What the organisation is actually doing with the AI it pays for, read across every seat and every vendor that reports. Findings are things you could act on this week; the tables underneath are the evidence for them, and the bottom of the page names what we still cannot see.
james.o@naturebaby.com → claude-sonnet-5; Claudia.Z@naturebaby.com → claude-sonnet-5; don.p@naturebaby.com → claude-opus-5; jacob@naturebaby.com → claude-sonnet-5. Same product, different tiers. Worth deciding whether that split is intentional or just how each person happened to be set up.
Bash (328 runs across 7 seats), mcp_tool (182 runs across 5 seats), Read (118 runs across 5 seats), Edit (109 runs across 4 seats), TaskUpdate (56 runs across 4 seats), Write (39 runs across 5 seats), ToolSearch (34 runs across 5 seats), TaskCreate (34 runs across 3 seats), SendUserFile (32 runs across 6 seats), Skill (23 runs across 4 seats), AskUserQuestion (16 runs across 4 seats), Projects (11 runs across 2 seats). Repeated practice across people is where a shared skill or template pays for itself.
mcp_tool ~9.1s over 182 runs. Slow tools stall the whole turn, and they are usually the cheapest thing on this page to fix.
| Seat | Model | Calls | Tokens | Share |
|---|---|---|---|---|
| james.o@naturebaby.com | claude-sonnet-5 | 65 | 5.3M | 1% |
| Claudia.Z@naturebaby.com | claude-sonnet-5 | 667 | 141.5M | 26% |
| don.p@naturebaby.com | claude-opus-5 | 251 | 30.2M | 5% |
| jacob@naturebaby.com | claude-sonnet-5 | 17 | 1.4M | 0% |
| hengda.q@naturebaby.com | no calls in the window | |||
| anna.t@naturebaby.com | no calls in the window | |||
| amy.w@naturebaby.com | no calls in the window | |||
| isobel.a@naturebaby.com | no calls in the window | |||
| Tool | Runs | Seats | Avg | Rejected |
|---|---|---|---|---|
| Bash | 328 | 7 | 3.2s | – |
| mcp_tool | 182 | 5 | 9.1s | – |
| Read | 118 | 5 | 87ms | – |
| Edit | 109 | 4 | 12ms | – |
| TaskUpdate | 56 | 4 | 17ms | – |
| Write | 39 | 5 | 22ms | 1 |
| ToolSearch | 34 | 5 | 8ms | – |
| TaskCreate | 34 | 3 | 22ms | – |
| SendUserFile | 32 | 6 | 711ms | – |
| Skill | 23 | 4 | 17ms | – |
| AskUserQuestion | 16 | 4 | 2ms | – |
| Projects | 11 | 2 | 1.1s | – |
| Grep | 4 | 2 | 19ms | – |
| Glob | 3 | 2 | 166ms | – |
| WebFetch | 3 | 1 | 3.1s | – |
| SearchMcpRegistry | 2 | 1 | 459ms | – |
| WebSearch | 2 | 1 | 725ms | – |
| PushNotification | 1 | 1 | – | – |
| Agent | 1 | 1 | 203.1s | – |
| ListConnectors | 1 | 1 | 370ms | – |
| SendUserMessage | 1 | 1 | 1ms | – |
Rejected counts the times a person declined an edit the agent proposed. They are not failures – they are the only place a harness tells us a human pushed back, which is the control plane working.
1 of 122 decisions went against the agent.
Discovery
Needs a connector into a system of record
Finding AI nobody registered means reading SSO logs, browser telemetry or a SaaS admin API. Telemetry only ever shows what already reports to us, which is the opposite problem.
Entitlements
Needs a scopes read per harness
Whether an agent can reach more than it needs is a comparison between what it was granted and what it has used. We hold the second half; the first has to be pulled from each vendor.
Benefit
Needs telling us what the work was worth
Hours saved and deals closed are not observable from a token counter. The cost side above is measured; the return has to come from you, per use case.