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 through millions of dollars in a few months on AI tools without adequate control. Therefore, the new solution stems from direct experience with waste.
In fact, the problem isn't unique to Rippling. Many Italian companies — SMEs and mid-market — are adopting AI tools in a fragmented way, without spending governance. Consequently, marketing and operational budgets are split across dozens of subscriptions, often overlapping. Furthermore, the lack of clear metrics makes it impossible to assess which AI investment is generating an actual return.
We at SHM Studio We're watching this dynamic closely. In particular, for marketing managers handling complex tech stacks, the ability to track AI ROI is becoming a strategic skill. Therefore, tools like AI Spend Console signal 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 an HR and payroll platform widely used in the Anglo-Saxon market. However, this week it made headlines for an unusual reason. The company admitted to spending millions of dollars on AI tools in just a few months. Therefore, it found itself grappling with a problem many organizations know well: a lack of visibility into AI spending.
The operational response has been AI Spend Console , a product that tracks AI spending per employee and per team. Thus, Rippling turned an internal criticality into a market offering. According to TechCrunch , the tool was developed precisely from the direct experience of waste experienced internally.
Furthermore, the timing is no coincidence. The market for AI tools for businesses has exploded in the last two years. As a result, AI spending governance has become a priority for CFOs and CMOs worldwide.
What AI Spend Console actually does
AI Spend Console integrates into the Rippling ecosystem and offers a centralized dashboard. Specifically, it allows you to see how much each employee is spending on AI tools, broken down by category and team. Therefore, the finance manager or marketing manager gets an aggregated and granular view at the same time.
Key features include:
- Tracking per employee: each user has an associated AI spending profile.
- Aggregation per 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, the solution is part of a platform already used for HR and payroll. Therefore, it does not require a new integration from scratch. This lowers the adoption barrier for existing Rippling customers.
Why the AI spending problem is structural, not episodic
The Rippling case is not an anomaly. According to recent research by McKinsey , most companies widely adopting AI struggle to measure its economic return. In fact, the proliferation of tools — from ChatGPT Enterprise to Midjourney, from Jasper to Perplexity — generates a fragmented stack.
Similarly, a report by Gartner already highlighted in 2025 how 60% of AI initiatives in medium-sized businesses lacked an ROI measurement framework. Consequently, budgets are scattered without an optimization mechanism.
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, campaign automation. However, it is also the department with less financial governance structures compared to, for example, IT.
Immediate impact for Italian marketing managers
Anyone managing a marketing budget in Italy in 2026 will face 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 meets a real need.
Specifically, there are three use cases that are most relevant for a marketing manager. First: identify which AI tools in the content team generate actual time savings. Second: compare the AI spending of the marketing team with that of other departments. Third: present a structured view of AI ROI to the board, not just anecdotal.
We at 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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