The surge in AI at work has many HR managers and business executives asking: How well are we using AI, and how can we do better? This is where an AI usage checker comes in. It as a way to measure AI usage across teams and roles, giving you visibility into who's using which AI tools, how often, and how efficient they utilize it.
Monitoring AI usage isn't about snooping or micromanaging; it's about insight. Tracking AI tools helps leaders see where AI delivers value and underutilizes it to reveal proficiency opportunities.
In many organizations, AI adoption is happening fast – but unevenly. According to one analysis, roughly 20–40% of workers already use AI at work, with adoption especially high in software development roles.
Tracking who is using AI (and how) shines light on these disparities – for example, which teams or roles have eagerly embraced AI versus which are barely experimenting, and whether higher AI usage is translating into faster project delivery or other productivity gains.
For executives, having data on AI usage by team is essential as it helps identify areas where proficiency can be improved. By analyzing how each team utilizes AI tools and technologies, leaders can uncover skill gaps and opportunities for development. It's hard to manage what you don't measure.
In other words, most organizations lack oversight of how much AI is utilized. An "AI usage checker" initiative provides some of that oversight by revealing usage patterns.
Finally, consider the productivity payoff. AI can dramatically amplify efficiency when used well – but you only get that benefit if people actually use the tools. Surveys show 96% of employees who use generative AI feel it boosts their productivity.
Beyond anecdotal examples, data shows that AI adoption in workplaces is surging – and it's already delivering tangible benefits. Keeping track of these trends can help make the case for monitoring and fostering AI use in your business.
Uptake is highest in tech-centric roles (no surprise – many programmers love their GitHub Copilot), but it's also spreading in functions like marketing and customer support.
What's really exciting for leaders is the early evidence of productivity gains. Consider these findings:
However, simply rolling out AI tools doesn't automatically mean people will use them. Many employees remain unsure or uneasy about using AI at work. That's where leadership support is crucial. Monitoring usage helps you spot where adoption is lagging or confidence is low.
One reason tracking AI usage has become critical is simply the sheer breadth of AI tools now in use. Modern workplaces are teeming with AI-powered platforms, each adopted by different roles. Let's look at some examples of how various teams might use AI tools today:
Each team might use a different mix of tools, and that's exactly why an AI usage checker is so valuable. It provides a consolidated view for leadership: a dashboard showing, say, high AI engagement in development and design teams, moderate in marketing, and low in operations.
That insight leads to meaningful conversations:
You already see the value in tracking AI usage but manual spreadsheets or one-off tool dashboards only get you so far. Worklytics’ AI Adoption Dashboard integrates usage logs from Slack, Microsoft 365 Copilot, Gemini, Zoom, and dozens of other tools into a single, privacy-safe view, so every step below is built on reliable, always-up-to-date data.
Worklytics automatically ingests and pseudonymizes system logs, then shows who is using which AI features, how often, and in what context—all broken down by team, role, and geography. Instead of a one-time survey, you get a living baseline that updates daily and can be sliced any way you need.
Skip the stitching-together of disparate reports: the AI Adoption Dashboard is your unified checker. Filter by tool, compare week-over-week trends, or set usage targets and watch progress tick up in real time.
The platform flags “power users” and “lagging teams” automatically. You can export a list of top adopters to spotlight in an internal AMA, or drill into teams with near-zero usage to uncover blockers before quarter-end.
4. Benchmark AI Adoption Against Peer Organizations
Benchmarking AI adoption involves comparing an organization’s AI maturity and practices against industry standards or peers to identify gaps and opportunities. This process helps accelerate AI adoption by clarifying priorities, setting realistic goals, and driving strategic alignment across teams.
Because Worklytics shows which features are under-utilized, enablement teams can build highly targeted workshops (“Copilot prompts for Finance,” “How Support can automate ticket triage with Gemini”) instead of generic lunch-and-learns. Progress after each session is visible in the dashboard, so you immediately see if training sticks.
ONA (Organizational Network Analysis) visualizations reveal how AI agents are embedded in real collaboration networks—e.g., whether Copilot comments are flowing across design–engineering hand-offs. Use these insights to turn on features where they naturally fit and to pair high-adopting teams with neighbors who could benefit.
Worklytics’ data is fully anonymized and content-free, helping compliance teams craft “approved use” policies with confidence—no sensitive prompts or output are ever stored, and the platform is GDPR/CCPA compliant out of the box.
Tie usage to outcomes by overlaying Worklytics’ productivity metrics (meeting hours saved, turnaround-time improvements, etc.) on top of AI adoption curves. When Support handles 20 % more tickets after rolling out an AI assistant, you’ll have the hard numbers—and a share-ready chart—to prove it.
Because the dataset streams continuously and can be pushed to your data warehouse—you can review adoption in every leadership meeting, benchmark against peer organizations, and course-correct fast. New AI features land in the dashboard automatically, so your strategy evolves alongside the tech.
Embedding Worklytics at the heart of your AI enablement playbook, you get precise measurement, actionable insights, and the privacy safeguards leaders demand—so boosting adoption becomes a data-driven habit, not a one-time project.
