Acme Corp has adopted 112 AI workflows across 14 active team members, delivering an estimated £186,000 annual value — equivalent to 2.7 FTE of additional capacity. The team adoption rate is 76%.
Engineering leads value creation at £34.5h/week saved, followed by Marketing (£28.0h) and Operations (£22.5h). ChatGPT remains the most widely adopted tool at 79% team coverage, with Claude and Cursor growing rapidly. 82% of implementations have documented human oversight levels.
Three high-priority recommendations are identified: accelerating piloting-stage workflows in Sales, improving evidence quality in Finance, and expanding Cursor adoption across the Engineering team.
112
Workflows Adopted
+19 vs prior
127h
Hours Saved / Week
+18.5h vs prior
£186k
Est. Annual Value
+£24k vs prior
148
Total Tracked
+32 vs prior
| Member | Dept | Adopted | Hrs/Wk | Est. Annual Value | Score |
| 1Sarah Johnson |
Marketing | 18 | 14.5h |
£27,840 |
88 |
| 2Marcus Taylor |
Engineering | 22 | 11.0h |
£21,120 |
82 |
| 3Aisha Patel |
Operations | 14 | 9.5h |
£18,240 |
76 |
| 4Raj Kumar |
Sales | 11 | 8.0h |
£15,360 |
71 |
| 5Laura Chen |
Design | 10 | 7.5h |
£14,400 |
64 |
| 6David Osei |
Engineering | 13 | 7.0h |
£13,440 |
62 |
| 7Priya Singh |
Marketing | 9 | 6.5h |
£12,480 |
58 |
| 8Tom Nakamura |
Finance | 7 | 5.5h |
£10,560 |
51 |
12 of 14 members have logged their AI stack.
Engineering
22 adopted · 4 members
£66,240/yr
81% evidenced
Marketing
18 adopted · 3 members
£53,760/yr
78% evidenced
Operations
16 adopted · 2 members
£43,200/yr
72% evidenced
Sales
11 adopted · 2 members
£32,640/yr
58% evidenced
Design
10 adopted · 1 member
£27,360/yr
60% evidenced
Finance
7 adopted · 2 members
£20,160/yr
43% evidenced
AI workflows in use that have high potential for full automation — freeing up further team capacity.
By Department
| Department | Distribution | H / M / L |
|---|
| Engineering |
|
5 / 11 / 6 |
| Marketing |
|
8 / 7 / 3 |
| Operations |
|
6 / 8 / 2 |
| Sales |
|
5 / 4 / 2 |
Based on 92 of 112 adopted implementations with automation data. 82% documentation coverage.
Deterministic gaps identified from the data. These are not speculative — they reflect measurable exposure.
⚠
Evidence Gap — Finance (43% evidenced)
Finance is the largest unevidenced value concentration at £11,520 of unvalidated savings. Adding outcome evidence here would significantly strengthen the business case when presenting to leadership.
◎
Concentration Risk — Sarah Johnson (11.4% of team hours)
A single individual accounts for 11.4% of total reported weekly hours saved (14.5h/wk). If this person leaves or changes role, reported team value drops materially.
⏸
Stalled Adoption — 9 workflows in piloting
9 implementations are stuck in piloting and not delivering confirmed value. Unblocking these represents an estimated £17,280 of potential annual uplift — primarily concentrated in the Sales team.
✓
HITL Coverage — 82% documented
Strong human oversight documentation across the team. The remaining 18% of implementations without oversight classification should be reviewed before the next board cycle.
1
Run a structured pilot unblocking session with Sales. 9 stalled workflows represent £17k of recoverable annual value. Assign a dedicated AI champion in the team and set a 6-week target for pilot-to-adopted conversion.
2
Prioritise evidence collection in Finance. At 43% evidenced, Finance is the weakest department for business case credibility. Introduce a simple outcome logging step into existing Finance workflows — even a monthly time-saving confirmation would move the needle significantly.
3
Expand Cursor adoption across the full Engineering team. Currently 6 of 4 Engineering members use it. Given the 11.0h/wk productivity demonstrated by Marcus Taylor, rolling this out org-wide could add an estimated £15k+ in annual value.
4
Reduce concentration risk by cross-training AI knowledge. Sarah Johnson's 14.5h/wk saved represents 11.4% of team output. A structured knowledge-sharing session would both reduce key-person dependency and lift the baseline for lower-performing members.
AI-generated recommendations based on team data
Methodology: Time savings based on reported usage frequency (daily = 240×/year, weekly = 48×/year, etc.).
Annual value uses each member's actual salary from their profile where available; falls back to £45,000.
FTE equivalent = annual hours ÷ 1,920 working hours/year.
"Evidenced" = validated or measured confidence level.
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