AI adoption is accelerating across large organisations. Teams are using powerful language models like GPT-4, Claude, and Gemini to automate tasks, write content, build new products, and more. But behind the scenes, most enterprises are running into a growing issue: they are struggling to track AI usage.
Without accurate tracking, companies risk going over budget, breaking compliance policies, and making decisions without data. This problem is not just technical. It affects finance teams, engineering leads, product owners, and legal departments alike. AI can deliver value, but only if enterprises can see where it is used, how much it is costing, and whether that usage is responsible and effective.
This article explains why tracking AI usage is so difficult, what risks it creates for enterprises, and how to fix it before costs and complexity get out of control.
The Root of the Problem
AI tools are incredibly easy to use. Teams can connect to OpenAI or Anthropic using just an API key and start building features the same day. But this ease of access leads to a major problem: decentralisation. Once AI access is granted, multiple teams begin using it in different ways, across various applications and environments.
In many cases, there is no central tracking system, no usage controls, and no clear owner. Finance teams may only see a large monthly invoice without any context. Security teams may not even know AI tools are in use. Engineering leaders often do not know which model is being used by which product. This lack of clarity opens the door to overuse, duplication, risk, and unnecessary spend.
Shared Keys and Shadow Usage
One common issue is the widespread use of shared API keys. These are often issued to multiple teams or applications with little oversight. Once a shared key is in use, it becomes very hard to track who is calling which model, how many tokens are being consumed, or what each team is contributing to the final cost.
This leads to what many call “shadow AI” usage. Engineering or data teams experiment with prompts and model calls without clear governance or cost tracking. As usage grows, so does the invoice, but there is no clear link between spend and value. Without usage-level observability, enterprises have no visibility into how AI is driving outcomes across the business.
Quick link: Generative AI cost: What Every CTO Should Know
Poor Reporting from Providers
AI infrastructure is still catching up to the needs of the enterprise. Most leading model providers give only basic usage reporting. An admin might see how many tokens were consumed or a rough breakdown by model, but there is usually no support for team-level usage, internal billing, or prompt-level analytics.
This makes it hard to assign costs to departments or products. For finance and operations teams, this lack of detail creates friction when planning budgets or reviewing spend. When there is no accurate internal reporting, AI spend becomes a black box.
Prompt Design Can Be Wasteful
Another reason enterprises struggle to track AI usage is that prompt design is often inefficient. Long or verbose prompts, multiple retries, and unnecessary context can quickly drive up token counts. Many teams unknowingly use the most expensive models for simple tasks that could be handled by cheaper alternatives.
Without tools to track and optimise these patterns, enterprises are left paying for inefficiencies. Even small issues like repeated instructions or unclear system messages can double or triple the cost of a single request. These costs add up fast at scale.
Lack of Finance and Engineering Collaboration
Successful AI adoption requires alignment between finance, engineering, and product teams. But in most enterprises, these groups speak different languages. Finance wants predictable costs and clean reporting. Engineers want freedom to experiment. Product teams want to move fast and test what works.
When AI usage is not tracked properly, these teams cannot collaborate effectively. Finance does not know how to assign cost. Engineers do not know how much they are spending. Product teams do not know what model choices mean for margin or budget.
This lack of shared understanding leads to missed targets, slow decision-making, and stalled innovation.
Security and Compliance Risks
Untracked AI usage also introduces serious risk. Many industries have rules about how data can be used, stored, and processed. If employees are sending sensitive data to AI models without proper controls, the organisation may be exposed to regulatory violations or security breaches.
Without audit logs or usage tracking, it is impossible to know who accessed which model, when, or with what input. Role-based access controls, rate limits, and spending caps are rarely enforced, leaving enterprises vulnerable to both internal misuse and external threats.
Quick link: What Is FinOps? And Why It’s Critical for AI Infrastructure Teams
The Need for an AI Control Plane
Enterprises need a better way to manage AI usage at scale. Just as cloud infrastructure required tools like observability dashboards, cost monitors, and access control platforms, AI infrastructure now requires a similar control layer.
This layer should provide real-time usage tracking, team-level visibility, spend caps, and automated insights. It should help technical teams understand model efficiency, finance teams assign cost centres, and compliance teams monitor access and usage.
Without it, AI usage remains unmanaged. With it, companies can scale AI with confidence.
How WrangleAI Solves the Problem
WrangleAI was built to solve this exact issue. It acts as a control plane for AI usage, cost, and governance, designed specifically for enterprises using large language models like GPT-4, Claude, Gemini, and more.
With WrangleAI, enterprises get full visibility into who is using which models, how many tokens they are consuming, and what that usage is costing. It breaks down usage by team, product, or initiative, allowing accurate reporting and clean internal billing.
WrangleAI also identifies wasteful prompts, helps teams route tasks to more cost-effective models, and enforces budgets with smart caps and alerts. Teams can create scoped API keys for different apps or users and monitor everything through a single dashboard.
This turns AI from a financial black hole into a trackable, optimised resource that supports innovation without risking runaway costs.
Final Thoughts
Enterprises are not struggling with AI because the technology is hard to use. They are struggling because it is hard to track. When teams cannot see what is being used, who is using it, or how much it is costing, AI becomes a hidden risk instead of a business advantage.
The solution is clear visibility, cost optimisation, and smart governance. That is what WrangleAI delivers. It gives your team the control it needs to scale AI with confidence, precision, and purpose.
If your enterprise is ready to track AI usage properly, control costs, and govern your models with confidence, visit wrangleai.com and request a free demo today.
