Your organization is investing in AI tools — but are people actually using them? And when they do, is it making a difference? ActivTrak for BI - AI Insights answers both questions by measuring how widely AI tools are adopted across your workforce, how deeply they're embedded in daily work, and whether that adoption is translating into measurable productivity gains.
- Understand how broadly AI tools are being used across teams and departments
- Track how individual users are progressing through AI Adoption Maturity stages over time
- Correlate AI adoption with productivity metrics to build the business case for enablement investment
- Identify which AI tools are driving the most usage — and which licenses may be underutilized
- Combine AI adoption data with other business metrics for more comprehensive executive reporting
Available to customers with a paid ActivTrak plan, the ActivConnect API (Add-on) and the AI Insights (Add-on), this report provides executives, IT leaders, and HR teams with a flexible, customizable view of AI adoption and impact data in their preferred BI environment.
To access ActivTrak for BI - AI Insights, please refer to our platform-specific BI Template Setup Guide:
Tip: Save this report in your BI workspace for quick access and consider publishing it to your organization's BI service for broader executive visibility.
Contents
- The challenge you're facing
- How to solve it
- How to read this template
- AI Adoption Maturity
- Sharing and distribution
- Practical applications
- Learn more
The challenge you're facing
Most organizations have rolled out AI tools and assume adoption will follow. But without visibility into how — or whether — employees are actually using them, you're left with three costly blind spots:
- Unclear ROI: You've invested in AI licenses and enablement programs, but can't demonstrate whether the spend is translating into more productive work
- Uneven adoption: Some teams are getting real value from AI while others barely use it — but you don't have the data to know which is which or why
- Wasted licenses: Tools that looked promising at rollout may be sitting unused, quietly draining budget with every renewal cycle
The result? AI investments that are difficult to justify, enablement resources directed at the wrong teams, and license costs that could be reduced or reallocated.
How to solve it
ActivTrak for BI - AI Insights gives you concrete answers to the questions leadership is asking:
- Are people actually using the AI tools we've rolled out? See adoption rates, active users, and usage trends across your organization
- How deeply is AI embedded in daily work? Understand whether employees are dabbling or genuinely integrating AI into their workflows
- Is AI adoption making people more productive? Correlate maturity stages with utilization and core activity metrics to build the business case
- Which teams need enablement investment most? Identify where adoption is lagging and target programs where they'll have the biggest impact
- Which tools are worth renewing? See which AI tools are driving real usage and which licenses could be reduced or reclaimed
You don't need to be a data analyst. This report brings the insights you need and the context to act on them.
How to read this template
ActivTrak for BI - AI Insights includes two report pages: Impact and Adoption. The Impact page answers whether AI usage is making a difference; the Adoption page answers who is using AI and how much. Together, they give you the full picture.
Glossary: ActivTrak for BI - AI Insights
For a deep dive into each of the terms and metrics found in ActivTrak for BI - AI Insights, check out the supporting Glossary.
Classify your AI tools
Before using AI Insights, make sure your AI applications are classified correctly in ActivTrak. The dashboard only counts activity from applications that are:
- Classified as Productive
- Assigned to the AI Tools & Assistants category
If your AI tools aren't classified, the dashboards won't reflect actual AI usage. See Activity Classification to get set up.
Focus your attention
Both report pages can be customized using the filter options at the top of each report:
- Date: Select a time period to analyze (e.g., Last 6 Months)
- Team: Choose one or more teams; we recommend selecting the departments and/or business units that represent the top line of your organization (i.e., Sales, IT, Customer Success) to provide leadership with a high-level view
- AI Adoption Maturity: Filter users by their assigned maturity stage (Stage 0–3) to focus analysis on a specific cohort
Impact report
The Impact report shows whether deeper AI adoption is translating into more productive, higher-value work — giving you the data to guide enablement investments and resource allocation.
The big picture
The Overview card displays three headline numbers for the selected filters and date range: Users Analyzed, % AI Users, and % AI Usage. Think of this as your pulse check — before diving into the tables and trends, these three numbers tell you the scale of what you're looking at and how broadly AI is being used across the population.
If the % AI Users is low, we recommend you focus on adoption. If % AI Users is healthy but % AI Usage is low, AI isn't yet embedded in their day — that's an enablement story, not an access problem.
