Your organization is investing in AI tools, but are people actually using them? And when they do, is it making a difference, and is it happening safely? ActivTrak for BI - AI Insights answers all three questions by measuring how widely AI tools are adopted across your workforce, how deeply they're embedded in daily work, whether that adoption is translating into measurable productivity gains, and whether usage stays within your approved tool list.
- 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
- Flag unsanctioned ("shadow") AI tool usage before it becomes a security or governance risk
- Combine AI adoption data with other business metrics for more comprehensive executive reporting
Available to customers with the ActivConnect API (Add-on) and the AI Insights (Add-on), and one of the following ActivTrak packages: Workforce Management, Productivity Optimization, and the Workforce Management + Productivity Optimization Bundle. 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 report
- 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 four 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
- Unsanctioned usage: Employees may be turning to AI tools your organization hasn't approved or reviewed, creating security and compliance exposure you can't see
The result? AI investments that are difficult to justify, enablement resources directed at the wrong teams, license costs that could be reduced or reallocated, and governance gaps that go unnoticed until they become incidents.
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
- Is our AI usage secure and sanctioned? See which tools are approved vs. unapproved, and where unsanctioned usage is concentrated
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 report
ActivTrak for BI - AI Insights includes three report pages: Impact, Adoption, and Compliance. The Impact page answers whether AI usage is making a difference; the Adoption page answers who is using AI and how much; the Compliance page answers whether that usage is sanctioned. 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. All three report pages only count applications assigned to the AI Tools & Assistants category. Within that category, the Productive / Unproductive classification determines how each tool is treated:
- The Impact and Adoption pages only count activity from AI Tools & Assistants classified as Productive
- The Compliance page counts AI Tools & Assistants regardless of Productive/Unproductive classification, and uses that same classification to set each tool's AI Tool Status: Productive tools show as Approved, and Unproductive tools show as Unapproved
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
All 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; available on the Impact and Adoption pages only. The Compliance page filters by Team and Date only, since a tool's Productive/Unproductive classification (and the AI Tool Status it drives) doesn't depend on maturity stage
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 show the scale of what you're looking at and how broadly AI is used across the population.
What to look for: If % AI Users is low, we recommend focusing 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 section has two parts:
- Stage 0 Baseline: a reference card showing, for users with no AI usage: Users in Stage, Overall Utilization, Workday Span, Focused Time, and Core Activity Eff.
- Stage 1 / 2 / 3 comparison table: for each higher stage, % Users, and the Δ (delta) vs. the Stage 0 baseline for % Overall Utilization, Workday Span (Hrs), % Focused Time, and % Core Activity Eff. shown with ▲/▼ arrows
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 positive deltas on the metrics that matter most? A consistently positive Δ % Core Activity Efficiency across Stages 1-3 is the clearest evidence that deeper AI adoption shifts time toward higher-value work, even if Δ % Overall Utilization is flat or negative, since higher-maturity users may simply be spending less raw time to produce more core-activity output. If the Core Activity Efficiency deltas are flat or negative, 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 by Team table shows the same maturity-to-productivity correlation, but broken down by team. Each row shows a team's distribution across AI Adoption Maturity stages using color-coded percentage columns (Stage 0-3), with additional columns for the team's % AI Usage, % Overall Utilization, Workday Span (Hrs), % Focused Time, and % Core Activity Eff.
What to look for: A team heavily concentrated in Stage 0 or Stage 1 with lower Core Activity Efficiency is your highest-priority target for enablement investment. 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 productivity metrics are shifting over time, broken out by AI Adoption Maturity stage. Use the Year Quarter, Year Month, or Week Date tabs at the top of the section to adjust the time interval across both charts.
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.
What to look for: If Stage 3 users are consistently tracking above the other lines on Core Activity Efficiency, it reinforces the value of moving users up the maturity curve. If the lines bunch together or cross frequently, productivity differences between stages may be less pronounced than expected, which is 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 to reclaim or reallocate licenses 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, with a companion bar showing the number of active Users. The tool list reflects whichever applications your account has classified into the AI Tools & Assistants category, so it will vary by organization.
- A long Usage (Hours) bar with a long Users bar means a tool is widely used and deeply embedded; your highest-value tools
- A short Usage (Hours) bar with a long Users 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 Usage (Hours) bar with a short Users 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 team-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 Stage 2 and Stage 3 signal that AI is genuinely becoming part of how people work
Who's using AI and how
The AI Adoption by Team table lists every Team in the filtered population, their spread of AI Adoption Maturity stages, and the underlying signals that drove that classification.
