Why we normalise every metric to 0-100: the case for health scores over raw numbers
A 3.5× ROI and a 72% satisfaction score are both numbers, but not the same kind of number. Here's why every headline metric in The GAiGE gets mapped onto a common 0-100 scale before it hits a gauge.
Colin Cardwell
Spend ten minutes in any AI ROI dashboard and you'll notice the same problem. The page shows you half a dozen metrics (ROI multiplier, hours saved per week, adoption rate, satisfaction, maybe a Net Promoter Score) and you have no idea which ones are good, which ones are in trouble, and which ones you should care about most. Every metric has its own scale, its own distribution, and its own sense of "what's normal".
The GAiGE solves this by normalising every headline metric onto a common 0-100 scale before drawing it. A score of 80 on ROI means the same thing as 80 on satisfaction, which means the same thing as 80 on adoption. Strong. Worth keeping. Tell the team.
This post is about why we made that call, what the curves actually look like, and where the model breaks down.
The problem with raw numbersLink to this section
Raw metrics are precise and incomparable. That's a worse combination than it sounds.
A 2.5× ROI sounds impressive or weak depending on what you compare it with. A 65% satisfaction is 3.25 out of 5, which reads differently once you know where your other tools sit. A 72% adoption sounds strong until you notice that 72% is what you'd get if nearly three out of every ten seats sit idle, which is probably not what "strong" should mean.
The reader now has to hold three separate mental rubrics at once, and toggle between them on every glance. That cognitive tax is small in isolation and ruinous at scale, because it means your leadership team will, in practice, ignore the metrics they don't intuit and over-rotate on the ones they do. Usually that's the dollar figure, which is the one most open to methodology disagreement.
What a health score gives you insteadLink to this section
A health score is a normalised, opinionated mapping of a raw metric onto a 0-100 scale where:
- 0-40 means "needs attention" (red).
- 40-70 means "okay, watch it" (amber).
- 70-90 means "strong" (green).
- 90-100 means "excellent, tell the CFO" (bright green).
The bands are deliberately coarse. You don't need a dashboard that can distinguish a 73.2 from a 74.9; you need one that can tell you, at a glance, whether to celebrate, plan a learning push, or drop the tool.
Every gauge shows the raw value as its number (2.8×, 142 hours, 4.2/5), and its needle sits at the health score, so the colour-coded dials compare across metrics. The raw number is there for the auditor.
Why piecewise-linear, not linearLink to this section
The obvious next question: how do we pick where 0, 40, 70 and 100 fall? A naive answer is "draw a straight line between min and max". We don't do that, because a straight line is almost always wrong for ROI-style metrics.
Take ROI. On a pure linear scale from 0× to 10×, a 2× ROI would map to 20/100, which would tell you "this tool is in trouble". Except that a 2× ROI is fine. You got double your money back. On the other end, the difference between a 15× ROI and a 20× ROI is, in practice, noise: both mean "this tool is a runaway winner", and neither needs a further 20 points of dashboard celebration.
So we use a piecewise-linear curve with explicit anchor points. For ROI:
- 1× → 20/100 (break-even, but under-delivering)
- 2× → 45/100 (fine, but not compelling)
- 5× → 75/100 (strong)
- 10× → 95/100 (excellent)
- 20×+ → 100/100 (celebrate; the gain flattens)
The anchors aren't arbitrary. They reflect how a reasonable leadership team would describe the same number in words. 2× ROI is "fine". 5× ROI is "we should expand this". 10× ROI is "this is one of the best AI bets we've made". The curve encodes that judgement, so the dashboard shows the judgement automatically.
The four curves we shipLink to this section
Reports leads with gauges for what each pulse measures. Each of the four headline metrics has its own piecewise curve.
ROI. As above. Capped at 20× so a single enthusiastic response can't drag the whole strip off the scale.
Hours saved per user per week. 0h → 0, 1h → 35, 2h → 60, 3h → 80, 5h+ → 100. An hour a week per person is meaningful. Five hours a week (most of a working day reclaimed) is as good as it gets before we start suspecting the pulse is miscalibrated.
Satisfaction. A straight line: the rating out of 5, scaled to 100, so 4.2 scores 84 and the lowest rating, 1, scores 20. Satisfaction is the one metric where the raw number is already intuitive (everyone knows what 4.2/5 means), so we don't fight it.
Adoption. 30% → 30, 60% → 65, 80% → 85, 95%+ → 98. Adoption is the metric most prone to flattery: 80% sounds great until you remember the other 20% are seats you pay for and nobody opens. The curve is slightly generous, so that 80% of seats in use reads as strong.
The curves are the same for every organisation, and we publish them here so you can argue with them. Tell us if yours should differ.
The label is the pointLink to this section
Behind the 0-100 number sits a four-label scale: Needs attention. Okay. Strong. Excellent. Every gauge shows one of those four words directly under the number. The word does most of the work.
This matters because nobody makes a decision off "our ROI is 62.4/100". People make decisions off "our ROI is okay, our satisfaction is strong, and our adoption needs attention". Three words, one conclusion: we have a reach problem, not a quality problem.
The health score is the maths; the label is the translation. We keep both on the page because the maths can be checked and the label can be acted on. You can push back on the curve, and the word is what moves a leadership meeting along.
Where this model breaks downLink to this section
Three places it's worth calling out.
1. The anchors are opinions. We picked them based on what we've seen across the AI adoption work that seeded this product. A different sector might disagree: a law firm might reasonably argue that 2× ROI on a premium research tool is the ceiling, and 5× is fantasy. The curves are fixed in the product for now, which is why they're published.
2. Normalisation hides volatility. A metric bouncing between 65 and 75 reads as "stable strong" on the health-score scale, but the underlying raw number might have swung 40% week over week. Read the raw figure on each gauge, and compare periods in Reports, because the score alone can hide a swing.
3. Composite gauges are tempting and wrong. It's easy to mush four health scores into one "overall AI health" number. We refuse to do it. Collapsing ROI, hours saved, satisfaction and adoption into a single figure destroys the diagnostic value, which is precisely the pattern the individual scores were designed to reveal. Four numbers beat one number every time, so long as the four are comparable.
In shortLink to this section
Raw metrics are precise and incomparable. Health scores are coarser and comparable. For a dashboard read in thirty seconds by a leadership team that needs to make a decision, coarser and comparable wins. We show both numbers (the raw for defensibility, the normalised for decisions) and let the label do the heavy lifting.
Curious how the raw ROI number gets computed in the first place? The 2.5× rule post covers the upstream side of this: how we turn a handful of pulses into a defensible multiplier before it reaches the curve.
Colin Cardwell
Founder of AiGILE and The GAiGE. Colin has spent thirty years designing custom software for tough business problems.
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