ChatGPT Enterprise: New Spend Controls and Usage Analytics

What changed in the June 2026 update

On June 18, 2026, OpenAI has officially released a relevant update for ChatGPT Enterprise. The announcement introduces two macro-features: the new Spending controls it's an advanced system for usage analytics. Both functionalities meet concrete operational needs for organizations that have already adopted AI in production.

Specifically, spend controls allow administrators to set spending limits per individual user, per team, or per organizational unit. Furthermore, it's possible to configure automatic alerts when predefined thresholds are reached. This reduces the risk of unplanned consumption, a frequent problem during rapid scaling phases.

Usage analytics, on the other hand, offer a centralized dashboard. Therefore, IT managers and CFOs can view usage metrics broken down by department. Consequently, the analysis of internal adoption becomes much more accurate compared to previous tools.

The problem these tools really solve

Up until now, one of the brakes on enterprise adoption of generative AI has been the poor predictability of costs. Many B2B organizations have experienced situations where usage grew rapidly, but without adequate visibility into internal distribution. However, blocking access to contain costs meant foregoing productivity benefits.

According to research from McKinsey on AI adoption, one of the main barriers to the systematic adoption of AI in businesses is precisely the difficulty of financial governance. Therefore, tools like spend controls are not a technical detail: they are a strategic enabler.

Similarly, the lack of granular analytics made it difficult to internally justify AI investments. Now, usage data becomes an asset for management. Therefore, the ROI of enterprise AI is more measurable and communicable to company boards.

Immediate impact for Italian B2B SMEs

Italian SMEs operating in B2B contexts often find themselves in a unique position. On one hand, they need to adopt AI to remain competitive. On the other hand, they have leaner IT teams and less flexible budgets compared to large corporations. Therefore, AI cost governance is even more critical for them.

With the new spend controls, an SME with 50-200 ChatGPT Enterprise users can now assign differentiated budgets by functional area. For example, the marketing team can have a different allocation compared to the legal or sales teams. Furthermore, automatic alerts allow for intervention before exceeding thresholds, without manual consumption monitoring.

Usage analytics, on the other hand, offer a less immediate but equally important advantage. In fact, knowing which teams use the tool the most—and in what contexts—allows for the identification of high-value use cases. Consequently, training and optimization can be focused where the impact is greatest.

We of SHM Studio We work with SMEs that are integrating AI tools into their operational flows. Often, the problem isn't the technology itself, but the lack of structure around it. These new OpenAI tools are going exactly in that direction.

Spend Controls Architecture: How it Works in Practice

ChatGPT Enterprise's spend controls operate at the workspace. The administrator can set monthly thresholds either as an aggregate or on a per-user basis. When a configurable percentage is reached—for example, 80% of the budget—the system sends automatic notifications via email or webhook.

Is it possible to set even a hard cap, meaning an absolute limit beyond which access is temporarily suspended. However, this option should be used with caution in production environments. Therefore, the ideal configuration includes multiple alerts before reaching the automatic block.

Usage analytics, on the other hand, aggregate data in a dashboard accessible to admins. Available metrics include: number of sessions per user, volume of tokens consumed, hourly access distribution, and usage type. Additionally, this information can be exported in CSV format for integration with company BI systems.

To further explore the technical architecture, it is also useful to consult the OpenAI official documentation, which constantly updates the specifications for enterprise features.

What to do now: three operational steps

For organizations already using ChatGPT Enterprise, the first step is to access the administrative section and check the new spend management options. First, it is advisable to map active teams and estimate the expected consumption for each. This provides the basis for configuring realistic thresholds.

Subsequently, it is advisable to activate usage analytics and collect data for at least four weeks before making optimization decisions. In fact, the first few weeks tend to show usage patterns that are not yet stabilized. Therefore, it is preferable to observe before intervening.

Finally, it's useful to share usage reports with the managers of each area. This creates internal accountability and encourages more mindful use of the tool. In addition, the data can fuel conversations about the value generated by AI in each department.

For SMEs considering adopting ChatGPT Enterprise, this update removes one of the main obstacles. However, the mere availability of tools does not guarantee effective adoption. Structured digital strategy the fundamental assumption remains.

The construction site is still open: what is still missing

Despite this, the update presents some areas for improvement. The current spend controls operate primarily at the token consumption level. However, it is not yet possible to differentiate costs by task type—for example, separating the use of GPT-4o from that of lighter models within the same workspace.

Furthermore, native integration with ERP systems or procurement platforms is still limited. SMEs managing IT spending through centralized tools will therefore have to rely on CSV exports and custom integrations. This represents a significant operational cost for small IT teams.

According to an analysis by Gartner on Enterprise AI Governance, organizations that implement structured AI-based spending controls achieve an average reduction of 23% in unplanned costs in the first year. Therefore, even with current limitations, the value of these tools is measurable.

For those managing strategies SEO o Google Ads campaigns With AI support, the cost governance of generative tools becomes an integral part of the marketing budget. Therefore, these functionalities are also relevant outside of IT teams.

Prospects: Where ChatGPT Enterprise is headed in the next 12-18 months

The June 2026 update fits into a clear trajectory. OpenAI is progressively shifting ChatGPT Enterprise's positioning from a productivity tool to Enterprise AI infrastructure. Therefore, governance features—spend controls, analytics, and audit logs—will become increasingly central compared to pure generative capabilities.

In the next 12-18 months, deeper integrations with IAM (Identity and Access Management) systems, compliance dashboards, and data lineage tools are reasonable to expect. Furthermore, European regulatory pressure—particularly the AI Act—will push vendors to offer increasingly granular traceability and explainability functionalities.

For Italian SMEs, this means that investing today in structuring AI adoption—with governance, training, and metrics—is not an ancillary activity. On the contrary, it is the condition for being able to scale sustainably when the tools become even more powerful.

Those who wish to learn more about integrating these tools into an overall digital strategy can explore SHM Studio services o contact the team directly. Furthermore, the SHM Studio Blog regularly publishes analyses on AI, web development e B2B LinkedIn Strategies. Finally, for those who work on optimized content, the service of SEO copywriting integrates AI-assisted approaches with structured editorial supervision.