Artificial intelligence is on every executive’s mind, touted as the next revolution in business. But amid all the hype, how do organizations ensure they’re reaping real value from AI?
The key is measurement.
Just as you wouldn’t invest in a new initiative without tracking its ROI, AI adoption needs to be quantified. Measuring which department is using AI, how often, what AI agents, and with what impact is crucial to bridge the gap between lofty promises and tangible outcomes. It’s the difference between talking about AI and leveraging AI.
A recent global survey highlights why this is so important: AI adoption in companies surged to 72% in 2024 (up from 55% in 2023).
High adoption rates alone don’t guarantee value. Simply deploying AI doesn’t mean it’s delivering results. Many firms enthusiastically enable AI features across the enterprise yet later discover that only a fraction of employees use them regularly. That’s why measurement comes first – it separates true transformation from tech fads.
For instance, if 72% of your company has access to AI, is it the light users merely dabbling or heavy users deeply integrating AI into daily work?
Measuring AI adoption provides several benefits: it quantifies the baseline (e.g. how many employees used an AI tool this month) and illuminates the breadth of usage (across teams, roles, and locations).
What exactly should you measure to gauge AI adoption? Not all metrics are created equal. Below are six key AI usage metrics that business and tech decision-makers should track, each shining a light on a different dimension of adoption. By monitoring these, you get a 360° view of how AI is permeating your organization:
This metric segments your users based on the intensity of their AI use. For example, what percentage of employees are “heavy” users (leveraging AI tools daily or for a high percentage of their work) versus “light” users (occasional or infrequent use)? If a large chunk of users remain light users, it signals untapped potential – perhaps due to lack of training or unclear value of the AI Agent.
AI usage often doesn’t spread evenly. Measuring adoption by department or team reveals where AI is taking hold and where it’s lagging. You might see that your Engineering and Customer Support departments have 80% of staff actively using AI (perhaps for coding assistance and ticket triage, respectively), while Finance or Legal are at 20%. Such insights are critical. Low adoption in a department could mean the AI tools available aren’t well-suited to that function’s work, or perhaps that team’s leadership isn’t encouraging experimentation.
This is a subset worth highlighting – the adoption rate among managers and team leads in each department. Managers set the tone; if they embrace AI tools, their teams are more likely to follow. Measuring manager usage shines a light on cultural buy-in. For example, you might discover that in Sales, 90% of frontline reps use an AI-driven CRM assistant, but only 40% of Sales managers do. That gap could indicate that managers are stuck in old workflows or don’t trust the AI – a red flag since a lack of leadership engagement can stall broader adoption.
On the flip side, if a particular department’s managers are all actively using AI (leading by example in meetings, processes, and decisions), it often correlates with that department’s overall high usage. Tracking this metric helps target leadership coaching and communication, ensuring that mid-level and senior managers are champions, not bottlenecks, for AI integration.
Do newer employees use AI more than veterans, or vice versa? This metric compares adoption between recent hires and long-tenured staff. New hires might be more digitally native or more recently trained in AI tools, so you may see a higher percentage of them quickly picking up AI in their workflows. Tenured employees, on the other hand, might rely on established processes and be slower to change habits. If your data shows that, say, 85% of employees hired in the last 12 months use AI weekly versus only 50% of those with 10+ years at the company, that’s a telling gap. It identifies a need for change in management, focusing on upskilling and reassuring long-time staff. Alternatively, if tenured experts are heavy AI users (perhaps because they know the domain problems deeply and see where AI helps), you’d want to capture their tacit knowledge and use them as AI mentors.
In either case, segmenting by tenure helps tailor your training and communication strategies to the different needs and attitudes of each cohort.
This metric extends beyond user counts to examine the penetration of workflow automation. In other words, what portion of employees’ day-to-day tasks are being aided by AI? For example, are 30% of all code commits in your repository coming with suggestions from GitHub Copilot? Is 25% of customer service chat volume being handled by an AI assistant? By tracking this, you focus on depth of usage: increasing the proportion of work where AI is actively contributing. Over time, you might set targets like “Our goal is for 50% of all customer support resolutions to involve AI assistance by Q4” and monitor progress.
Most organizations deploy multiple AI tools or agents. This metric looks at which AI tools are used the most (and by whom). For instance, out of all AI interactions company-wide, perhaps 40% are with Microsoft 365 Copilot, 25% with a Slack AI bot, 15% with ChatGPT Enterprise, 10% with Salesforce’s Einstein GPT, and so on. Visualizing this as a usage share pie chart or ranked list can be enlightening. A tool with a high share indicates strong adoption and perceived value; a tool with a very low share might be a candidate for reevaluation or better promotion. It also helps identify whether critical business functions are dependent on a single AI platform.
By tracking these six metrics, leaders get a comprehensive dashboard of AI adoption. Each metric, visualized as a chart or trendline, tells a different part of the story – from the breadth of usage across the org to the depth of engagement and the outcomes of AI-driven work.