For each team, you can see: % of Users, % AI Usage, AI Usage (Mins/day) per User, AI Interaction Frequency (Sessions/Day), Usage Frequency, and AI Tools Used per User.
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 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, with an overlaid % AI Usage line.
What you want to see: Stages 0 and 1 (red and teal) shrinking while Stages 2 and 3 (green and blue) grow, together with a rising % AI Usage line; that combination means more people are using AI tools and using them more deeply. If the stage distribution is flat or regressing while % AI Usage holds steady or drops, your adoption programs aren't sticking.
Compliance report
The Compliance report answers a question neither Impact nor Adoption cover: which AI tools are actually running in your environment, and are they the ones you've sanctioned? Use it to catch shadow AI, tools employees have started using without going through IT or security review, and to quantify the exposure.
Where you stand on approved vs. unapproved AI
Two headline cards summarize approved vs. unapproved usage for the selected filters and date range:
- Approved: Approved AI Tools and Using Approved AI, each shown with a supporting percentage: "X% of Y Discovered" (the share of all discovered AI tools that are approved) and "X% of Y Users" (the share of the analyzed population using at least one approved tool)
- Unapproved: Unapproved AI Tools and Using Unapproved AI, each shown with a supporting percentage: "X% of Y Discovered" (the share of all discovered AI tools that are unapproved) and "X% of Y Users" (the share of the analyzed population using at least one unapproved tool)
What this tells you: treat Tools Discovered as the denominator for tool-level exposure and Users Analyzed as the denominator for people-level exposure. A small Unapproved tool count paired with a large Unapproved user count means a few unsanctioned tools have spread widely, a bigger governance risk than the tool count alone would suggest.
What's running in your environment
A table listing every AI tool ActivTrak has detected in the environment, each with its AI Tool Status, number of Users, and Usage (Hrs).
What this tells you: examine Usage (Hrs) within the Unapproved rows to prioritize governance conversations; a heavily used unapproved tool is a bigger and more urgent risk than one with only a handful of hours logged.
Where unapproved usage is concentrated
A table that breaks down Users Analyzed, % Users Using Approved AI, % Users Using Unapproved AI, All AI Tools, Approved Tools, and Unapproved Tools per Team.
What this tells you: a team with a high % Users Using Unapproved AI is your highest-priority target for governance outreach. Pair this with the Adoption report's team breakdown; a team that's both heavy on unapproved tools and driving high overall AI usage is relying on unsanctioned tools to get real work done, which points to a gap in your approved tool catalog rather than a discipline problem.
Is shadow AI gaining or losing ground?
A combined chart over time displaying stacked bars of Approved Tools and Unapproved Tools counts, with % Users Using Approved AI and % Users Using Unapproved AI lines overlaid.
What this tells you: watch the % Users Using Unapproved AI line. Flat or declining while your approved tool catalog grows means enablement and governance are moving together; a rising line means unsanctioned tools are gaining ground faster than your approved rollout, an early warning worth acting on before the next renewal or audit cycle.
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 with frequent sessions using 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: Note the Stage 0 baseline figures — Overall Utilization, Workday Span, Focused Time, and Core Activity Efficiency — before launching enablement programs
- Track improvement over time: Monitor how the Δ vs. baseline for % Overall Utilization, Workday Span (Hrs), % Focused Time, and % Core Activity Efficiency changes as more users move into higher maturity stages
- Build the business case: Demonstrate the value of AI enablement programs to leadership with concrete, baseline-relative 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 AI Adoption by Team chart to confirm that enablement efforts are moving users into higher maturity stages over time
Optimizing AI tool licenses
Use the AI Tools Discovered 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
Assessing compliance exposure
Use the Compliance report to quantify shadow AI risk:
- Establish your baseline: Note the current split of Approved vs. Unapproved tool usage before starting a governance push
- Prioritize the highest-usage unapproved tools: Sort the AI Tools Discovered table by Usage (Hrs) among Unapproved tools to target the biggest risks first
- Watch team-level exposure: Use the AI Tool Usage breakdown to find where unsanctioned usage is concentrated, and pair governance outreach with enablement so an approved alternative is easy to adopt