How do you know it's working? Picking the right metrics

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Start with a simple metric mix
Use a few leading and lagging metrics instead of one number
A balanced scorecard shows whether the tool is used, whether it works, and whether it is worth it.
Usage: Is the team actually using the tool in the intended workflow?
Time saved: How many minutes, clicks, or handoffs did it remove?
Accuracy: Are there fewer mistakes, edits, or escalations?
Outcome: Did service, revenue, conversion, or cost improve?
Track behavior, performance, and business impact together.

Leading vs. lagging metrics
Some metrics give early signals, others prove final results
You need both: one helps you steer quickly, the other shows whether the business actually benefited.
Leading metrics: Adoption, completion rate, prompt success, and response quality.
Why they help: They tell you early if people can use the tool and trust it.
Lagging metrics: Cycle time, error rate, cost per task, CSAT, and conversion.
Why they matter: They show whether the AI changed the business result.
Use leading metrics to improve early and lagging metrics to prove value.

Which metrics mislead you?
Avoid vanity metrics that look impressive but say little
A useful metric helps you make a decision. A vanity metric mostly helps you make a slide.
Total prompts: More prompts do not automatically mean more value.
Great demos: A polished demo does not prove repeatable performance.
Cheap model cost: Low model cost can still hide rework and wasted labor.
One-number thinking: No single metric explains adoption, quality, and impact.
Choose numbers that drive action, not just attention.

How do you set a baseline?
Measure the current process before you judge the new one
You cannot claim improvement if you never recorded where the process started.
Pick one workflow: Choose one specific task, team, and output.
Capture today’s numbers: Record current time, volume, errors, and outcomes.
Define success: Set a target like 20% faster, fewer errors, or better conversion.
Review on a rhythm: Check weekly or monthly so the team can adjust quickly.
Baseline first, then compare fairly.

Small business takeaways
Keep metrics practical, few, and tied to real work
Small businesses do not need a giant analytics program: they need a clear way to know whether AI helps.
Measure the workflow: Tie metrics to a real task people do every week.
Pick 3 to 5 metrics max: Too many numbers make action harder.
Include one business outcome: Save time, reduce errors, improve service, or lift conversion.
Review with the team: The people doing the work often explain the numbers best.
Adjust fast: If the tool is not helping, change it before bad habits grow.
The right metric is the one that helps you decide what to do next.

Lesson done.

Up next

What is uptime, and what happens when AI goes down?

Where does this fit your business? Get your AI plan

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The short answer

You know AI is working when a few metrics tied to real work improve against a baseline you measured first: usage, time saved, accuracy and one business outcome. Keep it to three to five numbers and review them with the team, so you can change the tool quickly if it is not helping.

Three things to remember

01Use early signals like adoption to steer, and results like error rate and cost per task to prove value.
02Avoid vanity metrics such as total prompts or polished demos; they do not show repeatable value.
03Pick three to five metrics, include one business outcome, and review them with the people doing the work.

Every slide, in words

01
How do you know it’s working? Picking the right metrics
Measure the results that actually matter to the business
AI is only valuable if it improves the work, not if it just feels impressive.
Efficiency: Did the task get faster or easier to complete?
Quality: Did accuracy, consistency, or response quality improve?
Outcome: Did the change create a business result people care about?
Good metrics connect AI activity to business value.
02
Start with a simple metric mix
Use a few leading and lagging metrics instead of one number
A balanced scorecard shows whether the tool is used, whether it works, and whether it is worth it.
Usage: Is the team actually using the tool in the intended workflow?
Time saved: How many minutes, clicks, or handoffs did it remove?
Accuracy: Are there fewer mistakes, edits, or escalations?
Outcome: Did service, revenue, conversion, or cost improve?
Track behavior, performance, and business impact together.
03
Leading vs. lagging metrics
Some metrics give early signals, others prove final results
You need both: one helps you steer quickly, the other shows whether the business actually benefited.
Leading metrics: Adoption, completion rate, prompt success, and response quality.
Why they help: They tell you early if people can use the tool and trust it.
Lagging metrics: Cycle time, error rate, cost per task, CSAT, and conversion.
Why they matter: They show whether the AI changed the business result.
Use leading metrics to improve early and lagging metrics to prove value.
04
Which metrics mislead you?
Avoid vanity metrics that look impressive but say little
A useful metric helps you make a decision. A vanity metric mostly helps you make a slide.
Total prompts: More prompts do not automatically mean more value.
Great demos: A polished demo does not prove repeatable performance.
Cheap model cost: Low model cost can still hide rework and wasted labor.
One-number thinking: No single metric explains adoption, quality, and impact.
Choose numbers that drive action, not just attention.
05
How do you set a baseline?
Measure the current process before you judge the new one
You cannot claim improvement if you never recorded where the process started.
Pick one workflow: Choose one specific task, team, and output.
Capture today’s numbers: Record current time, volume, errors, and outcomes.
Define success: Set a target like 20% faster, fewer errors, or better conversion.
Review on a rhythm: Check weekly or monthly so the team can adjust quickly.
Baseline first, then compare fairly.
06
Small business takeaways
Keep metrics practical, few, and tied to real work
Small businesses do not need a giant analytics program: they need a clear way to know whether AI helps.
Measure the workflow: Tie metrics to a real task people do every week.
Pick 3 to 5 metrics max: Too many numbers make action harder.
Include one business outcome: Save time, reduce errors, improve service, or lift conversion.
Review with the team: The people doing the work often explain the numbers best.
Adjust fast: If the tool is not helping, change it before bad habits grow.
The right metric is the one that helps you decide what to do next.

More in part 4

Next step

Where does this fit your business?

Six quick questions about your business, then your AI plan: what to start with, what a person still checks, and the lessons that go with it. Or bring it straight to a free thirty-minute call.

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