Built from your team's logged time savings, valued at each member's hourly cost. Every logged item counts.
Firms up as more of it is backed by evidence or usage — see the confidence split below.
Frequency-weighted: hours per use × how often each task runs, summed across adopted and piloting items.
Based on 34 tasks across 11 active members — enough for a stable team average.
Members who've logged at least one AI implementation, out of paid seats. Spread across every department.
Even coverage across teams — no single function is carrying the number.
Share of your value that's backed by an evidence attachment or corroborated usage — not just self-reported.
62% backed, 38% self-reported. The higher this climbs, the harder your number is to dismiss.
Members who haven't logged recently. Their AI wins aren't being captured.
Nudge them to keep the number complete and representative.
How ready your team's data is to produce an AI-impact report you can take to leadership.
A number is only as strong as its data. This fills as your team:
When it's ready, your full ROI report unlocks.
Ranked by impact score — adopted implementations, hours saved, KPIs, and adoption rate.
| # | Member | Adopted? | Hrs/wk? | KPIs? | Score? |
|---|---|---|---|---|---|
| 1 | Sarah Johnson Marketing | 18 | 14.5h | 4 | 92 |
| 2 | Marcus Taylor Engineering | 15 | 11.0h | 3 | 81 |
| 3 | Aisha Patel Operations | 12 | 9.5h | 2 | 74 |
| 4 | Raj Kumar Sales | 9 | 8.0h | 2 | 61 |
| 5 | Laura Chen Design | 7 | 7.5h | 1 | 54 |
Share of the team using each tool
How much human involvement each logged AI task still needs — its automation potential.
Spot where your team could automate further.
56 high-automation candidates across your team
Based on 148 of 148 adopted implementations with automation data.
| Tool | Cost/seat/mo | Seats owned | Adoption |
|---|---|---|---|
| ChatGPT Plus | £20/seat/mo | 14 | 11/14 (79%) |
| Claude Pro | £20/seat/mo | 10 | 8/10 (80%) |
| Cursor | £16/seat/mo | 8 | 6/8 (75%) |
| GitHub Copilot | £8/seat/mo | 8 | 5/8 (63%) |
| Notion AI | £8/seat/mo | 14 | 4/14 (29%) |
Based on hours saved × hourly rate · members who've attributed at least one item
| Member | Tool | Hours/wk | Est. value/yr | Adoption |
|---|---|---|---|---|
Sarah Johnson Marketing |
ChatGPT | 8.5h | £22,100?
High confidence
From Sarah's logged tasks, backed by before/after evidence she attached. ChatGPT (browser) has no usage API, so evidence is what corroborates it here. Evidence-backed — stands on its own. |
Active |
Marcus Taylor Engineering |
Cursor | 11.0h | £28,600?
Directional
Self-reported from Marcus's logged tasks. Not yet backed by evidence or corroborated usage. Ask Marcus to attach evidence, or connect Cursor usage, to firm this up. |
Active |
Aisha Patel Operations |
Claude | 9.5h | £19,760?
High confidence
From Aisha's logged tasks, corroborated against her actual Claude API usage over the same period. Independently confirmed — this is the strongest kind. |
Active |
Raj Kumar Sales |
ChatGPT | 8.0h | £16,640 | Active |
Laura Chen Design |
Midjourney | 7.5h | £14,820 | Active |
| Member | In tokens | Out tokens | Requests | Models |
|---|---|---|---|---|
Marcus Taylor marcus@acmecorp.com |
892K | 381K | 621 | gpt-4o claude-3 |
Sarah Johnson sarah@acmecorp.com |
641K | 274K | 487 | gpt-4o |
Aisha Patel aisha@acmecorp.com |
418K | 179K | 312 | claude-3 |
Raj Kumar raj@acmecorp.com |
287K | 122K | 218 | gpt-4o |
Unattributed No email match · Review |
184K | 78K | 143 | gpt-4o-mini |