How to Measure AI Adoption: 5 Metrics and a Worked Example
Learn how to calculate AI adoption, frequency, retention and task use, with clear definitions, a worked example and a practical reporting checklist.
Use cases / Software and AI adoption
How does your software fit the work?
See which teams use the software you invested in and which familiar processes still surround it. Use Isaac, Orchard’s AI, to compare the activity and focus training or process changes where further investigation is warranted.
For IT, transformation, and operations leaders
A license or a login leaves important questions unanswered.
Software usage tells only part of the story. Spreadsheets, manual transfers, and workarounds can remain part of everyday work. Understanding that context helps you decide where training, access, or process design needs attention.
How are teams using software and AI across their everyday work?
What your team can
put into practice.
A scoped adoption review: observed use, context to confirm, and a team-specific training or process decision.
1
Set the scope
Choose the process, participating teams, applications, and comparison periods.
2
Review observed use
Use Isaac, Orchard’s AI, to compare software activity across groups and inspect the surrounding work.
3
Understand the reasons
Discuss the patterns with the team. Low use can reflect access, task fit, or a process that still depends on other tools.
4
Measure useful change
Review the effect of training or a revised process against completion, quality, or service measures.
Choose the support that fits the task.
Low use may reflect access, training, or a tool that does not fit the job. Ask the team before choosing a response. Judge the change by task quality, completion, and rework, alongside usage.
Possible starting points
- A professional-services firm reviewing how teams use AI
- An established business examining work across its applications
- A department deciding where to focus training
What we confirm first
Supported endpoints and an agreed collection scope. AI activity and copy/paste patterns depend on capture settings. Content access needs explicit review.
What makes the evaluation useful
Observed use is a starting point. It does not prove effective adoption, license savings, AI quality, or data-loss prevention. Bring your existing adoption reports so we can identify a specific gap.
Practical guides for your team
Methods, examples, and review questions to help you investigate the work.
Why Employees Aren’t Using Your AI Tools—and What to Check First
Find out why employees aren’t using your AI tools. Walk through a real task, identify the barrier and choose a useful change—or keep the current method.
See what you could change.
Know where to start.
See Isaac investigate how teams use software and AI, with the surrounding work in view. Discuss the questions your current reports leave unanswered and where Orchard could help.
- See a relevant product example
- Review your workflow and setup
- Decide whether it’s a fit
No installation or data upload needed for your demo.
What to expect from your demo