ChatGPT Enterprise: new spend controls and usage analytics

What changed in the June 2026 update

On June 18, 2026, OpenAI officially announced a major update for ChatGPT Enterprise. The announcement introduces two key features: the new spend controls and an advanced usage analytics system. Both address real operational needs of organizations that already run AI in production.

Specifically, spend controls let admins set spending limits per user, team or organizational unit. You can also configure automatic alerts when predefined thresholds are reached. This reduces the risk of unplanned usage, a common problem during rapid scaling.

Usage analytics, on the other hand, provide a centralized dashboard. IT leads and CFOs can view usage metrics broken down by department. As a result, analyzing internal adoption is far more precise than with previous tools.

The problem these tools actually solve

Until now, one of the brakes on enterprise adoption of generative AI was poor cost predictability. Many B2B organizations saw usage grow quickly without adequate visibility into how it was distributed internally. Yet blocking access to contain costs meant giving up the productivity benefits.

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

Similarly, the lack of granular analytics made it hard to justify AI investments internally. Now usage data becomes an asset for management. That makes the ROI of enterprise AI easier to measure and to communicate to company boards.

Immediate impact for Italian B2B SMEs

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

With the new spend controls, an SME with 50-200 ChatGPT Enterprise users can now assign different budgets by functional area. For example, the marketing team can have a different allocation than the legal or sales team. Automatic alerts also let you step in before thresholds are exceeded, without monitoring usage manually.

Usage analytics offer a less immediate but equally important advantage. Knowing which teams use the tool most, and in which contexts, helps you identify high-value use cases. You can then focus training and optimization where the impact is greatest.

At SHM Studio, we work with SMEs that are integrating AI tools into their daily workflows. Often the problem isn't the technology itself, but the lack of structure around it. These new OpenAI tools go exactly in that direction.

How spend controls work in practice

ChatGPT Enterprise spend controls operate at the workspace level. Admins can define monthly thresholds, either aggregated or per user. When a configurable percentage is reached, for example 80% of the budget, the system sends automatic notifications by email or webhook.

You can also set a hard cap, an absolute limit beyond which access is temporarily suspended. That said, use this option with caution in production environments. The ideal setup includes multiple alerts before reaching the automatic block.

Usage analytics aggregate data in a dashboard available to admins. Available metrics include: number of sessions per user, volume of tokens consumed, hourly distribution of access and type of usage. This information can also be exported as CSV for integration with company BI systems.

To dig deeper into the technical architecture, it's worth checking the official OpenAI documentation, which keeps enterprise feature specs constantly updated.

What to do now: three practical steps

For organizations already using ChatGPT Enterprise, the first step is to open the admin section and review the new spend management options. First, map the active teams and estimate expected usage for each. This gives you the basis for setting realistic thresholds.

Next, we recommend turning on usage analytics and collecting data for at least four weeks before making any optimization decisions. The first weeks tend to show usage patterns that haven't stabilized yet. It's better to observe first, then act.

Finally, share usage reports with the heads of each area. This builds internal accountability and encourages more conscious use of the tool. The data can also fuel conversations about the value AI generates in each department.

For SMEs weighing the adoption of ChatGPT Enterprise, this update removes one of the main obstacles. However, having the tools available doesn't guarantee effective adoption. A structured digital strategy remains the essential foundation.

The open work site: what's still missing

That said, the update leaves some room for improvement. Current spend controls work mainly at the token consumption level. It's not yet possible to differentiate costs by task type, for example separating GPT-4o usage from lighter models within the same workspace.

Native integration with ERP systems or procurement platforms is also still limited. SMEs that manage IT spend through centralized tools will have to rely on CSV exports and custom integrations. That's a non-trivial operating cost for small IT teams.

According to a Gartner analysis of enterprise AI governance, organizations that implement structured AI spend controls achieve an average 23% reduction in unplanned costs in the first year. So even with today's limitations, the value of these tools is measurable.

If you run SEO strategies or Google Ads campaigns with AI support, governance of generative tool costs becomes part of your marketing budget. These features matter beyond IT teams, too.

Outlook: where ChatGPT Enterprise is heading in the next 12-18 months

The June 2026 update fits a clear trajectory. OpenAI is gradually shifting ChatGPT Enterprise from a productivity tool to enterprise AI infrastructure. As a result, governance features such as spend controls, analytics and audit logs will become more central than pure generative capabilities.

Over the next 12-18 months, expect deeper integrations with IAM (Identity and Access Management) systems, compliance dashboards and data lineage tools. In addition, European regulatory pressure, especially the AI Act, will push vendors to offer ever more granular traceability and explainability features.

For Italian SMEs, this means that investing today in a structured approach to AI adoption, with governance, training and metrics, is not a side activity. On the contrary, it is the condition for scaling sustainably when the tools become even more powerful.

If you want to explore how to integrate these tools into an overall digital strategy, you can browse the SHM Studio services or contact the team directly. The SHM Studio blog also regularly publishes analysis on AI, web development and B2B LinkedIn strategies. Finally, if you work on optimized content, our SEO copywriting service already combines AI-assisted approaches with structured editorial oversight.