Partner Basecamp · Insights · London & San Francisco

Programme NPS +37 · 4 cohorts · 141 D2 respondents

4 v2 cohorts · 207 D1 respondents · Historical baseline NPS: +35

Programme NPS · all cohorts

+37

P 52% · Pa 32% · D 16%
n=141 D2 respondents

Cohorts

4 with full v2 data

Respondents

207

207 D1 · 141 D2

Cohort summary

Cohort	NPS	P%	Pa%	D%	D2 n	D1 n	Match	Conf Δ
C6 London May 18-19, 2026	+43	57.1%	28.6%	14.3%	21	39	9520%	+0.24
C7 San Francisco May 19-20, 2026	+31	52.5%	25.4%	22.0%	59	65	8310%	+0.09
C8 San Francisco May 20-21, 2026	+43	54.1%	35.1%	10.8%	37	48	9190%	+0.32
C9 London May 20-21, 2026	+38	45.8%	45.8%	8.3%	24	55	8750%	+0.22

Conf Δ = D2 apply-AI minus D1 baseline (v2 cohorts only). Match = D1/D2 linked respondents.

London · San Francisco · programme snapshot

London

+40

P 51% · Pa 38% · D 11%
n=45 D2 · 2 cohorts

C6 London May 18-19, 2026, C9 London May 20-21, 2026

San Francisco

+35

P 53% · Pa 29% · D 18%
n=96 D2 · 2 cohorts

C7 San Francisco May 19-20, 2026, C8 San Francisco May 20-21, 2026

Same content and programme design. City split reflects internal team and audience differences.

Audience composition · 207 D1 respondents pooled

Pooled across all cohorts. NPS by org aggregated from v2 cohorts only (n ≥ 4 suppressed).

Function

Engineering

35%

Architecture

25%

Project and engagement mana…

25%

Business leadership

16%

Seniority

Manager or Senior Manager

29%

Senior practitioner

26%

Practitioner

22%

Director, Senior Director, …

17%

Partner, Managing Director,…

AI Proficiency

Applying in practice

45%

Delivering independently

22%

Operating at the frontier

16%

Learning and exploring

16%

Organisation distribution · pooled

Organisation	n	%
Accenture	43	20.8%
Deloitte	41	19.8%
PwC	23	11.1%
Infosys	13	6.3%
Cognizant	9	4.3%
Reply	8	3.9%
Capgemini	7	3.4%
valantic	6	2.9%
Infomotion	5	2.4%
SFEIR	4	1.9%
Ascendion	4	1.9%
Sia	4	1.9%
Theodo	4	1.9%
Lovelytics	3	1.4%
Version 1	3	1.4%
Netlight	3	1.4%
Diverger	2	1.0%
NTT Data	2	1.0%
Bounteous	2	1.0%
Fractal	2	1.0%

NPS by organisation · aggregate (v2 cohorts, n ≥ 4)

Organisation	NPS	n
Accenture	+71	n=17
Deloitte	+54	n=30
Reply	+50	n=6
Infosys	+20	n=5
Cognizant	-14	n=7
PwC	-31	n=16

Day 1 calibration · confidence, depth, pace and relevance

Pooled across v2 cohorts. Confidence on 1–5 scale. Depth/pace as % of persona respondents.

D1 Confidence by Persona · mean /5

Developer
4.27/5 · n=49

85%

Architect
3.98/5 · n=54

80%

Transformation Lead
3.97/5 · n=104

79%

Bar = % of max scale (5). Pooled across v2 cohorts.

D1 Confidence by AI Proficiency · mean /5

Applying in practice
3.81/5 · n=93

76%

Delivering independently
4.43/5 · n=46

89%

Operating at the frontier
4.65/5 · n=34

93%

Learning and exploring
3.56/5 · n=34

71%

Bar = % of max scale (5). Pooled across v2 cohorts.

Content Depth by Persona · pooled

Persona	Too basic	About right	Too advanced
Architect	19% n=10	78% n=42	4% n=2
Developer	22% n=11	76% n=37	2% n=1
Transformation Lead	12% n=13	75% n=78	12% n=13

Session Pace by Persona · pooled

Persona	Too slow	Well paced	Too fast
Architect	6% n=3	87% n=47	7% n=4
Developer	4% n=2	92% n=45	4% n=2
Transformation Lead	6% n=6	82% n=85	12% n=13

Content Relevance by Persona · mean /5 · programme mean 4.08/5

Architect
4.19/5 · n=54

84%

Transformation Lead
4.06/5 · n=104

81%

Developer
4.00/5 · n=49

80%

Relevance = Day 1 session relevance rating (1–5). Pooled across v2 cohorts.

Outcomes · NPS by persona, proficiency and open text

Aggregate NPS breakouts from v2 cohorts (n ≥ 4 suppressed). 115 NPS open-text responses available.Synthesis not generated

NPS by Persona · aggregate (v2 cohorts, n ≥ 4)

Persona	NPS	n
Architect	+52	n=29
Unknown	+50	n=10
Transformation Lead	+34	n=65
Developer	+33	n=30

NPS by AI Proficiency · pooled (v2 cohorts, n ≥ 4)

Proficiency	NPS	P%	Pa%	D%	n
Applying in practice	+22	42.4%	37.3%	20.3%	n=59
Delivering independently	+59	66.7%	25.9%	7.4%	n=27
Operating at the frontier	+55	65.0%	25.0%	10.0%	n=20
Learning and exploring	+35	52.9%	29.4%	17.6%	n=17

Open text · NPS reasons · 115 responses

Synthesis not generatedRun with --synthesis to generate thematic analysis via Claude Haiku. 115 responses available.

