The AI Usage Dashboard for Adoption, Shadow AI, and Utilization

AI is already in use; you just need to know how.

Your leadership wants to know whether AI spend is producing anything. Security and compliance want to know whether regulated data is going into tools nobody vetted. To answer both, you need- reliable usage data, segmented by team, over time.

Without it, you run into four problems:

• AI usage is scattered across apps and websites, making it hard to see the full picture.
• Comparing AI adoption across users, teams, and time periods often means manual spreadsheet work.
• Team changes can disrupt historical group data, making long-term trends harder to trust.

Questions this page answers

1. What share of our workforce uses AI at all?
2. Which AI tools have real traction, and which were tried once?
3. Which departments are ahead, and which need enablement?
4. Where is unapproved AI usage concentrated?
5. Is AI adoption showing up in utilization, or not yet?
6. Can I hand a defensible report to the exec team monthly?

Find shadow AI before an auditor does

Shadow AI is rarely malicious. Someone has a deadline, the sanctioned tool isn’t provisioned yet, and a free personal account fills the gap. The governance problem isn’t intent – it’s that the activity is invisible, which means data-handling risk sits outside every control you’ve documented.

Once your approved AI tools are defined, the dashboard reports your AI landscape in two buckets:

• Approved tools – measure adoption, depth of use, and which teams are behind.
• Unapproved tools – see the tool, the volume, the teams, and the specific users.

The response is a judgement call, not an automatic penalty. Heavy unapproved usage in one department usually means the approved alternative is missing, poorly onboarded, or not licensed widely enough.

From discovery to decision

1. Spot the unapproved tool and its volume.
2. Identify which teams and users drive it.
3. Decide: enable an approved alternative, train, or restrict.
4. Restrict in BrowseControl when that’s the call.
5. Control data movement with AccessPatrol.
6. Re-check the dashboard next period to confirm the shift.

Related: shadow AI prevention and preventing AI data leaks.

Define your approved AI toolset

The Authorized AI Tool view is where governance becomes measurable. Administrators list the AI applications and websites the organization approves – globally, or per group when engineering, marketing, and finance are sanctioned for different tools.

Everything reported afterwards is framed against that list, which is what turns an activity log into a governance artefact you can show an auditor.

Configure at: organization level, or group level for teams with different sanctioned stacks.

Pair it with a written policy

A tool list without a published policy is hard to enforce and harder to defend. Our free AI workplace policy template and workplace monitoring policy template cover the acceptable-use and disclosure language most teams need before the first report goes out.

AI usage, measured against productive time

An AI usage number on its own invites the wrong conclusion in both directions. High usage looks like productivity. Low usage looks like resistance. Neither holds up without a denominator.

• User-level utilization appears alongside AI usage in the breakdowns.
• Each AI tool shows the average utilization associated with the people using it.
• The department heat map toggles between AI usage and utilization rate, so you can read the same teams on both axes.

If your managers prefer productive hours to percentages, utilization targets can be configured either way – see version 12.0.5. For the wider picture, see employee productivity tracking and measuring AI productivity impact.

Weighing specific vendors? See our ActivTrak alternative, Teramind alternative, and Insightful alternative pages.