Connecting adoption to productivity
The AI Adoption Maturity Impact on Productivity Metrics table shows how productivity metrics compare across each AI Adoption Maturity stage. For each stage, you can see % Users in Stage, % Overall Utilization, and % Core Activity Efficiency.
Important: % Core Activity Efficiency requires Core Categories to be configured in Activity Alignment.
The question to answer: Do users at higher AI adoption maturity stages show stronger productivity and core activity metrics? If utilization and core activity efficiency are notably higher at Stages 2 and 3, that's evidence that deeper AI adoption is associated with more productive, higher-value work. If the numbers are flat or inconsistent across stages, it may signal that enablement efforts haven't yet translated into workflow change — or that your Core Activities aren't configured to reflect the work that matters most.
Team adoption and productivity
The AI Adoption Maturity Impact on Productivity Metrics by Team table shows the same maturity-to-productivity correlation as the table above, but broken down by team. Each row shows a team's distribution across AI Adoption Maturity stages using color-coded bars, with columns for each team's % AI Usage, % Overall Utilization, and % Core Activity Efficiency.
What to look for: A team heavily concentrated in Stage 0 or Stage 1 with lower efficiency metrics is your highest-priority target for enablement investment. You can click any column header to re-sort the table by a different metric — sorting by % Core Activity Efficiency, for example, can surface teams where AI adoption hasn't yet shifted time toward high-value work, even if overall utilization looks healthy.
Tracking progress over time
The Trend Analysis section shows how your workforce's AI adoption and productivity metrics are shifting over time. Use the Year Quarter, Year Month, or Week Date tabs at the top of the section to adjust the time interval across all three charts.
The User Distribution by AI Adoption Maturity stacked bar chart shows how the proportion of users at each AI Adoption Maturity stage has shifted over time. If Stages 0 and 1 (red and teal) are shrinking and Stages 2 and 3 (green and blue) are growing, your adoption programs are working. If the distribution is flat or regressing, something isn't sticking.
The Overall Utilization by AI Adoption Maturity and Core Activity Efficiency by AI Adoption Maturity line charts plot each metric over time, with a separate line for each stage, so you can see whether the productivity gap between stages is widening, narrowing, or holding steady. If Stage 3 users are consistently tracking above the other lines, it reinforces the value of moving users up the maturity curve. If the lines are bunched together or crossing frequently, productivity differences between stages may be less pronounced than expected — worth investigating alongside your Core Activities configuration.
Adoption report
The Adoption report measures AI tool adoption across your organization to identify where to invest in enablement programs and where licenses can be reclaimed or reallocated to optimize costs.
Which tools are getting used
The Top AI Tools by Usage chart ranks AI tools by Usage (Hours) — the share of total productive time spent in each tool, shown as a blue bar — with a green bar indicating the number of active Users.
- A long blue bar with a long green bar means a tool is widely used and deeply embedded — your highest-value tools.
- A short blue bar with a long green bar means many people are opening the tool but not spending meaningful time in it — worth investigating whether it's being used superficially or for quick lookups.
- A long blue bar with a short green bar indicates a small group is relying heavily on it, which may indicate a skills gap across the broader team.
- A tool showing users but near-zero usage may be licensed but effectively unused — a candidate for reclamation.
Where your workforce stands
The four cards show the total number of users currently classified at each AI Adoption Maturity stage for the selected filters and date range. They give you an instant read on where your population sits before you drill into the user-level data.
As a rule of thumb:
- A high Stage 0 count suggests that access or awareness isn't translating into usage.
- A large Stage 1 count means users are experimenting but haven't made AI habitual — the most common target for structured enablement.
- Growing counts for Stages 2 and 3 signal that AI is genuinely becoming part of how people work.
Who's using AI and how
The Users by AI Adoption Maturity Stage table lists every User in the filtered population with their assigned AI Adoption Maturity stage and the underlying signals that drove that classification. It's sorted by maturity stage by default, with Stage 3 users at the top, so your most advanced AI users are visible first.
For each user, you can see: % AI Usage, Avg. AI Usage (Mins/Day), AI Interaction Frequency (Sessions/Day), Usage Frequency, and AI Tools Used.
