SightLift

How your AI-Use Score works

Your AI-Use Score is a number from 0 to 100 that measures how well your team uses AI — not just how much. It's the average of five dimensions, each also scored 0 to 100. You can weight the dimensions to match your strategy, and the score page always shows the handful of numbers behind each one.

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Every point traces back to something we counted in your own data — conversations, seats, dollars, and shared-tool use. Scores exist at the team and company level only: there is no individual score. It's a directional signal — read which way it is moving, not a 1-point difference between two teams.

What the score measures

Adoption — is the whole team on board?

Three ingredients, all counted from your usage:

Activation — of the seats you pay for, how many were actually used this month. We only score this when we can see a seat list that covers the people who were active: if the list we have is smaller than the number of people working, we leave this out rather than guess. You can tell us the seat count you pay for and we’ll use it: there is no field for it in the app yet, so ask your SightLift account team to record it. Once it is set, the card reads declared so the figure is never mistaken for one we counted.
Regularity — of the workdays in the month, how many your active people use AI on. A daily habit beats a monthly binge. Weekend work still counts toward the habit, and nobody is scored above 100%.
Depth — of the seats you pay for, how many belong to someone for whom AI is part of the working week: they use it on at least three workdays in five, in at least two different places (Claude Code and Cowork are two places; the same tool seen two ways is one). Activation and Regularity cannot see this on their own — a team of ten dabblers and a team of ten heavy users read the same on both. If we only ever see one place you work, we leave Depth out rather than score everyone zero for a product you did not buy.

Goes up when quiet seats come back to life, people build a habit, and heavy use spreads beyond a few individuals.

We used to include a third ingredient, Balance — how evenly use was spread across the team. It was removed: whether one person does more AI work than another is a fact about how you divide work, not about how well your team uses AI, and it meant a heavy user could lower your score by working more.

Sophistication — is AI doing real work?

Two ingredients:

Advanced practice — of the seats you pay for, how many belong to someone whose work goes through a skill, a shared tool your team chose, or a sub-agent. It counts people, not conversations: one person doing this all month is one seat, and the number rises when somebody new starts.
Output — what the work produced. Code, analysis, plans, and documents count for more than a quick email rewrite.

We grade the work, not the typing: using a skill to do a complex job in one step counts as the complex job. So as your team adopts shared tools and agents, conversations get shorter while the work gets bigger — and the score rewards that.

The tools that come with the assistant don’t count here — everyone has those. What counts is what your team chose or built.

Goes up when advanced practice spreads to more people, and when more conversations end in substantial output. It does not move when one person simply takes on bigger tasks — that is what the whole team catching up looks like, not one expert pulling further ahead.

Efficiency — how much of your spend does real work?

Six leaks, all counted in dollars and compared to your total spend:

Retry loops — the same ask sent again and again in one day.
Abandoned sessions — long conversations that ended with nothing produced.
Error churn — turns burned on errors and the retries after them.
Re-sent context — the same material pushed to the model over and over.
Model overkill — premium models doing routine tasks. For most teams, the biggest leak.
Non-work spend — clearly personal topics, only if your AI use policy says they count. Some companies bless personal use, and that's fine.

We grade the leaks, not the bill: 100 means no measurable waste, and spending more never lowers this score — only wasteful spend does.

Goes up when the waste dollars shrink — each Efficiency action names the exact leak and what it costs per month.

Reuse — does the team run on shared, proven tools?

Three ingredients, all counted across your shared tools — skills, templates, automations:

Used, not redone — when work matches a shared tool the team already has, how often the tool was used instead of the work being done by hand.
Coverage — of the repeat work we can see in your data, how much has a shared tool at all. If everyone separately asks AI to distill the weekly all-hands, that's the same work done twenty times with no shared tool — and the action is to ship one skill everyone uses.
Kept fresh — of the tools you have ever published, how many were revised or retired in the last 90 days, instead of just piling up alongside newer ones.

We count the habit, not the logo: skills your team built before SightLift count from day one — when the same shared tool shows up across your team's work, we recognize it automatically, no setup required.

Reuse isn't scored until we can see shared-tool activity or your first published capability — until then your score is simply the average of the other dimensions, and the score page shows how to activate it.

Goes up when people adopt the standard tool instead of hand-rolling the work, when repeated workflows get captured and published to your toolbox, and when a tool that's gone stale gets revised or retired instead of left to accumulate.

Governance — is sensitive work in the right hands?

Two ingredients, both measured only when you give us the context to judge fairly:

Right hands — once we know each person's Function Sensitivity (set in Configuration → Company profile → Org & roles): the share of sensitive work (legal, finance, personnel, security, strategic internal and strategic external) handled by people cleared for it.
Policy compliance — once we have your AI use policy: how well usage follows each rule we can measure — sensitive work staying on company accounts, AI work staying on approved tools, personal data (emails, phone numbers, card numbers) staying out of chats. Your governance score is the average of the rules we're actively checking, and each violation pattern becomes a clear action on your plan.

We measure against your rules, not our guesses: without roles or a policy, the dimension simply isn't scored, and sensitive findings appear as notes instead. No written policy yet? We'll help you start from a proven template.

Goes up when sensitive work lands with the right people and policy violations fade to zero.

How the overall score is put together

The score is the average of the dimensions we can measure, using your weights (equal by default — set them in Configuration → Company profile → Score weights to match the behaviors you want to drive). If a dimension can't be measured yet, it is skipped, never guessed. Teams with too little data in a month show "not enough data" rather than a shaky number.

Weights are a strategy lever, not a way to inflate the number: raising a weight makes that dimension matter more and makes its gaps cost more.

How actions connect to the score

Every action in your plan names the dimension it moves and shows the points it could add — computed from your own numbers, with the math on the card. For example: "4 of your 12 seats have gone quiet. Re-activating them raises Activation from 67% to 100% — about +3 points."

Two kinds of projection, always labeled:

Kind	What it means
Measured	Computed directly from observed counts — bypasses of a shared tool, waste dollars, quiet seats.
Estimate	Relies on one stated assumption, shown on the card (for example, "assumes this group's routine work moves up one complexity tier").

Some cards also show a dollar figure, like the monthly cost of manual work an automation would remove. Points rank the plan; dollars size the prize.

How to read it

The score is shown per team and for the whole org as a ribbon. The Weekly / Monthly / Quarterly toggle zooms the lookback (about 6 months / 18 months / 3 years).

We report the number and what it did, and we don't grade it. There used to be a four-band table here, one grading word per range of the score, and the same bands picked the colour every score in the product was printed in. A band is a verdict about your team wearing the clothes of a measurement, so the words and the colours went together: the score is plain text now, and the only thing that carries colour is the change beside it.

What you'll see	What it means
↑ / → / ↓	Direction on the ribbon, versus the previous period at whichever grain you're looking at.
▲ 3 this month	The change on the score itself, with the size of the move spelled out.
steady	The score moved less than 3 points. Banding is deliberate, so a 1-point wiggle can't read as a trend.
no change yet	There is no previous period to compare against. Not measured is a different fact from not moved.

Hover any cell to see the dimensions behind that score; click it to open the conversations underneath.

Privacy

Scores exist at the team and company level only. There is no individual score, and there never will be — managers drill into examples of work patterns, never into a ranked list of people. Where CRM outcomes are connected, they only help validate the weights; they are never an input to the score.

Questions about your score or your rollout? Contact your SightLift account team.