Collecting metrics is only half the battle – the real goal is to act on the insights they provide. The beauty of visualizing AI adoption data is that it naturally invites questions and strategies. Good charts not only inform but also inspire action.
Here’s how organizations can go from viewing a chart to implementing changes that boost AI ROI:
Segment and Pinpoint: First, breaking down metrics by relevant segments (such as department, role, location, etc.) enables targeted analysis. Worklytics, for instance, enables tracking AI usage by team, tool, and role, which provides granular insight into where uptake is strong and where it’s lagging. If the data shows that the R&D department heavily utilizes AI, but the Operations department barely touches it, that’s a prompt to investigate why. Maybe Ops employees don’t see how AI applies to their day-to-day tasks, or they’ve had difficulties with the tools – information you might gather via surveys or manager feedback. The segmentation answers where to focus.
Benchmark and Compare: Charts also enable you to benchmark performance over time and compare it against peers. Seeing a trendline of your organization’s AI adoption climbing (or plateauing) month over month provides context: did the big training program last quarter move the needle? Are you on track to meet your adoption goals? Moreover, some platforms enable benchmarking against industry averages or similar companies.
Implement Feedback Loops: Leading organizations treat these metrics as part of a continuous improvement loop. It works like this: you collect baseline metrics, take an action (for example, a targeted training session for a low-adoption department or a new policy that managers must complete an “AI champion” course), and then you watch the metrics in subsequent weeks to see if the needle moved. If the median number of interactions per user increases after training, that’s direct evidence that the intervention was effective. If it stays flat, perhaps the approach needs rethinking.
Measuring and visualizing AI usage across an entire organization can sound daunting – after all, modern enterprises use a plethora of tools.
This is where Worklytics comes in. Worklytics is a people analytics platform that specializes in aggregating work activity data (while protecting privacy) to generate insights. It has a dedicated AI Adoption analytics module designed to track AI usage across the myriad of apps and services your teams rely on daily.
Worklytics effortlessly plugs into the AI-powered apps and collaboration hubs your teams already rely on, including:
By tapping into the usage logs or APIs of these tools (with appropriate permissions and security), Worklytics consolidates data on who is using AI, how often, and in what ways.
The Worklytics dashboard comes with rich visualizations out of the box. It surfaces adoption and usage by team and role, so you can instantly pinpoint which departments are actively experimenting with AI and which might need encouragement. It provides trend lines over time, letting you spot whether AI pilot programs plateau or accelerate after training sessions. This helps in managing rollouts – if you see a plateau, it might be time for a refresher campaign; if you see acceleration, you know a particular initiative has gained traction.
Worklytics also includes benchmarks versus industry peers, leveraging aggregated (and anonymous) data to position your organization’s adoption relative to others.
Another standout capability is Worklytics’ Organizational Network Analysis (ONA) focused on AI. This analysis can map how AI agents integrate into collaboration patterns – for instance, identifying whether certain teams primarily use AI for internal knowledge sharing versus external client work.
In short, the Worklytics advantage is that it brings all the key metrics and tools together under one roof. You’re not manually cobbling together reports from Microsoft’s admin center, Slack’s usage stats, and so on. Instead, Worklytics provides a cohesive, real-time pulse of AI adoption. It tells you where AI is flourishing, how it’s being used (which tools, what frequency), and who your leaders and laggards are in the journey. All of this is delivered in a human-friendly visual format that decision-makers can grasp at a glance. By integrating with all your AI-enabled apps,
Whenever we talk about tracking workplace data – especially something as granular as how employees use AI tools – it raises an important question: How do we protect employee privacy? Business leaders and HR professionals must strike a balance between the drive for insight and the obligation to maintain trust and comply with privacy standards. Worklytics addresses this challenge head-on with a privacy-by-design approach that utilizes anonymized and aggregated data to provide insights without compromising individual privacy.
The platform is built with compliance in mind – aligning with GDPR, CCPA, and other regulations that demand strict handling of personal data.
One of the most valuable outcomes of measuring and visualizing AI adoption is the ability to benchmark your organization’s progress and get tailored recommendations on how to improve.
Compare Against Industry Standards: Seeing your AI usage data in isolation is useful, but seeing it in comparison to others is powerful. With benchmarking, you can answer that.
Worklytics, for example, allows companies to compare their AI adoption metrics to industry benchmarks or companies of similar size (all in an anonymized fashion). It also helps in setting realistic goals. If the top performers are at 85% adoption, that might be your north star, whereas if everyone is struggling to get above 50%, a goal of 60% might already put you in a leadership position.
By understanding your current position and gaining insight into the actions that will drive progress, you can accelerate the delivery of AI value.
In the new era of AI, the organizations that will thrive are those that close the loop between enthusiasm and execution. Measuring and visualizing AI usage is the linchpin of this process.
With the right tools like Worklytics, this can be done in a privacy-protected, automated, and insightful way. Instead of flying blind, you gain a dashboard for your AI journey, complete with benchmarks and feedback loops to keep improving.
