AI usage monitoring can mean anything from tracking time in an AI app to forensic security monitoring to measuring adoption and productivity. These seven tools cover very different jobs.
A time-tracking tool and a forensic security platform can both claim to monitor AI usage, but they answer very different questions. The comparison below shows what each tool can actually tell you and what it can’t.
Tool | What It Shows | Best Suited For | What It Doesn't Cover |
|---|---|---|---|
Hubstaff | Time, app and URL usage, optional screenshots, and AI-tool activity | Teams already using Hubstaff for time tracking, payroll, or billable work | Shows that an AI tool was used and for how long, but not proficiency, output quality, or business value |
Teramind | Screen activity, keystrokes, file and data movement, behavioral anomalies, and shadow AI activity | Security and compliance teams that need detailed oversight of data reaching AI tools | Focuses on security and forensic visibility rather than adoption depth, proficiency, or productivity value |
ActivTrak | AI usage, adoption maturity, productivity and capacity signals, and ROI analysis through AI Insights | HR and operations teams measuring AI adoption and workforce impact | Uses workforce behavioral data rather than token-level spend or engineering delivery data |
Insightful | Time, app and website activity, and optional screenshots | Distributed teams that want adjustable levels of workforce monitoring | Shows activity around AI tools but not what was produced or how effectively AI was used |
Controlio | Screen recording, keystrokes, file activity, and live monitoring | Teams prioritizing security, oversight, and policy enforcement | Does not focus on AI adoption, proficiency, or business-value measurement |
WorkTime | Active and idle time, logins, and app and website use without screenshots, keystrokes, or content capture | Privacy-sensitive environments that want lighter-touch monitoring | Shows whether and how long tools were used, not output quality or proficiency |
Worklytics | AI usage across tools such as ChatGPT, Copilot, Gemini, and Claude, plus proficiency and productivity signals | Organizations measuring AI adoption, proficiency, and workforce productivity across corporate tools | Uses workforce analytics rather than granular token spend or engineering-specific delivery data |
Start with the question you're trying to answer. A tool designed for one job can give you a misleading picture when you ask it to do another. Decide whether your priority is basic activity visibility, security and forensic monitoring, or AI adoption and productivity measurement, then compare tools within that category.
If you mainly need to know which applications people use and how much time they spend in them, tools such as Hubstaff, Insightful, and WorkTime can provide that visibility.
The main difference is how much additional activity they capture. Hubstaff and Insightful can include screenshots, while WorkTime explicitly avoids screenshots, keystrokes, and content capture.
Teramind and Controlio go deeper into activity capture.
That can make sense when the priority is detecting sensitive data movement, enforcing policy, or investigating what happened inside a specific session. It is a different job from measuring whether AI adoption is producing business value.
ActivTrak and Worklytics go beyond basic activity monitoring.
ActivTrak's AI Insights covers adoption maturity, productivity impact, and ROI signals. Worklytics combines cross-tool AI adoption data with proficiency and productivity analytics and publishes its own ROI framework.
If those are the questions you're trying to answer, these two are much closer to dedicated AI measurement than a conventional employee-monitoring tool.
Activity and security tools can show whether AI tools are being used or surface potential security and policy issues. But they don’t necessarily answer broader questions such as:
Larridin's AI Adoption, AI Fluency, and Token Spend & Insights address those questions across the enterprise.
The distinction is narrower with ActivTrak and Worklytics because both already provide meaningful AI adoption and productivity measurement. Larridin adds token- and model-level spend attribution, along with engineering-specific measurement through Agent Effectiveness.
The goal is to choose measurement that matches the question you need to answer rather than expecting one tool to cover every use case.
It depends on what you need to measure. Some tools focus on basic app activity, others on security and forensic monitoring, and others on AI adoption and productivity. Start with the question you need the tool to answer.
Some platforms now include AI adoption, productivity, or ROI analysis, but many still focus primarily on activity or security data. Check what the product actually measures before treating usage data as evidence of ROI.
Consider the level of visibility you actually need, along with privacy, security, governance, and measurement requirements. More detailed monitoring is not automatically more useful if it does not answer the business question you're trying to solve.
Larridin connects AI adoption, proficiency, spend, and business impact across the enterprise, with additional measurement for engineering teams and AI agents.