Note that AI Interaction Frequency and Usage Frequency measure different things. A user can be Daily (high Usage Frequency) with only one or two long sessions (low AI Interaction Frequency) — deeply engaged but focused. Another user might be Regular (medium Usage Frequency) but with many short sessions (high AI Interaction Frequency) spread across the day. Looking at both together, alongside Avg. AI Usage, gives you a clearer picture of how AI actually fits into someone's workflow.
Track adoption over time
The Usage Trend chart tracks adoption over time, using green bars to represent the total number of Users with AI activity and a blue line to represent Usage (Hours).
What you want to see: User counts and % AI Usage both trending upward, indicating that more people are using AI tools and using them more deeply.
- If user counts are stable but Usage (Hours) is rising, the same people are spending more time in AI tools — a sign of deepening adoption.
- If user counts are growing but Usage (Hours) is flat, more people are trying AI, but no one is using it more — typical of early rollout phases.
- A drop in both series together may signal disengagement worth investigating.
AI Adoption Maturity
The AI Adoption Maturity model provides a structured way to understand how deeply AI is embedded into a user's daily work. Each stage represents a progression — from no usage through research and exploration, task execution, and ultimately workflow integration — based on observable usage patterns.
| Stage | Maturity level | Behavior pattern | Interpretation |
|---|---|---|---|
| Stage 0 | No Usage | No AI activity recorded | User has not engaged with any AI tools |
| Stage 1 | Research Assistance | AI supplements a small fraction of work; not habitual | AI is being tried, explored, or used for quick inputs or research |
| Stage 2 | Task Execution | AI supports discrete tasks; consistent but bounded usage | AI is actively used to draft, analyze, or refine work outputs |
| Stage 3 | Workflow Integration | AI is used throughout the day; frequent context switching between AI and non-AI tools | AI is embedded in workflows and decision-making, producing near-final outputs |
Stages are inferred from three signals: Usage Frequency (habit formation), % AI Usage (workday penetration), and AI Interaction Frequency (workflow embedding, measured as the average number of sessions per day).
| Stage | Usage Frequency | % AI Usage |
AI Interaction Frequency (avg. sessions/day) |
|---|---|---|---|
| Stage 0 | No Usage | 0% | 0 |
| Stage 1 | Sporadic | Any | Any |
| Regular | ≤10% | Any | |
| Stage 2 | Regular | >10% to ≤20% | Any |
| Regular | >20% | ≤5 | |
| Daily | ≤10% | Any | |
| Daily | >10% | ≤5 | |
| Stage 3 | Regular | >20% | >5 |
| Daily | >10% | >5 |
Sharing and distribution
Ensure the right stakeholders receive this data by leveraging your BI platform's sharing capabilities:
- Export to PDF/PowerPoint: Create executive-ready presentations for board meetings or leadership reviews
- BI service publishing: Publish the report to your organization's BI service for browser-based access
- Email subscriptions: Set up automated email delivery of the report through your BI platform's subscription features
- Mobile access: Enable executives to view the report on their mobile devices through the BI mobile app
Tip: When sharing this report with leadership, pair the Impact and Adoption reports together. Present the Adoption page first to establish the breadth of usage, then use the Impact page to connect adoption to business outcomes.
Practical applications
Measuring AI ROI
Use the Impact report to correlate AI adoption maturity with productivity metrics:
- Establish a baseline: Identify the current distribution of users across maturity stages and their associated productivity levels before launching enablement programs
- Track improvement over time: Monitor how % Overall Utilization and % Core Activity Efficiency change as more users move into higher maturity stages
- Build the business case: Demonstrate the value of AI enablement programs to leadership with concrete productivity evidence
Identifying enablement opportunities
The Adoption report's maturity breakdown helps pinpoint where to invest in training and enablement:
- Focus on Stage 0 and Stage 1 users: Users with no usage or only sporadic research-level usage represent the highest enablement opportunity
- Segment by team: Use the Team filter to identify which departments are furthest behind in AI adoption and target them with tailored enablement programs
- Track adoption trends: Use the Usage Trend chart to confirm that enablement efforts are moving users into higher maturity stages over time
Optimizing AI tool licenses
Use the Top AI Tools by Usage chart to make smarter license decisions:
- Identify underused tools: Tools with low allocation percentages or few active users may be candidates for license reduction or consolidation
- Validate tool investments: Confirm that your highest-cost AI tools are also among the most adopted before renewing contracts