A polished dashboard is easy to demo. The harder questions to answer are where the numbers come from, what activity the platform can actually see, and whether it can connect AI usage and spend to business results.
Key Takeaways
- Start with coverage. A platform can’t measure AI activity it can’t see across the tools, teams, and workflows your organization actually uses.
- Ask how every important metric is defined and calculated before comparing numbers across platforms.
- Look past company-wide adoption totals. Useful AI ROI measurement should connect usage and spend to specific teams, workflows, and outcomes.
Use these seven questions to pressure-test those claims during a demo or pilot.
1. What AI Activity Can the Platform Actually See?
Start with coverage. Some platforms only measure AI tools connected through specific integrations, while others can identify usage outside the approved stack. Incomplete coverage can make adoption, spend, and governance data look more complete than it is.
Ask:
- Which AI tools and models are supported today?
- How is usage detected or collected?
- What happens when employees start using a new tool?
- Can the platform identify shadow AI?
- Are there parts of your environment it cannot measure?
Then test those claims against your own environment rather than relying only on the demo.
2. How Are the Metrics Defined?
Terms such as active user, adoption, utilization, productivity, and ROI can mean very different things depending on the platform.
An active user might mean someone who logged in during the month, someone who completed an AI-assisted workflow, or someone who generated a certain amount of activity. Each definition produces a very different number.
Before comparing platforms, ask for plain-language explanations of the metrics that matter to you:
- What counts as active AI usage?
- How is adoption calculated?
- How is proficiency measured?
- What goes into the ROI calculation?
- Which numbers are directly measured, calculated, estimated, or self-reported?
A dashboard number is only useful if you know what produced it.
3. Can It Attribute Usage and Spend to the Work Generating Them?
A company-wide AI usage total shows how much AI activity there is, but not which work is driving it or what value it produces.
Ask whether the platform can break usage and spend down by:
- Team
- Department
- Workflow
- Application
- Agent
- Use case
This is where aggregation becomes attribution. Instead of knowing only that AI spend increased, you can see which work generated the increase and investigate whether that investment is producing a result.
Larridin’s Token Spend & Insights connects AI spend and usage to the teams, tools, agents, and workflows generating it. The platform also tracks AI activity across people, teams, agents, workflows, spend, proficiency, and outcomes.
4. Can It Distinguish Human and Agent Activity?
As organizations use more AI agents, a single usage number doesn’t tell you much. Ask whether the platform can distinguish activity initiated by people from activity generated by agents and automated workflows.
That separation matters for questions such as:
- Which costs come from employee AI use versus autonomous activity?
- Which workflows are driving token consumption?
- Are agents creating measurable output or simply generating more activity?
- How much human review or verification follows agent-assisted work?
5. Can It Connect AI Activity to an Outcome?
Usage isn’t ROI. Ask the provider to show how the platform connects AI activity or spend to a measurable result. Depending on the use case, that could include:
- Cycle time
- Cost per task
- Error or rework rates
- Revenue
- Cost avoided
- Engineering delivery or quality
- Another business metric your organization already tracks
The important part is the connection between the AI activity and the outcome.
An AI ROI framework can help define those relationships before you evaluate how well a platform measures them.
6. Can You Validate the Platform With Your Own Data?
Case studies can show what happened elsewhere. They can’t tell you whether the same measurement will work inside your organization.
Ask whether you can validate the platform against a known sample of your own data before committing. For example:
- Does it find the AI tools you already know employees use?
- Does its spend data reconcile with known bills or usage records?
- Can you trace a dashboard result back to the underlying activity?
- Can your team reproduce or explain an important calculation?
This is especially useful for metrics that may eventually appear in finance, executive, or board reporting.
You should be able to answer “Where did this number come from?” without reverse-engineering the dashboard after rollout.
7. What Data Does It Collect, and Who Can See It?
AI measurement can involve employee and workflow data, so the evaluation also needs a privacy and governance check.
Ask:
- Does the platform capture content or only metadata?
- Does it record prompts, messages, keystrokes, or document contents?
- Is individual-level data available?
- Who can access that data?
- What access controls and retention policies apply?
- Can reporting be limited to the level of detail the organization actually needs?
More data collection doesn’t necessarily mean better measurement.
Larridin uses a metadata-based approach and doesn’t read or record keystrokes, emails, private messages, or information in documents.
Bring the Questions to the Demo
An enterprise AI ROI platform should be able to explain more than what appears on the dashboard.
Before buying, make sure you understand:
what it can see → how it calculates the numbers → how it attributes activity → how it connects AI to outcomes → what data it collects
Those answers make it much easier to compare platforms on the measurement problem you actually need to solve.
Frequently Asked Questions
What should you look for in an enterprise AI ROI platform?
Look for coverage across the AI tools your organization uses, clear metric definitions, spend and usage attribution, outcome measurement, appropriate visibility into human and agent activity, and data collection that fits your privacy and governance requirements.
How can you tell whether an AI ROI platform’s metrics are reliable?
Ask how each important metric is calculated, which underlying data sources it uses, and whether the number is measured, estimated, or self-reported. When possible, validate important results against your own data before relying on them for executive reporting.
Is AI usage data enough to measure ROI?
No. Usage can show adoption or activity, but ROI requires a connection between the investment and a measurable result. That usually requires cost, attribution, and outcome data in addition to usage.
Should an AI ROI platform monitor individual employees?
Not necessarily. The level of detail should match the measurement or governance question the organization needs to answer. Collecting more employee data than necessary can create privacy and trust problems without improving ROI measurement.
See What’s Behind Your AI ROI Numbers
Larridin connects AI usage, spend, proficiency, agents, workflows, and business outcomes so leaders can trace AI investment back to the activity and results behind it.