AI Spending Tools: 5 Ways to Prove Your AI ROI

New AI spending tools from Anthropic aim to answer the question every CFO is asking right now: what exactly are we getting for all these tokens?

On October 28, Anthropic announced a suite of reporting tools that show enterprise customers what their AI spending produces, as TechCrunch reported. Whatever provider you use, the idea behind it is worth borrowing. Let me explain why.

What Anthropic Announced

The tools combine internal analytics with employee surveys. Managers pick a date range and see what a team produced, what it cost, and where workflows broke down. Reports include recommendations for improving results, and Anthropic says the data will not be used for model training by default. It is also adding pooled group budgets, so teams can manage token spend together instead of person by person.

TechCrunch describes this as an effort to prove return on investment, and as a lighter version of the hands-on help that forward-deployed engineering teams give big customers.

Why ROI Is the Real Problem

The same report cites data from Jellyfish, a developer analytics company, covering 276,000 customers’ use of agentic coding tools. Spending on these tools has grown about tenfold since the start of the year and now equals roughly 2% of total engineering cost. Jellyfish’s chief scientist Nicholas Arcolano said that 10x growth “is growth that most CFOs didn’t plan for.”

That is the squeeze. Two percent sounds small, and it is. But a line that grows tenfold in ten months gets attention fast. The article also notes that most Fortune 500 companies are not yet seeing major gains from AI deployments. So finance teams want proof, and engineering teams need a way to give it.

Way 1: Measure Outcomes, Not Usage

Token counts and seat numbers tell you who is using the tool. They do not tell you whether work got better. Pick two or three outcomes per team, such as cycle time for pull requests, tickets closed without rework, or support resolution time. Track them before and after rollout. If you cannot name the outcome, you cannot defend the budget.

Way 2: Find the Friction

Anthropic’s reports highlight where workflows break down. You can do a manual version today. Ask each team the same short survey every month: where did the AI waste your time, and where did it save hours? Patterns show up quickly. Often the fix is a better prompt library or cleaner internal documentation, not a more expensive model.

Way 3: Pool Budgets and Set Guardrails

Individual caps create two problems: people hoard usage, or they blow through it. Group budgets let teams move spend to where it matters. Pair them with alerts at 50%, 80% and 100% of budget, and a named owner who reviews spend weekly. This works with any provider, not just one.

Way 4: Mind Data and Compliance

For European firms in particular, usage analytics that combine employee surveys and work output touch on privacy. GDPR obligations and works council expectations may apply when you measure people’s work. Check what data a tool collects, where it is stored, and whether it is used for training. Anthropic’s default of not training on this data is a good sign, but verify it in your own contract.

Way 5: Tell a Story Finance Can Read

A dashboard full of token charts will lose the room. Translate it. “Our payments team shipped 30% more changes with the same headcount” lands better than “we used 4 billion tokens.” Put cost, output and risk on one page, and update it monthly. Honest numbers, including the disappointing ones, build trust faster than a glossy deck.

A Note on Vendor Lock-In

Jellyfish’s data suggests switching costs grow once a team has standardized workflows around one provider’s tools. That is not automatically bad, but you should decide it on purpose. Keep your prompts, evaluations and internal documentation portable, so you can compare providers on results rather than habit.

Key Takeaways

• ROI is the new bottleneck: Spend on agentic coding tools has grown about 10x this year, per Jellyfish data cited by TechCrunch.
• Measure outcomes: Track cycle time, rework and resolution time, not just token counts.
• Use pooled budgets: Shared team budgets and weekly reviews prevent both hoarding and overspend.
• Check privacy early: Analytics that touch employee output can trigger GDPR and works council questions.
• Keep things portable: Own your prompts and evaluations so provider choice stays a real decision.

How TecniForge Can Help

At TecniForge, we help businesses navigate these technology shifts. Whether you need custom software development, AI integration, or cloud migration, our team builds scalable solutions. We can help you set up usage tracking, define ROI metrics and integrate AI into your products without losing sight of cost. For background on European data rules, see the European Commission’s data protection pages. Talk to our experts.

If your CFO asked tomorrow what your AI budget bought last quarter, could you answer in one slide?