If you buy 500 ChatGPT licenses, but only 200 employees use them, you’re paying more than double what you should.
According to the Larridin 2026 State of Enterprise AI Report, 72% of technology investments destroy value through waste and poor governance. Licensing is one of the first places where waste shows up. It’s hard for organizations to optimize licensing allocation for AI platforms, including ChatGPT, Copilot, and Gemini, without tracking actual tool usage across them. And it’s even harder to spot the use of unauthorized, unlicensed AI tools without tracking. Real-time monitoring tools provide valuable insights into AI usage patterns, enabling data-driven decisions about pricing, onboarding, and functionality needs.
Organizations buy licenses for generative AI platforms, such as ChatGPT, Copilot, and Gemini, and assume employees will use AI. Reality differs. Without AI use tracking, companies can’t see who uses AI tools, how often, or for what use cases.
Perhaps worse, employees often use non-licensed AI tools, often quite effectively, but unknown to IT. This is an additional form of the longstanding problem of shadow IT, known as shadow AI, and may perhaps be even worse. Link to new Identifying hidden AI article.
According to Larridin, 84% of organizations discover more AI tools than expected during audits. Larridin also reports that 83% say shadow AI is spreading faster than security teams can track. This AI sprawl creates hidden costs through over-licensing some platforms while under-licensing others.
Common scenarios: Marketing teams get enterprise licenses, but stick with free ChatGPT. Project management tools with AI-powered functionality aren’t used. Contractors and regular employees with special skills use new or niche AI tools - some regularly, some on an ad hoc basis. New AI platforms are added without retiring old ones. All of these scenarios represent wasted budget and/or unwanted risk to the organization.
Tracking tools and AI monitoring solutions show actual AI usage across your organization. Real-time dashboards provide metrics that should include:
AI use tracking reveals security risks. Monitoring tools identify when employees use personal AI accounts with sensitive data. We recommend that you track AI usage for many reasons, not least to spot potential data leaks before they become cybersecurity incidents. Employee monitoring for AI systems protects against unauthorized use of artificial intelligence with confidential information.
Usage trends show where to optimize licensing allocation. Automated tracking reveals if departments are over-licensed and have unused seats or teams are under-licensed, so employees have to share accounts, use personal accounts or use unmonitored “shadow” AI. Data-driven decisions about pricing tiers ensure you buy the right licenses at the right levels.
Implement AI monitoring solutions that provide real-time visibility into tool usage. Begin by establishing baseline metrics before optimizing. Browser-based tracking tools monitor AI platform interactions, and API integrations connect to SaaS AI tools for usage data.
Define what good AI usage looks like for different roles. Marketing teams need generative AI to create content. Engineers benefit from AI-assisted coding. Project management uses AI-powered automation. Use templates to guide onboarding and help track AI adoption against expectations.
Track AI usage to measure the impact of AI on employee productivity. Valuable insights show which use cases deliver competitive advantage. Success stories emerge from teams using AI effectively. Use these to guide initiatives expanding proven AI applications.
Turn tracking insights into action through systematic optimization of licensing allocation and AI adoption strategy.
Analyze usage trends quarterly. Remove licenses from inactive users. Add seats where teams have high adoption, but not enough accounts. Adjust pricing tiers based on functionality actually used. Consider consolidating multiple AI tools serving similar use cases.
AI use tracking reveals unused licenses; investigate why. Is it poor onboarding? Wrong functionality for workflows? Lack of training? Use data-driven decisions to address adoption barriers. Don’t assume that employees don’t want to, or can’t, use AI.
Usage metrics show which AI tools and use cases deliver results. Expand successful AI platforms to more teams. Provide datasets and templates to help new users succeed. Boost productivity organization-wide by scaling proven applications of artificial intelligence.
By adopting AI use tracking, you can transform AI tool licensing from a fixed cost to an optimized investment. Organizations that use monitoring tools for smarter decision-making about which AI tools to license, how many seats each team needs, what pricing tiers deliver best value, and when to consolidate or expand AI platforms get outsized results. Link to new AI Maturity Over Time post.
The impact of AI use tracking goes beyond cost savings. Better allocation improves employee productivity by ensuring the right people have access to the right AI tools. Reduced security risks protect sensitive data. Increased AI adoption comes from data-driven decisions about onboarding, enablement, and usage.
Start tracking AI usage today. Deploy monitoring tools across AI platforms; establish baseline metrics; analyze usage trends; optimize licensing allocation; and measure the impact of AI on business outcomes. You can’t optimize what you don’t track.
The monitoring needed to get the most out of AI software licensing is hard to create and manage yourself, while adopting and using Larridin is easy. In addition, Larridin is engineered from the beginning to meet and maintain industry standards and support governance, based on our ongoing engagement with leading AI platforms. With Larridin, it’s easy to shed the licenses you don’t use and add the licenses needed to eliminate shadow AI in your organization. To see Larridin in action, connect with us for a demo.
Ready to optimize your AI licensing through usage tracking?