Rippling announced this week AI Spend Console, a tool that monitors AI spending at the individual and team level. The news is significant: the company itself had burned millions of dollars in just a few months on AI tools without adequate control. Therefore, the new solution stems from direct experience of waste.
Indeed, the problem is not exclusive to Rippling. Many Italian companies — SMEs and the mid-market — are adopting AI tools in a fragmented way, without spending governance. As a result, marketing and operational budgets are fragmented across dozens of subscriptions, which often overlap. Furthermore, the lack of clear metrics makes it impossible to evaluate which AI investment generates an actual return.
We of SHM Studio We are observing this dynamic carefully. In particular, for marketing managers who manage complex technology stacks, the ability to track the ROI of AI is becoming a strategic skill. Therefore, tools like the AI Spend Console represent a sign of market maturity. In summary: the era of indiscriminate AI adoption is giving way to a more analytical and measurable phase.
The context: when even vendors burn AI budgets
Rippling is a very popular HR and payroll platform in the English-speaking market. However, this week it made headlines for an unusual reason. The company admitted to spending millions of dollars on AI tools within a few months. Therefore, it found itself dealing with a problem that many organizations know well: a lack of visibility into AI spending.
The operational response was AI Spend Console, a product that tracks AI spending per employee and per team. Thus, Rippling turned an internal pain point into a market offering. According to reports TechCrunch, the tool was developed starting precisely from the direct experience of waste lived internally.
Moreover, the timing is not accidental. The market for enterprise AI tools has exploded over the past two years. Consequently, AI spending governance has become a priority for CFOs and CMOs worldwide.
What does AI Spend Console actually do?
The AI Spend Console integrates into the Rippling ecosystem and offers a centralized dashboard. Specifically, it allows you to see how much each employee spends on AI tools, broken down by category and team. Therefore, the Chief Financial Officer or marketing manager gets a view that is both aggregated and granular.
Main features include:
- Employee tracking: Each user has an associated AI spending profile.
- Aggregation by team: visibility into departmental costs, useful for comparing efficiency between divisions.
- Duplicate identification: The tool flags overlapping or underutilized subscriptions.
- ROI Reporting: correlation between AI spending and measurable outputs, where available.
In addition to this, the solution is integrated into a platform already used for HR and payroll. Therefore, it does not require a new integration from scratch. This lowers the adoption barrier for companies that are already Rippling clients.
Why the AI spending problem is structural, not episodic
The Rippling case is not an anomaly. According to recent research by McKinsey, most companies that widely adopt AI struggle to measure its economic return. In fact, the proliferation of tools—from ChatGPT Enterprise to Midjourney, from Jasper to Perplexity—creates a fragmented stack.
Similarly, a report by Gartner had already pointed out in 2025 that 60% of AI initiatives in medium-sized companies lacked a framework for measuring ROI. As a result, budgets are wasted without a mechanism for optimization.
For Italian marketing managers, this scenario is particularly critical. In fact, the marketing department is often the first to adopt AI tools—for copywriting, image generation, data analysis, and campaign automation. However, it is also the department with the fewest financial governance structures compared to, for example, IT.
Immediate impact for Italian marketing managers
Those who manage a marketing budget in Italy in 2026 find themselves making complex choices. On one hand, the pressure to integrate AI is high. On the other hand, justifying spending to the CFO requires precise data. Therefore, a tool like AI Spend Console addresses a real need.
In particular, the most relevant use cases for a marketing manager are three. First: identifying which AI tools in the content team actually generate time savings. Second: comparing the marketing team's AI spending with that of other departments. Third: presenting a structured vision of AI ROI to the board, rather than just an anecdotal one.
We of SHM Studio We work daily with companies facing this challenge. In many cases, the first step is an audit of the existing technology stack. Therefore, AI spending governance is not a future problem: it is already present in the projects we follow.
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