Confidence · D1→D2 deltas across all three dimensions

Apply-AI · Design-AI · Commercial confidence. D1 baseline → D2 delta. v2 cohorts only.

D1 → D2 Confidence by Dimension · per cohort

Cohort	D1 build	D2 apply-AI	D2 design-AI	D2 commercial
C6 London May 18-19, 2026	4.00	4.24 +0.24	4.05 +0.05	4.19 +0.19
C7 San Francisco May 19-20, 2026	4.08	4.17 +0.09	4.29 +0.21	4.29 +0.21
C8 San Francisco May 20-21, 2026	4.00	4.32 +0.32	4.14 +0.14	4.19 +0.19
C9 London May 20-21, 2026	3.95	4.17 +0.22	4.38 +0.43	4.17 +0.22

D2 value shown above; delta (D2 – D1) shown below in smaller text. Scale 1–5.

Confidence Delta by Persona · pooled

Architect
n=29

+0.45

Transformation Lead
n=65

+0.14

Developer
n=30

-0.07

D1→D2 apply-AI mean delta. Positive = gained confidence. Scale 1–5.

Confidence Delta by NPS Segment · pooled

Promoter
n=66

+0.17

Passive
n=39

+0.23

Detractor
n=16

+0.07

Promoters gain more confidence than detractors — or vice versa?

3 systematic patterns · 1 flag · 5 suggested queries

Systematic patterns = same direction vs programme NPS across ≥2 cohorts. Suggested queries pre-written for ask.py.

Organisation patterns · consistent across ≥2 cohorts

Pattern	Direction	Avg Δ	Cohorts	Seen in
PwC scored below programme NPS in all 2 cohorts analysed (avg -60 pts) Likely reflects audience fit, not delivery variance.	↓ Below programme	-60 pts	2	C7 San Francisco May 19-20, 2026, C8 San Francisco May 20-21, 2026
Accenture scored above programme NPS in all 2 cohorts analysed (avg +36 pts) Likely reflects audience fit, not delivery variance.	↑ Above programme	+36 pts	2	C7 San Francisco May 19-20, 2026, C9 London May 20-21, 2026
Deloitte scored above programme NPS in all 2 cohorts analysed (avg +18 pts) Likely reflects audience fit, not delivery variance.	↑ Above programme	+18 pts	2	C7 San Francisco May 19-20, 2026, C8 San Francisco May 20-21, 2026

Outlier flags

● High passive rateHigh passive rate in C9 London May 20-21, 2026 (46%). Passives are the most likely source of churn — explore what would convert them to promoters.

Suggested ask.py queries

Run these commands from the Partner Basecamp/ folder to investigate the patterns above. Add --save when you have a conclusion to commit to the insights cache.

PwC — org pattern

PwC scored below programme NPS in all 2 cohorts analysed (avg -60 pts)

python3 _scripts/ask.py --question "What explains PwC's persistent below-programme NPS score across 2 cohorts, and what would change it?" --cache cohort-findings/insights-data-c6-c9.json

Accenture — org pattern

Accenture scored above programme NPS in all 2 cohorts analysed (avg +36 pts)

python3 _scripts/ask.py --question "What explains Accenture's persistent above-programme NPS score across 2 cohorts, and what would change it?" --cache cohort-findings/insights-data-c6-c9.json

Deloitte — org pattern

Deloitte scored above programme NPS in all 2 cohorts analysed (avg +18 pts)

python3 _scripts/ask.py --question "What explains Deloitte's persistent above-programme NPS score across 2 cohorts, and what would change it?" --cache cohort-findings/insights-data-c6-c9.json

High passives: C9 London May 20-21, 2026

Passive rate: 46%

python3 _scripts/ask.py --question "What is driving the high passive rate in C9 London May 20-21, 2026 — what do the verbatims say and what would convert them to promoters?" --cache cohort-findings/insights-data-c6-c9.json

Promoter vs passive — what separates them

NPS verbatim analysis — highest signal for passive conversion

python3 _scripts/ask.py --question "What do promoters and passives say about the programme, and what specifically separates them?" --cache cohort-findings/insights-data-c6-c9.json

Investigation log · no conclusions saved yet

Conclusions committed via ask.py --save, open signals from Tab 06 patterns, and pre-written queries.

No conclusions saved yetRun python3 _scripts/ask.py --question "..." --cache cohort-findings/insights-data-<slug>.json --save after reviewing an answer to commit it here. Conclusions build the investigation record over time.

Open signals

Systematic patterns

consistent across ≥2 cohorts

Outlier flags

anomalies flagged

Conclusions saved

from ask.py --save

Suggested queries

pre-written in Tab 06

Patterns not yet addressed by a saved conclusion:

PwC scored below programme NPS in all 2 cohorts analysed (avg -60 pts)
Accenture scored above programme NPS in all 2 cohorts analysed (avg +36 pts)
Deloitte scored above programme NPS in all 2 cohorts analysed (avg +18 pts)

Pre-written queries → Tab 06 · Patterns

Tab 06 contains pre-written ask.py commands for each pattern and flag detected. Run them from the Partner Basecamp/ folder, review the output, then re-run with --save to commit the conclusion here.

PwC — org pattern — PwC scored below programme NPS in all 2 cohorts analysed (avg -60 pts)
Accenture — org pattern — Accenture scored above programme NPS in all 2 cohorts analysed (avg +36 pts)
Deloitte — org pattern — Deloitte scored above programme NPS in all 2 cohorts analysed (avg +18 pts)
High passives: C9 London May 20-21, 2026 — Passive rate: 46%
Promoter vs passive — what separates them — NPS verbatim analysis — highest signal for passive conversion