFO priorities

The AI-as-a-service models market has grown exponentially. According to Gartner, by 2027, more than 70% of mid-market companies will have integrated at least one AI tool into their core processes. However, the same study shows that less than 30% of these companies have a formal framework in place to monitor usage costs.

Furthermore, token-based pricing models create variability that is difficult to budget for. A company using GPT-4 or Claude 3.5 for content marketing may see its monthly costs fluctuate by 40–60% depending on the volume of requests. As a result, CFOs are beginning to require marketing and digital teams to report AI usage with the same level of precision as they report advertising spend.

In Italy, the context is further complicated. SMEs often adopt AI tools through SaaS subscriptions that include usage quotas. When these quotas are exhausted, unplanned additional costs are incurred. Therefore, governance is not just an issue for large enterprises; it also concerns those managing teams of 10-50 people.

Strategic Reading: Three Dynamics to Observe

Analyzing the phenomenon from a consulting perspective, three structural dynamics emerge that deserve attention.

First of all, democratization generates dispersion. When an AI tool is accessible to all employees, consumption becomes chaotic. There's no prioritization of high-value use cases. Each user utilizes the tool according to their own logic, often for tasks that wouldn't require AI.

Secondly, the lack of output metrics thwarts ROI. Many companies measure token consumption (input), but they don't measure the value output generated. For example, how many hours of work were saved? How much content produced generated organic traffic? Without these metrics, the AI budget remains an opaque cost item.

Finally, competitive pressure pushes for hasty adoption. Many SMEs integrated AI tools in 2025 without a governance strategy. Today they are dealing with the consequences. However, this does not mean that AI is a mistake: it means a more mature approach is needed.

The Unfinished Work: AI Governance in Italian SMEs

Building an AI governance model doesn't necessarily require complex structures. We at SHM Studio We work with companies of different sizes and observe that the most effective frameworks share some common elements.

Use case mapping by value level. Not all AI uses are equivalent. Generati a campaign of SEO copywriting optimized has measurable value. Rewording an internal email has marginal value. Therefore, the first step is to classify use cases into three categories: high value (strategic), medium value (operational), and low value (to be limited or automated with cheaper models).

Budget allocation by team or function. Analogous to how budgets are managed for Google Ads campaigns or for the LinkedIn campaign, AI consumption can be allocated by department. This creates accountability and encourages mindful use.

Choosing the right model for each task. Using a state-of-the-art model for every request is like using an SUV to go 500 meters. There are lighter and more economical models suitable for simple tasks. Therefore, effective governance also includes a model selection policy based on task complexity.

Operational implications for marketing and digital teams

For marketing managers, AI governance translates into concrete decisions. Here are the priority areas for action.

• Current consumption audit Before rationalizing, it's necessary to understand where the tokens are going. Many providers offer detailed usage dashboards. Using them is the starting point.
• AI Integration into Strategies SEO e digital marketing: High ROI use cases include optimized content generation, semantic SERP analysis, and campaign personalization. These uses justify a significant token investment.
• Team formation Employees need to understand that every prompt has a cost. This is not about limiting creativity, but about directing it towards measurable goals.
• Continuous monitoring AI consumption is not static. It grows with adoption. Therefore, the budget must be reviewed at least quarterly, not annually.

Furthermore, it's worth considering the integration of tools for AI governance in their technology stacks. Some platforms allow setting consumption thresholds, automatic alerts, and differentiated access policies by role.

Towards 2027: Whoever Governs AI Will Govern Competitive Advantage

The topic of AI governance is not going away. In fact, it will grow in importance as models become more powerful and per-token costs are spread across ever-larger volumes. According to Harvard Business Review, Organizations that invest in AI governance today are building a competitive capability that is difficult to replicate in the short term.

For Italian SMEs, this means acting now. Don't wait until the budget runs out to start thinking about how to allocate it. Don't wait for a CFO to ask for reporting to build output metrics. Therefore, AI governance is not a constraint: it is an enabler.

Teams that can balance freedom of use and cost control will get the most out of AI. Those who simply cut off access to tools risk losing the real benefits of the technology. The balance point lies in strategy, not in blocking.

To further explore how to structure a sustainable AI strategy for your business context, the team at SHM Studio is available for a dedicated consultation. You can contact us through the Contact Us to explore our digital services. Further details are available in our blog.